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

      Inhibition of DUSP18 impairs cholesterol biosynthesis and promotes anti-tumor immunity in colorectal cancer

      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

          Tumor cells reprogram their metabolism to produce specialized metabolites that both fuel their own growth and license tumor immune evasion. However, the relationships between these functions remain poorly understood. Here, we report CRISPR screens in a mouse model of colo-rectal cancer (CRC) that implicates the dual specificity phosphatase 18 (DUSP18) in the establishment of tumor-directed immune evasion. Dusp18 inhibition reduces CRC growth rates, which correlate with high levels of CD8 + T cell activation. Mechanistically, DUSP18 dephosphorylates and stabilizes the USF1 bHLH-ZIP transcription factor. In turn, USF1 induces the SREBF2 gene, which allows cells to accumulate the cholesterol biosynthesis intermediate lanosterol and release it into the tumor microenvironment (TME). There, lanosterol uptake by CD8 + T cells suppresses the mevalonate pathway and reduces KRAS protein prenylation and function, which in turn inhibits their activation and establishes a molecular basis for tumor cell immune escape. Finally, the combination of an anti-PD-1 antibody and Lumacaftor, an FDA-approved small molecule inhibitor of DUSP18, inhibits CRC growth in mice and synergistically enhances anti-tumor immunity. Collectively, our findings support the idea that a combination of immune checkpoint and metabolic blockade represents a rationally-designed, mechanistically-based and potential therapy for CRC.

          Abstract

          Dual-specificity phosphatases regulate several processes associated with carcinogenesis. Here the authors show that inhibition of the dual-specificity phosphatase DUSP18 improves the activity of tumor-infiltrating CD8 T cells, enhancing response to immune checkpoint blockade in preclinical models of colorectal cancer.

          Related collections

          Most cited references73

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

          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
            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: found
              Is Open Access

              Integrated analysis of multimodal single-cell data

              Summary The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.
                Bookmark

                Author and article information

                Contributors
                liy7@whu.edu.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                12 July 2024
                12 July 2024
                2024
                : 15
                : 5851
                Affiliations
                [1 ]GRID grid.49470.3e, ISNI 0000 0001 2331 6153, Department of Colorectal and Anal Surgery, Zhongnan Hospital of Wuhan University, Hubei Key Laboratory of Cell Homeostasis, College of Life Sciences, , Wuhan University, ; Wuhan, Hubei 430072 China
                [2 ]GRID grid.49470.3e, ISNI 0000 0001 2331 6153, Frontier Science Center for Immunology and Metabolism, Medical Research Institute, TaiKang Center for Life and Medical Sciences, , Wuhan University, ; Wuhan, Hubei 430071 China
                [3 ]Division of Hematology/Oncology, Children’s Hospital of Pittsburgh of UPMC, ( https://ror.org/03763ep67) Pittsburgh, PA 15224 USA
                [4 ]GRID grid.416864.9, ISNI 0000 0004 0435 1502, Department of Microbiology and Molecular Genetics of UPMC, ; Pittsburgh, PA 15224 USA
                [5 ]GRID grid.416864.9, ISNI 0000 0004 0435 1502, The Pittsburgh Liver Research Center, , The Hillman Cancer Institute of UPMC, ; Pittsburgh, PA 15224 USA
                Author information
                http://orcid.org/0000-0001-6035-0756
                http://orcid.org/0000-0003-0133-1277
                Article
                50138
                10.1038/s41467-024-50138-x
                11239938
                38992029
                899cc864-1a80-46c4-93f6-415b2338967b
                © The Author(s) 2024

                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
                : 5 February 2024
                : 2 July 2024
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                cancer microenvironment,cancer metabolism,tumour immunology
                Uncategorized
                cancer microenvironment, cancer metabolism, tumour immunology

                Comments

                Comment on this article