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      Cancer SLC43A2 alters T cell methionine metabolism and histone methylation

      research-article
      1 , 2 , 16 , 1 , 2 , 16 , 3 , 3 , 1 , 2 , 4 , 1 , 2 , 3 , 3 , 5 , 6 , 1 , 2 , 1 , 2 , 1 , 2 , 1 , 2 , 1 , 2 , 1 , 2 , 1 , 2 , 1 , 2 , 7 , 7 , 4 , 8 , 8 , 9 , 10 , 11 , 12 , 12 , 6 , 6 , 1 , 2 , 3 , 13 , 14 , 15 , 1 , 2 , 8 , 14 , 15 , *
      Nature
      Methionine, SAM, H3K79me2, STAT5, SLC43A2, CD8+ T cell, cancer, immunotherapy, metabolism, epigenetic, anti-PD-L1, checkpoint blockade

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          Abstract

          Abnormal epigenetic patterns correlate with effector T cell malfunction in tumors 14 . However, their causal link is unknown. Here, we show that tumor cells disrupt methionine metabolism in CD8 + T cells, thereby lowering intracellular methionine levels and the methyl donor S-adenosylmethionine (SAM), resulting in loss of H3K79me2. Consequently, loss of H3K79me2 led to low STAT5 expression and impaired T cell immunity. Mechanistically, tumor cells avidly consumed and outcompeted T cells for methionine via high expression of SLC43A2, a methionine transporter. Genetic and biochemical inhibition of tumor SLC43A2 rescued T cell H3K79me2 levels, boosting spontaneous and checkpoint-induced tumor immunity. Moreover, we found that methionine supplementation improved expression of H3K79me2 and STAT5 in T cells, accompanied by increased T cell immunity in tumor bearing models and colon cancer patients. Clinically, tumor SLC43A2 negatively correlated with T cell histone methylation and functional gene signatures. Our work reveals a novel mechanistic connection between methionine metabolism, histone patterns, and T cell immunity in the tumor microenvironment. Thus, cancer methionine consumption is an unappreciated immune evasion mechanism, and targeting cancer methionine signaling may provide an immunotherapeutic approach.

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          Most cited references44

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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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            Is Open Access

            ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks

            Summary: We have developed ClueGO, an easy to use Cytoscape plug-in that strongly improves biological interpretation of large lists of genes. ClueGO integrates Gene Ontology (GO) terms as well as KEGG/BioCarta pathways and creates a functionally organized GO/pathway term network. It can analyze one or compare two lists of genes and comprehensively visualizes functionally grouped terms. A one-click update option allows ClueGO to automatically download the most recent GO/KEGG release at any time. ClueGO provides an intuitive representation of the analysis results and can be optionally used in conjunction with the GOlorize plug-in. Availability: http://www.ici.upmc.fr/cluego/cluegoDownload.shtml Contact: jerome.galon@crc.jussieu.fr Supplementary information: Supplementary data are available at Bioinformatics online.
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              The Human Genome Browser at UCSC

              As vertebrate genome sequences near completion and research refocuses to their analysis, the issue of effective genome annotation display becomes critical. A mature web tool for rapid and reliable display of any requested portion of the genome at any scale, together with several dozen aligned annotation tracks, is provided at http://genome.ucsc.edu. This browser displays assembly contigs and gaps, mRNA and expressed sequence tag alignments, multiple gene predictions, cross-species homologies, single nucleotide polymorphisms, sequence-tagged sites, radiation hybrid data, transposon repeats, and more as a stack of coregistered tracks. Text and sequence-based searches provide quick and precise access to any region of specific interest. Secondary links from individual features lead to sequence details and supplementary off-site databases. One-half of the annotation tracks are computed at the University of California, Santa Cruz from publicly available sequence data; collaborators worldwide provide the rest. Users can stably add their own custom tracks to the browser for educational or research purposes. The conceptual and technical framework of the browser, its underlying MYSQL database, and overall use are described. The web site currently serves over 50,000 pages per day to over 3000 different users.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                30 May 2020
                02 September 2020
                September 2020
                02 March 2021
                : 585
                : 7824
                : 277-282
                Affiliations
                [1 ]Department of Surgery, University of Michigan School of Medicine, Ann Arbor, MI 48109, USA
                [2 ]Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, University of Michigan School of Medicine, Ann Arbor, MI 48109, USA
                [3 ]Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
                [4 ]Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI 48019, USA
                [5 ]Department of Virology and Immunology, Maria Curie-Skłodowska University, Lublin 20-031, Poland
                [6 ]1 st Chair and Department of Oncological Gynecology and Gynecology, Medical University of Lublin, Lublin 20-081, Poland
                [7 ]Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI 48019, USA
                [8 ]Department of Pathology, University of Michigan, Ann Arbor, MI 48019, USA
                [9 ]Department of Urology, University of Michigan, Ann Arbor, MI 48019, USA
                [10 ]Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48019, USA
                [11 ]Howard Hughes Medical Institute, University of Michigan, Ann Arbor, MI 48019, USA
                [12 ]2 nd Department of General, Gastrointestinal Surgery and Surgical Oncology of the Alimentary Tract, Medical University of Lublin, Lublin 20-081, Poland
                [13 ]Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109 USA
                [14 ]Graduate Program in Immunology, University of Michigan School of Medicine, Ann Arbor, MI 48109, USA
                [15 ]Graduate Program in Cancer Biology, University of Michigan School of Medicine, Ann Arbor, MI 48109, USA
                [16 ]These authors contributed equally to this work.
                Author notes

                Author Contributions

                Y. B., W. L., and W. Z. proposed the research concept. Y. B. performed the majority of the experiments and explored the concept for SLC transporter. Y. B., W. L., and W. Z. designed the experiments. W.L. and J.C. performed some in vivo experiments with Dot1l −/− mice. D. M. K., P. S., Z. L., Z. C. N. and C. A. L. designed, performed, and analyzed the mass spectrometry experiments for metabolite tests and analysis. S. L., J. L., M. C. and A. C. assisted with the RNA-seq and single cell RNA-seq data analysis. H. X., P. L., L. V., W. S., and I. K. aided in mouse and human sample collection and FACS data analysis. S. W. and S. G performed mice genotyping and breeding. J. R. L. and K. M. assisted in clinical study design and ovarian cancer patient specimen collection. A.C., A. P., W. Z., G.W., I. W., and K. O. performed the clinical study on colorectal cancer patients. Y. B., W. L., and W. Z. wrote the manuscript.

                [* ]Correspondence: wzou@ 123456med.umich.edu (W.Z.)
                Article
                NIHMS1599392
                10.1038/s41586-020-2682-1
                7486248
                32879489
                f2b863a9-822d-47c5-8c2e-2206de9fc98a

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                methionine,sam,h3k79me2,stat5,slc43a2,cd8+ t cell,cancer,immunotherapy,metabolism,epigenetic,anti-pd-l1,checkpoint blockade

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