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      Estrogen-related receptor gamma regulates mitochondrial and synaptic genes and modulates vulnerability to synucleinopathy

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          Abstract

          Many studies implicate mitochondrial dysfunction as a key contributor to cell loss in Parkinson disease (PD). Previous analyses of dopaminergic (DAergic) neurons from patients with Lewy-body pathology revealed a deficiency in nuclear-encoded genes for mitochondrial respiration, many of which are targets for the transcription factor estrogen-related receptor gamma ( Esrrg/ERRγ). We demonstrate that deletion of ERRγ from DAergic neurons in adult mice was sufficient to cause a levodopa-responsive PD-like phenotype with reductions in mitochondrial gene expression and number, that partial deficiency of ERRγ hastens synuclein-mediated toxicity, and that ERRγ overexpression reduces inclusion load and delays synuclein-mediated cell loss. While ERRγ deletion did not fully recapitulate the transcriptional alterations observed in postmortem tissue, it caused reductions in genes involved in synaptic and mitochondrial function and autophagy. Altogether, these experiments suggest that ERRγ-deficient mice could provide a model for understanding the regulation of transcription in DAergic neurons and that amplifying ERRγ -mediated transcriptional programs should be considered as a strategy to promote DAergic maintenance in PD.

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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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              HTSeq—a Python framework to work with high-throughput sequencing data

              Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de
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                Author and article information

                Contributors
                rcowell@southernresearch.org
                Journal
                NPJ Parkinsons Dis
                NPJ Parkinsons Dis
                NPJ Parkinson's Disease
                Nature Publishing Group UK (London )
                2373-8057
                18 August 2022
                18 August 2022
                2022
                : 8
                : 106
                Affiliations
                [1 ]GRID grid.454225.0, ISNI 0000 0004 0376 8349, Neuroscience Department, Drug Discovery Division, Southern Research, ; Birmingham, AL 35205 USA
                [2 ]GRID grid.265892.2, ISNI 0000000106344187, Department of Cell, Developmental and Integrative Biology, , University of Alabama at Birmingham, ; Birmingham, AL 35294 USA
                [3 ]GRID grid.265892.2, ISNI 0000000106344187, Department of Psychiatry and Behavioral Neurobiology, , University of Alabama at Birmingham, ; Birmingham, AL 35294 USA
                [4 ]NeuroInitiative, LLC, Jacksonville, FL 32207 USA
                [5 ]GRID grid.265892.2, ISNI 0000000106344187, Department of Pediatrics, Infectious Disease, Neuroscience Vector and Virus Core, , University of Alabama at Birmingham, ; Birmingham, AL 35223 USA
                [6 ]GRID grid.251017.0, ISNI 0000 0004 0406 2057, Department of Neurodegenerative Science, Van Andel Institute, ; Grand Rapids, MI 49503 USA
                [7 ]GRID grid.5333.6, ISNI 0000000121839049, Swiss Federal Institute of Technology Lausanne, ; Lausanne, Switzerland
                [8 ]GRID grid.265892.2, ISNI 0000000106344187, Department of Urology, , University of Alabama at Birmingham, ; Birmingham, AL 35294 USA
                [9 ]GRID grid.265892.2, ISNI 0000000106344187, Department of Genetics, , University of Alabama at Birmingham, ; Birmingham, AL 35294 USA
                [10 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Department of Physiology, , Johns Hopkins University School of Medicine, ; Baltimore, MD 21205 USA
                Author information
                http://orcid.org/0000-0003-2170-8342
                http://orcid.org/0000-0003-3572-7819
                http://orcid.org/0000-0002-6194-5855
                http://orcid.org/0000-0002-1510-6831
                http://orcid.org/0000-0002-0981-169X
                http://orcid.org/0000-0001-5814-9412
                Article
                369
                10.1038/s41531-022-00369-w
                9388660
                35982091
                635ee74e-60d5-442f-b1e4-ac4e9e9f0f8d
                © The Author(s) 2022

                Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 5 October 2021
                : 12 July 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000065, U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS);
                Award ID: 5 R01 NS101958
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000864, Michael J. Fox Foundation for Parkinson's Research (Michael J. Fox Foundation);
                Award ID: Target Validation Award #10758
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100005162, Meyer Foundation;
                Funded by: FundRef https://doi.org/10.13039/100008333, University of Alabama at Birmingham (UAB);
                Award ID: 5 T32NS095775
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000049, U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging);
                Award ID: 5 F99AG068428
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                parkinson's disease,cellular neuroscience
                parkinson's disease, cellular neuroscience

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