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      A role for orphan nuclear receptor liver receptor homolog-1 (LRH-1, NR5A2) in primordial follicle activation

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          Abstract

          Liver receptor homolog-1 (NR5A2) is expressed specifically in granulosa cells of developing ovarian follicles where it regulates the late stages of follicle development and ovulation. To establish its effects earlier in the trajectory of follicular development, NR5A2 was depleted from granulosa cells of murine primordial and primary follicles. Follicle populations were enumerated in neonates at postnatal day 4 (PND4) coinciding with the end of the formation of the primordial follicle pool. The frequency of primordial follicles in PND4 conditional knockout (cKO) ovaries was greater and primary follicles were substantially fewer relative to control (CON) counterparts. Ten-day in vitro culture of PND4 ovaries recapitulated in vivo findings and indicated that CON mice developed primary follicles in the ovarian medulla to a greater extent than did cKO animals. Two subsets of primordial follicles were observed in wildtype ovaries: one that expressed NR5A2 and the second in which the transcript was absent. Neither expressed the mitotic marker. KI-67, indicating their developmental quiescence. RNA sequencing on PND4 demonstrated that loss of NR5A2 induced changes in 432 transcripts, including quiescence markers, inhibitors of follicle activation, and regulators of cellular migration and epithelial-to-mesenchymal transition. These experiments suggest that NR5A2 expression poises primordial follicles for entry into the developing pool.

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          Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

          DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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            Metascape provides a biologist-oriented resource for the analysis of systems-level datasets

            A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets. Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results. Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists. In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal. Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments. Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs. Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era.
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              Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists

              Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biological processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categorized into three major classes, according to their underlying enrichment algorithms. The comprehensive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by-tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.
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                Author and article information

                Contributors
                bruce.d.murphy@umontreal.ca
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                13 January 2021
                13 January 2021
                2021
                : 11
                : 1079
                Affiliations
                [1 ]GRID grid.14848.31, ISNI 0000 0001 2292 3357, Centre de recherche en reproduction et fertilité, , Université de Montréal, ; 3200 rue Sicotte, St-Hyacinthe, QC J2S 7C6 Canada
                [2 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Pediatric Surgical Research Laboratories, Simches Research Center, , Massachusetts General Hospital, ; 185 Cambridge St., Boston, MA 02114 USA
                [3 ]GRID grid.14709.3b, ISNI 0000 0004 1936 8649, Department of Animal Science, , McGill University, ; 21111 Lakeshore Rd., MS1085, Ste-Anne de Bellevue, QC H9X 3V9 Canada
                Article
                80178
                10.1038/s41598-020-80178-4
                7807074
                33441767
                83aeaa2a-61fc-4d96-b591-51138eebad6c
                © The Author(s) 2021

                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 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 July 2020
                : 17 December 2020
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                developmental biology,physiology,endocrinology
                Uncategorized
                developmental biology, physiology, endocrinology

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