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      m6A RNA methylation regulators could contribute to the occurrence of chronic obstructive pulmonary disease

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          Abstract

          N6‐methyladenosine (m6A) RNA methylation, the most prevalent internal chemical modification of mRNA, has been reported to participate in the progression of various tumours via the dynamic regulation of m6A RNA methylation regulators. However, the role of m6A RNA methylation regulators in chronic obstructive pulmonary disease (COPD) has never been reported. This study aimed to determine the expression and potential functions of m6A RNA methylation regulators in COPD. Four gene expression data sets were acquired from Gene Expression Omnibus. Gene ontology function, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses, weighted correlation network analysis and protein‐protein interaction network analysis were performed. The correlation analyses of m6A RNA methylation regulators and key COPD genes were also performed. We found that the mRNA expressions of IGF2BP3, FTO, METTL3 and YTHDC2, which have the significant associations with some key genes enriched in the signalling pathway and biological processes that promote the development progression of COPD, are highly correlated with the occurrence of COPD. In conclusion, six central m6A RNA methylation regulators could contribute to the occurrence of COPD. This study provides important evidence for further examination of the role of m6A RNA methylation in COPD.

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          Most cited references48

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          WGCNA: an R package for weighted correlation network analysis

          Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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            Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq.

            An extensive repertoire of modifications is known to underlie the versatile coding, structural and catalytic functions of RNA, but it remains largely uncharted territory. Although biochemical studies indicate that N(6)-methyladenosine (m(6)A) is the most prevalent internal modification in messenger RNA, an in-depth study of its distribution and functions has been impeded by a lack of robust analytical methods. Here we present the human and mouse m(6)A modification landscape in a transcriptome-wide manner, using a novel approach, m(6)A-seq, based on antibody-mediated capture and massively parallel sequencing. We identify over 12,000 m(6)A sites characterized by a typical consensus in the transcripts of more than 7,000 human genes. Sites preferentially appear in two distinct landmarks--around stop codons and within long internal exons--and are highly conserved between human and mouse. Although most sites are well preserved across normal and cancerous tissues and in response to various stimuli, a subset of stimulus-dependent, dynamically modulated sites is identified. Silencing the m(6)A methyltransferase significantly affects gene expression and alternative splicing patterns, resulting in modulation of the p53 (also known as TP53) signalling pathway and apoptosis. Our findings therefore suggest that RNA decoration by m(6)A has a fundamental role in regulation of gene expression.
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              RNA N6-methyladenosine methyltransferase METTL3 promotes liver cancer progression through YTHDF2 dependent post-transcriptional silencing of SOCS2.

              Epigenetic alterations immensely contributed to human carcinogenesis. Conventional epigenetic studies predominantly focused on DNA methylation, histone modifications, and chromatin remodeling. Recently, diverse and reversible chemical modifications on RNAs emerge as a new layer of epigenetic regulation. N6-methyladenosine (m6A) is the most abundant chemical modification on eukaryotic mRNA and is important to the regulation of mRNA stability, splicing, and translation. Using transcriptome sequencing, we discovered that METTL3 (methyltransferase like 3), a major RNA N6-adenosine methyltransferase, was significantly up-regulated in human hepatocellular carcinoma (HCC) and multiple solid tumors. Clinically, overexpression of METTL3 was associated with poor prognosis of HCC patients. Functionally, we proved that knockdown of METTL3 drastically reduced HCC cell proliferation, migration and colony formation in vitro. Knockout of METTL3 remarkably suppressed HCC tumorigenicity and lung metastasis in vivo. On the other hand, using CRISPR/dCas9-VP64 activation system, we demonstrated that overexpression of METTL3 significantly promoted HCC growth both in vitro and in vivo. Through transcriptome sequencing, m6A-Seq and m6A MeRIP qRT-PCR, we identified SOCS2 (suppressor of cytokine signaling 2) as a target of METTL3-mediated m6A modification. Knockdown of METTL3 substantially abolished SOCS2 mRNA m6A modification and augmented SOCS2 mRNA expression. We also showed that m6A-mediated SOCS2 mRNA degradation relied on m6A "reader" protein YTHDF2 dependent pathway. In conclusion, we demonstrated that METTL3 was frequently up-regulated in human HCC and contributed to HCC progression. METTL3 repressed SOCS2 expression in HCC via the m6A-YTHDF2 dependent mechanism. Thus, our findings suggested a new dimension of epigenetic alteration in liver carcinogenesis. This article is protected by copyright. All rights reserved.
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                Author and article information

                Contributors
                huanggenetics@163.com
                2000012@tongji.edu.cn
                Journal
                J Cell Mol Med
                J Cell Mol Med
                10.1111/(ISSN)1582-4934
                JCMM
                Journal of Cellular and Molecular Medicine
                John Wiley and Sons Inc. (Hoboken )
                1582-1838
                1582-4934
                22 September 2020
                November 2020
                : 24
                : 21 ( doiID: 10.1111/jcmm.v24.21 )
                : 12706-12715
                Affiliations
                [ 1 ] Translational Research Institute of Brain and Brain‐Like Intelligence Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine Shanghai China
                [ 2 ] Department of Rehabilitation Medicine Shanghai Fourth People's Hospital Affiliated to Tongji University School of Medicine Shanghai China
                [ 3 ] Department of Medical Oncology The Third Affiliated Hospital of Kunming Medical University (Tumor Hospital of Yunnan Province) Kunming China
                Author notes
                [*] [* ] Correspondence

                Xinwei Huang, Translational Research Institute of Brain and Brain‐Like Intelligence, Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine, No. 1878, North Sichuan Road, Shanghai 200081, China.

                Email: huanggenetics@ 123456163.com

                Hong Zhang, Department of Rehabilitation Medicine, Shanghai Fourth People's Hospital Affiliated to Tongji University School of Medicine, No. 1878, North Sichuan Road, Shanghai 200081, China.

                Email: 2000012@ 123456tongji.edu.cn

                Author information
                https://orcid.org/0000-0002-8568-6261
                Article
                JCMM15848
                10.1111/jcmm.15848
                7686997
                32961012
                bb6264dc-7554-4feb-b398-49e99c33880e
                © 2020 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 26 May 2020
                : 04 August 2020
                : 19 August 2020
                Page count
                Figures: 3, Tables: 1, Pages: 10, Words: 5793
                Funding
                Funded by: Education Office of Sichuan province
                Award ID: 17ZA0130
                Funded by: Sichuan Provincial Health and Family Planning Commission Universal Application Project
                Award ID: 18PJ514
                Categories
                Original Article
                Original Articles
                Custom metadata
                2.0
                November 2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.9.4 mode:remove_FC converted:25.11.2020

                Molecular medicine
                bioinformatics,chronic obstructive pulmonary disease,igf2bp3,m6a rna methylation regulators,mettl3

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