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

      Hypoxia activates SREBP2 through Golgi disassembly in bone marrow‐derived monocytes for enhanced tumor growth

      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

          Bone marrow‐derived cells (BMDCs) infiltrate hypoxic tumors at a pre‐angiogenic state and differentiate into mature macrophages, thereby inducing pro‐tumorigenic immunity. A critical factor regulating this differentiation is activation of SREBP2—a well‐known transcription factor participating in tumorigenesis progression—through unknown cellular mechanisms. Here, we show that hypoxia‐induced Golgi disassembly and Golgi‐ER fusion in monocytic myeloid cells result in nuclear translocation and activation of SREBP2 in a SCAP‐independent manner. Notably, hypoxia‐induced SREBP2 activation was only observed in an immature lineage of bone marrow‐derived cells. Single‐cell RNA‐seq analysis revealed that SREBP2‐mediated cholesterol biosynthesis was upregulated in HSCs and monocytes but not in macrophages in the hypoxic bone marrow niche. Moreover, inhibition of cholesterol biosynthesis impaired tumor growth through suppression of pro‐tumorigenic immunity and angiogenesis. Thus, our findings indicate that Golgi‐ER fusion regulates SREBP2‐mediated metabolic alteration in lineage‐specific BMDCs under hypoxia for tumor progression.

          Abstract

          Low‐oxygen conditions induce lineage‐specific activation of SREBP2 via Golgi‐ER fusion, promoting lipid biosynthesis, myeloid cell infiltration, and tumorigenesis.

          Related collections

          Most cited references68

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

          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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

            GSVA: gene set variation analysis for microarray and RNA-Seq data

            Background Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Integrating single-cell transcriptomic data across different conditions, technologies, and species

              Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
                Bookmark

                Author and article information

                Contributors
                osawa@lsbm.org
                Journal
                EMBO J
                EMBO J
                10.1002/(ISSN)1460-2075
                EMBJ
                embojnl
                The EMBO Journal
                John Wiley and Sons Inc. (Hoboken )
                0261-4189
                1460-2075
                02 October 2023
                November 2023
                02 October 2023
                : 42
                : 22 ( doiID: 10.1002/embj.v42.22 )
                : e114032
                Affiliations
                [ 1 ] Division of Nutriomics and Oncology, RCAST The University of Tokyo Tokyo Japan
                [ 2 ] Department of Chemistry and Biotechnology, Graduate School of Engineering The University of Tokyo Tokyo Japan
                [ 3 ] Department of Systems Biology, Graduate School of Medicine Nagoya University Nagoya Japan
                [ 4 ] Division of Molecular and Vascular Biology, IRDA Kumamoto University Kumamoto Japan
                [ 5 ] Division of Metabolic Medicine, RCAST The University of Tokyo Tokyo Japan
                [ 6 ] Department of Inflammology, RCAST The University of Tokyo Tokyo Japan
                [ 7 ] Department of Cell Biology Japanese Foundation for Cancer Research Tokyo Japan
                [ 8 ] Department of Bioengineering, Graduate School of Engineering The University of Tokyo Tokyo Japan
                [ 9 ] Institute of Physiology and Medicine Jobu University Takasaki Japan
                [ 10 ] Division of Molecular Physiology and Metabolism, Graduate School of Medicine Tohoku University Sendai Japan
                [ 11 ] Department of Signal Transduction, RIMD Osaka University Osaka Japan
                [ 12 ] Department of Integrative Vascular Biology, Faculty of Medical Sciences University of Fukui Fukui Japan
                [ 13 ]Present address: Department of Computational and Systems Biology, Medical Research Institute Tokyo Medical and Dental University Tokyo Japan
                Author notes
                [*] [* ] Corresponding author. Tel: +81 (0)3 5452 5025; E‐mail: osawa@ 123456lsbm.org

                [ † ]

                These authors contributed equally to this work

                Author information
                https://orcid.org/0000-0002-5278-4500
                https://orcid.org/0000-0002-3108-0440
                https://orcid.org/0000-0003-4043-1035
                https://orcid.org/0000-0002-9479-6665
                Article
                EMBJ2023114032
                10.15252/embj.2023114032
                10646561
                37781951
                3a0d2b38-94ff-4e54-8f91-d63c14845a91
                © 2023 The Authors. Published under the terms of the CC BY NC ND 4.0 license.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 22 August 2023
                : 15 March 2023
                : 28 August 2023
                Page count
                Figures: 14, Tables: 1, Pages: 34, Words: 18111
                Funding
                Funded by: Cannon Foundation (The Cannon Foundation) , doi 10.13039/100007132;
                Funded by: Japan Agency for Medical Research and Development (AMED) , doi 10.13039/100009619;
                Funded by: Koyanagi Foundation
                Funded by: Kurata Memorial Hitachi Science and Technology Foundation , doi 10.13039/100010278;
                Funded by: MEXT | Japan Society for the Promotion of Science (JSPS) , doi 10.13039/501100001691;
                Award ID: 22H04922
                Award ID: 21K19399
                Award ID: 20H04834
                Award ID: 19H03496
                Award ID: 19K22553
                Award ID: 23K18234
                Funded by: Naito Science and Engineering Foundation (公益財団法人 内藤科学技術振興財団)
                Funded by: SGH Foundation (SGH財団)
                Funded by: Sumitomo Foundation (住友財団) , doi 10.13039/100008608;
                Funded by: Takeda Foundation (般財団法人 武田計測先端知財団)
                Funded by: The Shimadzu Science Foundation
                Funded by: Uehara Memorial Foundation (UMF) , doi 10.13039/100008732;
                Categories
                Article
                Articles
                Custom metadata
                2.0
                15 November 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.3.4 mode:remove_FC converted:15.11.2023

                Molecular biology
                cholesterol biosynthesis,golgi‐er fusion,hypoxia,myeloid differentiation,srebp2,cancer,metabolism

                Comments

                Comment on this article