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      The RNA-binding protein SERBP1 functions as a novel oncogenic factor in glioblastoma by bridging cancer metabolism and epigenetic regulation

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      1 , 2 , 1 , 2 , 1 , 3 , 4 , 5 , 1 , 6 , 4 , 3 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 6 , 6 , 1 , 7 , 1 , 8 , 2 , 6 , 9 , 10 , 6 , 9 , 11 , 4 , , 5 , , 1 , 2 ,
      Genome Biology
      BioMed Central
      GBM, SERBP1, RNA-binding protein, Cancer metabolism, One carbon cycle, Epigenetic regulation

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          Abstract

          Background

          RNA-binding proteins (RBPs) function as master regulators of gene expression. Alterations in RBP expression and function are often observed in cancer and influence critical pathways implicated in tumor initiation and growth. Identification and characterization of oncogenic RBPs and their regulatory networks provide new opportunities for targeted therapy.

          Results

          We identify the RNA-binding protein SERBP1 as a novel regulator of glioblastoma (GBM) development. High SERBP1 expression is prevalent in GBMs and correlates with poor patient survival and poor response to chemo- and radiotherapy. SERBP1 knockdown causes delay in tumor growth and impacts cancer-relevant phenotypes in GBM and glioma stem cell lines. RNAcompete identifies a GC-rich region as SERBP1-binding motif; subsequent genomic and functional analyses establish SERBP1 regulation role in metabolic routes preferentially used by cancer cells. An important consequence of these functions is SERBP1 impact on methionine production. SERBP1 knockdown decreases methionine levels causing a subsequent reduction in histone methylation as shown for H3K27me3 and upregulation of genes associated with neurogenesis, neuronal differentiation, and function. Further analysis demonstrates that several of these genes are downregulated in GBM, potentially through epigenetic silencing as indicated by the presence of H3K27me3 sites.

          Conclusions

          SERBP1 is the first example of an RNA-binding protein functioning as a central regulator of cancer metabolism and indirect modulator of epigenetic regulation in GBM. By bridging these two processes, SERBP1 enhances glioma stem cell phenotypes and contributes to GBM poorly differentiated state.

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          Most cited references48

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          Phosphoglycerate dehydrogenase diverts glycolytic flux and contributes to oncogenesis.

          Most tumors exhibit increased glucose metabolism to lactate, however, the extent to which glucose-derived metabolic fluxes are used for alternative processes is poorly understood. Using a metabolomics approach with isotope labeling, we found that in some cancer cells a relatively large amount of glycolytic carbon is diverted into serine and glycine metabolism through phosphoglycerate dehydrogenase (PHGDH). An analysis of human cancers showed that PHGDH is recurrently amplified in a genomic region of focal copy number gain most commonly found in melanoma. Decreasing PHGDH expression impaired proliferation in amplified cell lines. Increased expression was also associated with breast cancer subtypes, and ectopic expression of PHGDH in mammary epithelial cells disrupted acinar morphogenesis and induced other phenotypic alterations that may predispose cells to transformation. Our findings show that the diversion of glycolytic flux into a specific alternate pathway can be selected during tumor development and may contribute to the pathogenesis of human cancer.
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            GlioVis data portal for visualization and analysis of brain tumor expression datasets

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              PANTHER version 7: improved phylogenetic trees, orthologs and collaboration with the Gene Ontology Consortium

              Protein Analysis THrough Evolutionary Relationships (PANTHER) is a comprehensive software system for inferring the functions of genes based on their evolutionary relationships. Phylogenetic trees of gene families form the basis for PANTHER and these trees are annotated with ontology terms describing the evolution of gene function from ancestral to modern day genes. One of the main applications of PANTHER is in accurate prediction of the functions of uncharacterized genes, based on their evolutionary relationships to genes with functions known from experiment. The PANTHER website, freely available at http://www.pantherdb.org, also includes software tools for analyzing genomic data relative to known and inferred gene functions. Since 2007, there have been several new developments to PANTHER: (i) improved phylogenetic trees, explicitly representing speciation and gene duplication events, (ii) identification of gene orthologs, including least diverged orthologs (best one-to-one pairs), (iii) coverage of more genomes (48 genomes, up to 87% of genes in each genome; see http://www.pantherdb.org/panther/summaryStats.jsp), (iv) improved support for alternative database identifiers for genes, proteins and microarray probes and (v) adoption of the SBGN standard for display of biological pathways. In addition, PANTHER trees are being annotated with gene function as part of the Gene Ontology Reference Genome project, resulting in an increasing number of curated functional annotations.
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                Author and article information

