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      Pontin arginine methylation by CARM1 is crucial for epigenetic regulation of autophagy

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          Abstract

          Autophagy is a catabolic process through which cytoplasmic components are degraded and recycled in response to various stresses including starvation. Recently, transcriptional and epigenetic regulations of autophagy have emerged as essential mechanisms for maintaining homeostasis. Here, we identify that coactivator-associated arginine methyltransferase 1 (CARM1) methylates Pontin chromatin-remodeling factor under glucose starvation, and methylated Pontin binds Forkhead Box O 3a (FOXO3a). Genome-wide analyses and biochemical studies reveal that methylated Pontin functions as a platform for recruiting Tip60 histone acetyltransferase with increased H4 acetylation and subsequent activation of autophagy genes regulated by FOXO3a. Surprisingly, CARM1-Pontin-FOXO3a signaling axis can work in the distal regions and activate autophagy genes through enhancer activation. Together, our findings provide a signaling axis of CARM1-Pontin-FOXO3a and further expand the role of CARM1 in nuclear regulation of autophagy.

          Abstract

          Epigenetic regulations of autophagy have emerged as mechanisms for maintaining cellular homeostasis. Here the authors reveal that the CARM1-Pontin-FOXO3a signaling axis can activate autophagy related genes through enhancer activation.

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          Most cited references49

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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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            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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              Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

              DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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                Author and article information

                Contributors
                sbaek@snu.ac.kr
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                8 December 2020
                8 December 2020
                2020
                : 11
                : 6297
                Affiliations
                [1 ]GRID grid.31501.36, ISNI 0000 0004 0470 5905, Creative Research Initiatives Center for Epigenetic Code and Diseases, Department of Biological Sciences, , Seoul National University, ; Seoul, 08826 South Korea
                [2 ]GRID grid.411947.e, ISNI 0000 0004 0470 4224, Department of Anatomy, College of Medicine, , The Catholic University of Korea, ; Seoul, 06591 South Korea
                [3 ]GRID grid.264381.a, ISNI 0000 0001 2181 989X, Department of Molecular Cell Biology, School of Medicine, , Sungkyunkwan University, ; Suwon, 16419 South Korea
                [4 ]GRID grid.5254.6, ISNI 0000 0001 0674 042X, Biotech Research and Innovation Centre (BRIC), , University of Copenhagen, ; 2200 Copenhagen N, Denmark
                [5 ]GRID grid.5254.6, ISNI 0000 0001 0674 042X, Novo Nordisk Foundation Center for Stem Cell Biology, DanStem, Faculty of Health and Medical Sciences, , University of Copenhagen, ; 2200 Copenhagen N, Denmark
                [6 ]GRID grid.47840.3f, ISNI 0000 0001 2181 7878, Present Address: Department of Molecular and Cell Biology, , University of California Berkeley, ; Berkeley, CA 94720 USA
                Author information
                http://orcid.org/0000-0001-5642-4441
                http://orcid.org/0000-0002-3454-6605
                http://orcid.org/0000-0002-9273-4129
                http://orcid.org/0000-0002-3773-2737
                http://orcid.org/0000-0002-0767-0766
                http://orcid.org/0000-0003-2515-8894
                Article
                20080
                10.1038/s41467-020-20080-9
                7722926
                33293536
                36277dad-ae4c-48ec-94f6-8f54ed7b4887
                © The Author(s) 2020

                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
                : 24 April 2020
                : 10 November 2020
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                © The Author(s) 2020

                Uncategorized
                biochemistry,cell biology,molecular biology
                Uncategorized
                biochemistry, cell biology, molecular biology

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