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      Prevention of age-associated neuronal hyperexcitability with improved learning and attention upon knockout or antagonism of LPAR2

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          Abstract

          Recent studies suggest that synaptic lysophosphatidic acids (LPAs) augment glutamate-dependent cortical excitability and sensory information processing in mice and humans via presynaptic LPAR2 activation. Here, we studied the consequences of LPAR2 deletion or antagonism on various aspects of cognition using a set of behavioral and electrophysiological analyses. Hippocampal neuronal network activity was decreased in middle-aged LPAR2 −/− mice, whereas hippocampal long-term potentiation (LTP) was increased suggesting cognitive advantages of LPAR2 −/− mice. In line with the lower excitability, RNAseq studies revealed reduced transcription of neuronal activity markers in the dentate gyrus of the hippocampus in naïve LPAR2 −/− mice, including ARC, FOS, FOSB, NR4A, NPAS4 and EGR2. LPAR2 −/− mice behaved similarly to wild-type controls in maze tests of spatial or social learning and memory but showed faster and accurate responses in a 5-choice serial reaction touchscreen task requiring high attention and fast spatial discrimination. In IntelliCage learning experiments, LPAR2 −/− were less active during daytime but normally active at night, and showed higher accuracy and attention to LED cues during active times. Overall, they maintained equal or superior licking success with fewer trials. Pharmacological block of the LPAR2 receptor recapitulated the LPAR2 −/− phenotype, which was characterized by economic corner usage, stronger daytime resting behavior and higher proportions of correct trials. We conclude that LPAR2 stabilizes neuronal network excitability upon aging and allows for more efficient use of resting periods, better memory consolidation and better  performance in tasks requiring high selective attention. Therapeutic LPAR2 antagonism may alleviate aging-associated cognitive dysfunctions.

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          The online version of this article (10.1007/s00018-020-03553-4) contains supplementary material, which is available to authorized users.

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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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            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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              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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                Author and article information

                Contributors
                itegeder@hotmail.com , tegeder@em.uni-frankfurt.de
                Journal
                Cell Mol Life Sci
                Cell Mol Life Sci
                Cellular and Molecular Life Sciences
                Springer International Publishing (Cham )
                1420-682X
                1420-9071
                28 May 2020
                28 May 2020
                2021
                : 78
                : 3
                : 1029-1050
                Affiliations
                [1 ]GRID grid.7839.5, ISNI 0000 0004 1936 9721, Institute of Clinical Pharmacology, , Goethe-University Frankfurt, Faculty of Medicine, ; Frankfurt, Germany
                [2 ]GRID grid.410607.4, Institute for Microscopic Anatomy and Neurobiology, , University Medical Center of the Johannes Gutenberg University, ; Mainz, Germany
                [3 ]Institute for Human Genetics, University Medical Center, Johannes Gutenberg University, Mainz, Germany
                [4 ]GRID grid.410607.4, Department of Neurology, , University Medical Center of the Johannes Gutenberg University, ; Mainz, Germany
                [5 ]GRID grid.4488.0, ISNI 0000 0001 2111 7257, Institute of Anatomy, , Medical Faculty Carl Gustav Carus Technische Universität, School of Medicine, ; Dresden, Germany
                [6 ]GRID grid.5949.1, ISNI 0000 0001 2172 9288, Institute for Translational Neuroscience, , Westfälische Wilhelms Universität, ; Münster, Germany
                [7 ]GRID grid.6190.e, ISNI 0000 0000 8580 3777, Center of Anatomy, , University of Cologne, ; Cologne, Germany
                Author information
                http://orcid.org/0000-0001-7524-8025
                Article
                3553
                10.1007/s00018-020-03553-4
                7897625
                32468095
                19146e05-d928-4d91-95c6-38fd00005550
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 13 November 2019
                : 16 April 2020
                : 13 May 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft;
                Award ID: CRC1039 A03
                Award ID: CRC1080 A03
                Award ID: CRC1080 B05
                Award ID: SFB1193 A05
                Award Recipient :
                Categories
                Original Article
                Custom metadata
                © Springer Nature Switzerland AG 2021

                Molecular biology
                lysophosphatidic acids,cognition,touchscreen,intellicage,long-term potentiation,hippocampal excitability

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