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      The cryptic gonadotropin-releasing hormone neuronal system of human basal ganglia

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          Abstract

          Human reproduction is controlled by ~2000 hypothalamic gonadotropin-releasing hormone (GnRH) neurons. Here, we report the discovery and characterization of additional ~150,000–200,000 GnRH-synthesizing cells in the human basal ganglia and basal forebrain. Nearly all extrahypothalamic GnRH neurons expressed the cholinergic marker enzyme choline acetyltransferase. Similarly, hypothalamic GnRH neurons were also cholinergic both in embryonic and adult human brains. Whole-transcriptome analysis of cholinergic interneurons and medium spiny projection neurons laser-microdissected from the human putamen showed selective expression of GNRH1 and GNRHR1 autoreceptors in the cholinergic cell population and uncovered the detailed transcriptome profile and molecular connectome of these two cell types. Higher-order non-reproductive functions regulated by GnRH under physiological conditions in the human basal ganglia and basal forebrain require clarification. The role and changes of GnRH/GnRHR1 signaling in neurodegenerative disorders affecting cholinergic neurocircuitries, including Parkinson’s and Alzheimer’s diseases, need to be explored.

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          Most cited references56

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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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              featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

              Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                15 June 2021
                2021
                : 10
                : e67714
                Affiliations
                [1 ]Laboratory of Reproductive Neurobiology, Institute of Experimental Medicine BudapestHungary
                [2 ]Laboratory of Endocrine Neurobiology, Institute of Experimental Medicine BudapestHungary
                [3 ]1st Department of Pathology and Experimental Cancer Research, Semmelweis University BudapestHungary
                [4 ]Centre for Bioinformatics, University of Veterinary Medicine BudapestHungary
                [5 ]Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen DebrecenHungary
                [6 ]Department of Inorganic and Analytical Chemistry, Budapest University of Technology and Economics BudapestHungary
                [7 ]Department of Gene Technology and Developmental Biology, Institute of Experimental Medicine BudapestHungary
                [8 ]Amolyt Pharma NewtonFrance
                [9 ]Univ. Lille, Inserm, CHU Lille, Laboratory of Development and Plasticity of the Neuroendocrine Brain, Lille Neuroscience & Cognition LilleFrance
                University of Maryland School of Medicine United States
                Harvard University United States
                University of Maryland School of Medicine United States
                Author information
                https://orcid.org/0000-0002-0159-4408
                http://orcid.org/0000-0003-1783-2041
                http://orcid.org/0000-0002-3075-1441
                https://orcid.org/0000-0001-6927-0015
                Article
                67714
                10.7554/eLife.67714
                8245125
                34128468
                3a482549-454e-464f-9945-0a1fa73ea200
                © 2021, Skrapits et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 19 February 2021
                : 14 June 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100010024, Hungarian Science Foundation;
                Award ID: K128317
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100010024, Hungarian Science Foundation;
                Award ID: PD134837
                Award Recipient :
                Funded by: Hungarian Brain Research Program;
                Award ID: 2017-1.2.1-NKP- 2017-00002
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001677, Institut National de la Santé et de la Recherche Médicale;
                Award ID: U1172
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001665, Agence Nationale de la Recherche;
                Award ID: ANR-19-CE16-0021-02
                Award Recipient :
                Funded by: Inserm Cross-Cutting Scientific Program;
                Award ID: HuDeCa
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100011019, NRDI Office;
                Award ID: TKP2020 IES
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100011019, NRDI Office;
                Award ID: BME-IE-BIO
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Cell Biology
                Neuroscience
                Custom metadata
                Neuroanatomical studies reveal presence and cholinergic phenotype of 150,000-200,000 extrahypothalamic gonadotropin-releasing hormone neurons in human basal ganglia and basal forebrain, and RNA-sequencing detects gonadotropin-releasing hormone receptor-1 expression in cholinergic interneurons but not in spiny projection neurons of the human putamen.

                Life sciences
                cholinergic interneurons,gnrh,human transcriptomics,neuropeptides,rna-sequencing,striatum,human

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