                Contributors
                kosti@uthscsa.edu
                biopaty@hotmail.com
                bluehattie@hotmail.com
                gguardia@mochsl.org.br
                jennifer.j.chiou@gmail.com
                ych14789850262@sina.com
                debray1@gmail.com
                fmeliso@mochsl.org.br
                yiyimig@163.com
                takaviol@gmail.com
                ljqmlx@hotmail.com
                florbeer@berkeley.edu
                fanny.georgi@uzh.ch
                flosbachm@gmail.com
                sarah.klinnert@usz.ch
                annejenseit@web.de
                lei@uthscsa.edu
                jcromerosandoval@gmail.com
                k.ha@mail.utoronto.ca
                hongz.zheng@gmail.com
                renupandeyji@gmail.com
                gruslova@uthscsa.edu
                guptaY@uthscsa.edu
                brennerA@uthscsa.edu
                kokovaye@uthscsa.edu
                t.hughes@utoronto.ca
                quaid.morris@gmail.com
                pgalante@mochsl.org.br
                tiziani@austin.utexas.edu
                penalva@uthscsa.edu
                Journal
                Genome Biol
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1474-7596
                1474-760X
                6 August 2020
                6 August 2020
                2020
                : 21
                : 195
                Affiliations
                [1 ]Children’s Cancer Research Institute, UT Health San Antonio, San Antonio, TX 78229 USA
                [2 ]Department of Cell Systems and Anatomy, UT Health San Antonio, San Antonio, TX 78229 USA
                [3 ]Shanghai Changzheng Hospital, Second Military Medical University, Shanghai, China
                [4 ]GRID grid.413471.4, ISNI 0000 0000 9080 8521, Centro de Oncologia Molecular, Hospital Sírio-Libanês, ; São Paulo, São Paulo 01309-060 Brazil
                [5 ]GRID grid.89336.37, ISNI 0000 0004 1936 9924, Department of Nutritional Sciences, , Dell Pediatric Research Institute, Dell Medical School, The University of Texas at Austin, ; Austin, TX 78712 USA
                [6 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Donnelly Centre, , University of Toronto, ; Toronto, ON M5S 3E1 Canada
                [7 ]Department of Medicine, UT Health San Antonio, San Antonio, TX 78229 USA
                [8 ]Mays Cancer Center, UT Health San Antonio, San Antonio, TX 78229 USA
                [9 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Department of Molecular Genetics, , University of Toronto, ; Toronto, ON M5S 1A8 Canada
                [10 ]GRID grid.440050.5, ISNI 0000 0004 0408 2525, Canadian Institute for Advanced Research, MaRS Centre, ; West Tower, 661 University Avenue, Suite 505, Toronto, ON M5G 1M1 Canada
                [11 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Department of Computer Science, , University of Toronto, ; Toronto, ON M5T 3A1 Canada
                Author information
                http://orcid.org/0000-0003-4491-6769
                Article
                2115
                10.1186/s13059-020-02115-y
                7412812
                32762776
                a56872f3-b722-41b3-840a-aa4555d81b99
                © The Author(s) 2020

                Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 23 March 2020
                : 22 July 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: 2R01 HG006015
                Award ID: 2R01 HG006015
                Award Recipient :
                Funded by: CPRIT
                Award ID: RP140105
                Funded by: CNPq
                Categories
                Research
                Custom metadata
                © The Author(s) 2020

                Genetics
                gbm,serbp1,rna-binding protein,cancer metabolism,one carbon cycle,epigenetic regulation
                Genetics
                gbm, serbp1, rna-binding protein, cancer metabolism, one carbon cycle, epigenetic regulation

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