3
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      The polarity protein Par3 coordinates positively self-renewal and negatively invasiveness in glioblastoma

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Glioblastoma (GBM) is a brain malignancy characterized by invasiveness to the surrounding brain tissue and by stem-like cells, which propagate the tumor and may also regulate invasiveness. During brain development, polarity proteins, such as Par3, regulate asymmetric cell division of neuro-glial progenitors and neurite motility. We, therefore, studied the role of the Par3 protein (encoded by PARD3) in GBM. GBM patient transcriptomic data and patient-derived culture analysis indicated diverse levels of expression of PARD3 across and independent from subtypes. Multiplex immunolocalization in GBM tumors identified Par3 protein enrichment in SOX2-, CD133-, and NESTIN-positive (stem-like) cells. Analysis of GBM cultures of the three subtypes (proneural, classical, mesenchymal), revealed decreased gliomasphere forming capacity and enhanced invasiveness upon silencing Par3. GBM cultures with suppressed Par3 showed low expression of stemness ( SOX2 and NESTIN) but higher expression of differentiation ( GFAP) genes. Moreover, Par3 silencing reduced the expression of a set of genes encoding mitochondrial enzymes that generate ATP. Accordingly, silencing Par3 reduced ATP production and concomitantly increased reactive oxygen species. The latter was required for the enhanced migration observed upon silencing of Par3 as anti-oxidants blocked the enhanced migration. These findings support the notion that Par3 exerts homeostatic redox control, which could limit the tumor cell-derived pool of oxygen radicals, and thereby the tumorigenicity of GBM.

          Related collections

          Most cited references63

          • Record: found
          • Abstract: found
          • Article: not found

          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.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

              Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.
                Bookmark

                Author and article information

                Contributors
                aris.moustakas@imbim.uu.se
                Journal
                Cell Death Dis
                Cell Death Dis
                Cell Death & Disease
                Nature Publishing Group UK (London )
                2041-4889
                12 October 2021
                12 October 2021
                October 2021
                : 12
                : 10
                : 932
                Affiliations
                [1 ]GRID grid.8993.b, ISNI 0000 0004 1936 9457, Department of Medical Biochemistry and Microbiology, , Science for Life Laboratory, Box 582, Biomedical Center, Uppsala University, ; SE-75123 Uppsala, Sweden
                [2 ]GRID grid.8993.b, ISNI 0000 0004 1936 9457, Department of Immunology, , Genetics and Pathology, Rudbeck Laboratory, Science for Life Laboratory, Uppsala University, ; SE-75185 Uppsala, Sweden
                [3 ]GRID grid.5386.8, ISNI 000000041936877X, Brain and Mind Research Institute, Weill Cornell Medicine, ; New York, NY 10021 USA
                [4 ]GRID grid.10419.3d, ISNI 0000000089452978, Department of Cell and Chemical Biology, , Oncode Institute, Leiden University Medical Center, ; Leiden, The Netherlands
                Author information
                http://orcid.org/0000-0002-8786-8763
                http://orcid.org/0000-0002-9508-896X
                http://orcid.org/0000-0001-9131-3827
                Article
                4220
                10.1038/s41419-021-04220-7
                8511086
                34642295
                a442606c-0d56-4825-9532-10c91eeb4a3d
                © 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
                : 16 August 2020
                : 15 September 2021
                : 28 September 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100002794, Cancerfonden (Swedish Cancer Society);
                Award ID: CAN 2012/1186
                Award ID: CAN 2016/445
                Award ID: CAN 2015/438
                Award ID: CAN2018/469
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100006285, Magnus Bergvalls Stiftelse (Magnus Bergvall Foundation);
                Award ID: 2019-03444
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100004359, Vetenskapsrådet (Swedish Research Council);
                Award ID: 2015-02757
                Award ID: K2013-66X-14936-10-5
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100010663, EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council);
                Award ID: 787472
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100009729, Ludwig Institute for Cancer Research (Ludwig Cancer Research);
                Award ID: Uppsala Branch
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Cell biology
                cell polarity,cns cancer
                Cell biology
                cell polarity, cns cancer

                Comments

                Comment on this article