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      CG6015 controls spermatogonia transit-amplifying divisions by epidermal growth factor receptor signaling in Drosophila testes

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          Abstract

          Spermatogonia transit-amplifying (TA) divisions are crucial for the differentiation of germline stem cell daughters. However, the underlying mechanism is largely unknown. In the present study, we demonstrated that CG6015 was essential for spermatogonia TA-divisions and elongated spermatozoon development in Drosophila melanogaster. Spermatogonia deficient in CG6015 inhibited germline differentiation leading to the accumulation of undifferentiated cell populations. Transcriptome profiling using RNA sequencing indicated that CG6015 was involved in spermatogenesis, spermatid differentiation, and metabolic processes. Gene Set Enrichment Analysis (GSEA) revealed the relationship between CG6015 and the epidermal growth factor receptor (EGFR) signaling pathway. Unexpectedly, we discovered that phosphorylated extracellular regulated kinase (dpERK) signals were activated in germline stem cell (GSC)-like cells after reduction of CG6015 in spermatogonia. Moreover, Downstream of raf1 (Dsor1), a key downstream target of EGFR, mimicked the phenotype of CG6015, and germline dpERK signals were activated in spermatogonia of Dsor1 RNAi testes. Together, these findings revealed a potential regulatory mechanism of CG6015 via EGFR signaling during spermatogonia TA-divisions in Drosophila testes.

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          Cytoscape: a software environment for integrated models of biomolecular interaction networks.

          Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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            clusterProfiler: an R package for comparing biological themes among gene clusters.

            Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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              STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

              Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
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                Author and article information

                Contributors
                yujun9117@126.com
                mansnoopy@163.com
                sunfei@ntu.edu.cn
                Journal
                Cell Death Dis
                Cell Death Dis
                Cell Death & Disease
                Nature Publishing Group UK (London )
                2041-4889
                14 May 2021
                14 May 2021
                May 2021
                : 12
                : 5
                : 491
                Affiliations
                [1 ]GRID grid.260483.b, ISNI 0000 0000 9530 8833, Institute of Reproductive Medicine, School of Medicine, , Nantong University, ; Nantong, China
                [2 ]GRID grid.440785.a, ISNI 0000 0001 0743 511X, Department of Gynecology, , the Affiliated Hospital of Jiangsu University, Jiangsu University, ; Zhenjiang, China
                [3 ]GRID grid.89957.3a, ISNI 0000 0000 9255 8984, State Key Laboratory of Reproductive Medicine, Center for Reproduction and Genetics, Suzhou Municipal Hospital, , The Affiliated Suzhou Hospital of Nanjing Medical University, Gusu School, Nanjing Medical University, ; Suzhou, China
                Author information
                http://orcid.org/0000-0003-3109-4102
                http://orcid.org/0000-0002-1496-0753
                http://orcid.org/0000-0002-0870-8375
                Article
                3783
                10.1038/s41419-021-03783-9
                8121936
                33990549
                a51c67ba-6ba6-4c48-b76d-2724eca0aa30
                © 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 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
                : 15 January 2021
                : 29 April 2021
                : 30 April 2021
                Funding
                Funded by: Large Instruments Open Foundation of Nantong University (KFJN2157)
                Funded by: The National Natural Science Foundation of China (81901533)
                Funded by: The National Natural Science Foundation of China (81901532),Natural Science Foundation of Jiangsu Province (BK20190188),Suzhou Key Laboratory of Male Reproduction Research (SZS201718)
                Funded by: The National Key Research and Development Program of China (No. 2018YFC1003500),The National Natural Science Foundation of China (81671510)
                Categories
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                Custom metadata
                © The Author(s) 2021

                Cell biology
                cell proliferation,differentiation
                Cell biology
                cell proliferation, differentiation

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