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      METTL3 acetylation impedes cancer metastasis via fine-tuning its nuclear and cytosolic functions

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          Abstract

          The methyltransferase like 3 (METTL3) has been generally recognized as a nuclear protein bearing oncogenic properties. We find predominantly cytoplasmic METTL3 expression inversely correlates with node metastasis in human cancers. It remains unclear if nuclear METTL3 is functionally distinct from cytosolic METTL3 in driving tumorigenesis and, if any, how tumor cells sense oncogenic insults to coordinate METTL3 functions within these intracellular compartments. Here, we report an acetylation-dependent regulation of METTL3 localization that impacts on metastatic dissemination. We identify an IL-6-dependent positive feedback axis to facilitate nuclear METTL3 functions, eliciting breast cancer metastasis. IL-6, whose mRNA transcript is subjected to METTL3-mediated m 6A modification, promotes METTL3 deacetylation and nuclear translocation, thereby inducing global m 6A abundance. This deacetylation-mediated nuclear shift of METTL3 can be counterbalanced by SIRT1 inhibition, a process that is further enforced by aspirin treatment, leading to ablated lung metastasis via impaired m 6A methylation. Intriguingly, acetylation-mimetic METTL3 mutant reconstitution results in enhanced translation and compromised metastatic potential. Our study identifies an acetylation-dependent regulatory mechanism determining the subcellular localization of METTL3, which may provide mechanistic clues for developing therapeutic strategies to combat breast cancer metastasis.

          Abstract

          METTL3 catalyzes mRNA m 6A deposition. The authors identify an acetylation-mediated regulation of METTL3 subcellular localization and compartment-specific functions, a process that is fine-tuned by anti-inflammatory and pro-inflammatory signals, which ultimately determine breast cancer metastasis.

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          Most cited references72

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          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.
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            Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

            Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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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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                Author and article information

                Contributors
                hyou@xmu.edu.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                26 October 2022
                26 October 2022
                2022
                : 13
                : 6350
                Affiliations
                [1 ]GRID grid.12955.3a, ISNI 0000 0001 2264 7233, State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, , Xiamen University, ; 361102 Xiamen, China
                [2 ]GRID grid.459328.1, ISNI 0000 0004 1758 9149, Wuxi Cancer Institute, , Affiliated Hospital of Jiangnan University, ; 214062 Wuxi, China
                [3 ]GRID grid.24696.3f, ISNI 0000 0004 0369 153X, Department of Biochemistry and Molecular Biology, , Capital Medical University, ; 100069 Beijing, China
                [4 ]GRID grid.12955.3a, ISNI 0000 0001 2264 7233, State Key Laboratory of Cellular Stress Biology, School of Pharmaceutical Sciences, , Xiamen University, ; 361102 Xiamen, China
                [5 ]GRID grid.9227.e, ISNI 0000000119573309, Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, , Chinese Academy of Sciences, ; 650223 Kunming, China
                [6 ]GRID grid.12955.3a, ISNI 0000 0001 2264 7233, Fujian Provincial Key Laboratory of Reproductive Health Research, School of Medicine, , Xiamen University, ; 361102 Xiamen, China
                [7 ]GRID grid.24516.34, ISNI 0000000123704535, Tongji University Cancer Center, Shanghai Tenth People’s Hospital of Tongji University, School of Medicine, Tongji University, ; 200092 Shanghai, China
                [8 ]GRID grid.73113.37, ISNI 0000 0004 0369 1660, Department of Neurosurgery, Shanghai Changhai Hospital, , Naval Medical University, ; 200433 Shanghai, China
                [9 ]GRID grid.412509.b, ISNI 0000 0004 1808 3414, The Biomedical Translational Research Institute, School of Life Sciences, , Shandong University of Technology, ; 255049 Zibo, China
                [10 ]GRID grid.16821.3c, ISNI 0000 0004 0368 8293, Translational Medicine Center, Shanghai General Hospital, , Shanghai Jiao Tong University School of Medicine, ; 201620 Shanghai, China
                [11 ]GRID grid.233520.5, ISNI 0000 0004 1761 4404, The State Key Laboratory of Cancer Biology, Department of Biochemistry and Molecular Biology, , Fourth Military Medical University, ; 710032 Xi’an, China
                [12 ]GRID grid.233520.5, ISNI 0000 0004 1761 4404, Department of Pathology, Xijing Hospital and School of Basic Medicine, , Fourth Military Medical University, ; 710032 Xi’an, China
                Author information
                http://orcid.org/0000-0002-8235-8924
                http://orcid.org/0000-0003-3434-4162
                http://orcid.org/0000-0001-6398-3516
                http://orcid.org/0000-0002-9053-5249
                http://orcid.org/0000-0003-3833-1035
                http://orcid.org/0000-0002-1213-946X
                http://orcid.org/0000-0001-9129-9092
                http://orcid.org/0000-0003-1481-5465
                Article
                34209
                10.1038/s41467-022-34209-5
                9605963
                36289222
                e829c918-044c-4215-a5e5-e47bd02dc878
                © The Author(s) 2022

                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
                : 28 March 2022
                : 14 October 2022
                Funding
                Funded by: the National Natural Science Foundation of China
                Categories
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                Custom metadata
                © The Author(s) 2022

                Uncategorized
                cell signalling,cancer
                Uncategorized
                cell signalling, cancer

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