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      Gonadal Cycle-Dependent Expression of Genes Encoding Peptide-, Growth Factor-, and Orphan G-Protein-Coupled Receptors in Gonadotropin- Releasing Hormone Neurons of Mice

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          Abstract

          Rising serum estradiol triggers the surge release of gonadotropin-releasing hormone (GnRH) at late proestrus leading to ovulation. We hypothesized that proestrus evokes alterations in peptidergic signaling onto GnRH neurons inducing a differential expression of neuropeptide-, growth factor-, and orphan G-protein-coupled receptor (GPCR) genes. Thus, we analyzed the transcriptome of GnRH neurons collected from intact, proestrous and metestrous GnRH-green fluorescent protein (GnRH-GFP) transgenic mice using Affymetrix microarray technique. Proestrus resulted in a differential expression of genes coding for peptide/neuropeptide receptors including Adipor1, Prokr1, Ednrb, Rtn4r, Nmbr, Acvr2b, Sctr, Npr3, Nmur1, Mc3r, Cckbr, and Amhr2. In this gene cluster, Adipor1 mRNA expression was upregulated and the others were downregulated. Expression of growth factor receptors and their related proteins was also altered showing upregulation of Fgfr1, Igf1r, Grb2, Grb10, and Ngfrap1 and downregulation of Egfr and Tgfbr2 genes. Gpr107, an orphan GPCR, was upregulated during proestrus, while others were significantly downregulated ( Gpr1, Gpr87, Gpr18, Gpr62, Gpr125, Gpr183, Gpr4, and Gpr88). Further affected receptors included vomeronasal receptors ( Vmn1r172, Vmn2r-ps54, and Vmn1r148) and platelet-activating factor receptor ( Ptafr), all with marked downregulation. Patch-clamp recordings from mouse GnRH-GFP neurons carried out at metestrus confirmed that the differentially expressed IGF-1, secretin, and GPR107 receptors were operational, as their activation by specific ligands evoked an increase in the frequency of miniature postsynaptic currents (mPSCs). These findings show the contribution of certain novel peptides, growth factors, and ligands of orphan GPCRs to regulation of GnRH neurons and their preparation for the surge release.

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          STRING v10: protein–protein interaction networks, integrated over the tree of life

          The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database (http://string-db.org) aims to provide a critical assessment and integration of protein–protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein–protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks.
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            Orchestrating high-throughput genomic analysis with Bioconductor.

            Bioconductor is an open-source, open-development software project for the analysis and comprehension of high-throughput data in genomics and molecular biology. The project aims to enable interdisciplinary research, collaboration and rapid development of scientific software. Based on the statistical programming language R, Bioconductor comprises 934 interoperable packages contributed by a large, diverse community of scientists. Packages cover a range of bioinformatic and statistical applications. They undergo formal initial review and continuous automated testing. We present an overview for prospective users and contributors.
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              Exploration, normalization, and summaries of high density oligonucleotide array probe level data.

              In this paper we report exploratory analyses of high-density oligonucleotide array data from the Affymetrix GeneChip system with the objective of improving upon currently used measures of gene expression. Our analyses make use of three data sets: a small experimental study consisting of five MGU74A mouse GeneChip arrays, part of the data from an extensive spike-in study conducted by Gene Logic and Wyeth's Genetics Institute involving 95 HG-U95A human GeneChip arrays; and part of a dilution study conducted by Gene Logic involving 75 HG-U95A GeneChip arrays. We display some familiar features of the perfect match and mismatch probe (PM and MM) values of these data, and examine the variance-mean relationship with probe-level data from probes believed to be defective, and so delivering noise only. We explain why we need to normalize the arrays to one another using probe level intensities. We then examine the behavior of the PM and MM using spike-in data and assess three commonly used summary measures: Affymetrix's (i) average difference (AvDiff) and (ii) MAS 5.0 signal, and (iii) the Li and Wong multiplicative model-based expression index (MBEI). The exploratory data analyses of the probe level data motivate a new summary measure that is a robust multi-array average (RMA) of background-adjusted, normalized, and log-transformed PM values. We evaluate the four expression summary measures using the dilution study data, assessing their behavior in terms of bias, variance and (for MBEI and RMA) model fit. Finally, we evaluate the algorithms in terms of their ability to detect known levels of differential expression using the spike-in data. We conclude that there is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model which removes probe-specific affinities.
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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
                18 January 2021
                2020
                : 13
                : 594119
                Affiliations
                [1] 1Laboratory of Endocrine Neurobiology, Institute of Experimental Medicine , Budapest, Hungary
                [2] 2Faculty of Information Technology and Bionics, Roska Tamás Doctoral School of Sciences and Technology, Pázmány Péter Catholic University , Budapest, Hungary
                [3] 3Centre for Bioinformatics, University of Veterinary Medicine , Budapest, Hungary
                [4] 4Department of Neuroscience, Faculty of Information Technology and Bionics, Pázmány Péter Catholic University , Budapest, Hungary
                Author notes

                Edited by: Ildikó Rácz, University Hospital Bonn, Germany

                Reviewed by: Richard Anthony DeFazio, University of Michigan, United States; Takayoshi Ubuka, Waseda University, Japan

                *Correspondence: Zsolt Liposits liposits.zsolt@ 123456koki.mta.hu

                †These authors have contributed equally to this work

                ‡ORCID: Csaba Vastagh orcid.org/0000-0002-5008-0999

                Article
                10.3389/fnmol.2020.594119
                7863983
                dd1f05f7-1aa1-4167-85e8-e5c36367e118
                Copyright © 2021 Vastagh, Csillag, Solymosi, Farkas and Liposits.

                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 2020
                : 30 November 2020
                Page count
                Figures: 3, Tables: 4, Equations: 0, References: 134, Pages: 17, Words: 12504
                Funding
                Funded by: Nemzeti Kutatási Fejlesztési és Innovációs Hivatal 10.13039/501100011019
                Award ID: NKFI K-115984
                Award ID: NKFI K-128278
                Categories
                Neuroscience
                Original Research

                Neurosciences
                neuropeptides,gnrh,mouse,proestrus,transcriptome,growth factors,g-protein- coupled receptors,slice electrophysiology

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