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Comprehensive analysis of DNA methylation for periodontitis

BACKGROUND: Periodontitis is an infectious disease, and a risk factor for peri-implantitis that could result in the implant loss. DNA methylation has an essential role in the etiology and pathogenesis of inflammatory disease. However, there is lack of study on methylation status of genes in periodon...

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Autores principales: Zhao, Zengbo, Wang, Huimin, Li, Xiaona, Hou, Jingya, Yang, Yuntian, Li, Hexiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9058047/
https://www.ncbi.nlm.nih.gov/pubmed/35491409
http://dx.doi.org/10.1186/s40729-022-00420-8
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author Zhao, Zengbo
Wang, Huimin
Li, Xiaona
Hou, Jingya
Yang, Yuntian
Li, Hexiang
author_facet Zhao, Zengbo
Wang, Huimin
Li, Xiaona
Hou, Jingya
Yang, Yuntian
Li, Hexiang
author_sort Zhao, Zengbo
collection PubMed
description BACKGROUND: Periodontitis is an infectious disease, and a risk factor for peri-implantitis that could result in the implant loss. DNA methylation has an essential role in the etiology and pathogenesis of inflammatory disease. However, there is lack of study on methylation status of genes in periodontitis. This study sought to explore the gene methylation profiling microarray in periodontitis. METHODS: Through searching in the Gene Expression Omnibus database, a gene methylation profiling data set GSE173081 was identified, which included 12 periodontitis samples and 12 normal samples, respectively. Thereafter, the data of GSE173081 was downloaded and analyzed to determined differentially methylated genes (DMGs), which then were used to perform Gene Ontology analysis and pathway enrichment analyses through online database. In addition, the DMGs were applied to construct the protein–protein interaction (PPI) network information, predict the hub genes in pathology of periodontitis. RESULTS: In total 668 DMGs were sorted and identified from the data set, which included 621 hypo-methylated genes and 47 hyper-methylated genes. Through the function and ontology analysis, these 668 genes are mainly classified into intracellular signaling pathway, cell components, cell–cell interaction, and cellular behaviors. The pathway analysis showed that the hypo-methylated genes were mostly enriched in the pathway of cGMP–PKG signaling pathway; RAF/MAP kinase; PI3K–Akt signaling pathway, while hyper-methylated genes were mostly enriched in the pathway of bacterial invasion of epithelial cells; sphingolipid signaling pathway and DCC mediated attractive signaling. The PPI network contained 630 nodes and 1790 interactions. Moreover, further analysis identified top 10 hub genes (APP; PAX6; LPAR1; WNT3A; BMP2; PI3KR2; GATA4; PLCB1; GATA6; CXCL12) as central nodes that are involved in the immune system and the inflammatory response. CONCLUSIONS: This study provides comprehensive information of methylation status of genes to the revelation of periodontitis pathogenesis that may contribute to future research on periodontitis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40729-022-00420-8.
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spelling pubmed-90580472022-05-07 Comprehensive analysis of DNA methylation for periodontitis Zhao, Zengbo Wang, Huimin Li, Xiaona Hou, Jingya Yang, Yuntian Li, Hexiang Int J Implant Dent Research BACKGROUND: Periodontitis is an infectious disease, and a risk factor for peri-implantitis that could result in the implant loss. DNA methylation has an essential role in the etiology and pathogenesis of inflammatory disease. However, there is lack of study on methylation status of genes in periodontitis. This study sought to explore the gene methylation profiling microarray in periodontitis. METHODS: Through searching in the Gene Expression Omnibus database, a gene methylation profiling data set GSE173081 was identified, which included 12 periodontitis samples and 12 normal samples, respectively. Thereafter, the data of GSE173081 was downloaded and analyzed to determined differentially methylated genes (DMGs), which then were used to perform Gene Ontology analysis and pathway enrichment analyses through online database. In addition, the DMGs were applied to construct the protein–protein interaction (PPI) network information, predict the hub genes in pathology of periodontitis. RESULTS: In total 668 DMGs were sorted and identified from the data set, which included 621 hypo-methylated genes and 47 hyper-methylated genes. Through the function and ontology analysis, these 668 genes are mainly classified into intracellular signaling pathway, cell components, cell–cell interaction, and cellular behaviors. The pathway analysis showed that the hypo-methylated genes were mostly enriched in the pathway of cGMP–PKG signaling pathway; RAF/MAP kinase; PI3K–Akt signaling pathway, while hyper-methylated genes were mostly enriched in the pathway of bacterial invasion of epithelial cells; sphingolipid signaling pathway and DCC mediated attractive signaling. The PPI network contained 630 nodes and 1790 interactions. Moreover, further analysis identified top 10 hub genes (APP; PAX6; LPAR1; WNT3A; BMP2; PI3KR2; GATA4; PLCB1; GATA6; CXCL12) as central nodes that are involved in the immune system and the inflammatory response. CONCLUSIONS: This study provides comprehensive information of methylation status of genes to the revelation of periodontitis pathogenesis that may contribute to future research on periodontitis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40729-022-00420-8. Springer Berlin Heidelberg 2022-05-02 /pmc/articles/PMC9058047/ /pubmed/35491409 http://dx.doi.org/10.1186/s40729-022-00420-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research
Zhao, Zengbo
Wang, Huimin
Li, Xiaona
Hou, Jingya
Yang, Yuntian
Li, Hexiang
Comprehensive analysis of DNA methylation for periodontitis
title Comprehensive analysis of DNA methylation for periodontitis
title_full Comprehensive analysis of DNA methylation for periodontitis
title_fullStr Comprehensive analysis of DNA methylation for periodontitis
title_full_unstemmed Comprehensive analysis of DNA methylation for periodontitis
title_short Comprehensive analysis of DNA methylation for periodontitis
title_sort comprehensive analysis of dna methylation for periodontitis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9058047/
https://www.ncbi.nlm.nih.gov/pubmed/35491409
http://dx.doi.org/10.1186/s40729-022-00420-8
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