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Identification of key DNA methylation changes on fasting plasma glucose: a genome-wide DNA methylation analysis in Chinese monozygotic twins

BACKGROUND: Elevated fasting plasma glucose (FPG) levels can increase morbidity and mortality even when it is below the diagnostic threshold of type 2 diabetes mellitus (T2DM). We conducted a genome-wide DNA methylation analysis to detect DNA methylation (DNAm) variants potentially related to FPG in...

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Autores principales: Wang, Weijing, Yao, Wenqin, Tan, Qihua, Li, Shuxia, Duan, Haiping, Tian, Xiaocao, Xu, Chunsheng, Zhang, Dongfeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10351111/
https://www.ncbi.nlm.nih.gov/pubmed/37461060
http://dx.doi.org/10.1186/s13098-023-01136-4
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author Wang, Weijing
Yao, Wenqin
Tan, Qihua
Li, Shuxia
Duan, Haiping
Tian, Xiaocao
Xu, Chunsheng
Zhang, Dongfeng
author_facet Wang, Weijing
Yao, Wenqin
Tan, Qihua
Li, Shuxia
Duan, Haiping
Tian, Xiaocao
Xu, Chunsheng
Zhang, Dongfeng
author_sort Wang, Weijing
collection PubMed
description BACKGROUND: Elevated fasting plasma glucose (FPG) levels can increase morbidity and mortality even when it is below the diagnostic threshold of type 2 diabetes mellitus (T2DM). We conducted a genome-wide DNA methylation analysis to detect DNA methylation (DNAm) variants potentially related to FPG in Chinese monozygotic twins. METHODS: Genome-wide DNA methylation profiling in whole blood of twins was performed using Reduced Representation Bisulfite Sequencing (RRBS), yielding 551,447 raw CpGs. Association between DNAm of single CpG and FPG was tested using a generalized estimation equation. Differentially methylated regions (DMRs) were identified using comb-P approach. ICE FALCON method was utilized to perform the causal inference. Candidate CpGs were quantified and validated using Sequenom MassARRAY platform in a community population. Weighted gene co-expression network analysis (WGCNA) was conducted using gene expression data from twins. RESULTS: The mean age of 52 twin pairs was 52 years (SD: 7). The relationship between DNAm of 142 CpGs and FPG reached the genome-wide significance level. Thirty-two DMRs within 24 genes were identified, including TLCD1, MRPS31P5, CASZ1, and CXADRP3. The causal relationship of top CpGs mapped to TLCD1, MZF1, PTPRN2, SLC6A18, ASTN2, IQCA1, GRIN1, and PDE2A genes with FPG were further identified using ICE FALCON method. Pathways potentially related to FPG were also identified, such as phospholipid-hydroperoxide glutathione peroxidase activity and mitogen-activated protein kinase p38 binding. Three CpGs mapped to SLC6A18 gene were validated in a community population, with a hypermethylated direction in diabetic patients. The expression levels of 18 genes (including SLC6A18 and TLCD1) were positively correlated with FPG levels. CONCLUSIONS: We detect many DNAm variants that may be associated with FPG in whole blood, particularly the loci within SLC6A18 gene. Our findings provide important reference for the epigenetic regulation of elevated FPG levels and diabetes. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13098-023-01136-4.
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spelling pubmed-103511112023-07-18 Identification of key DNA methylation changes on fasting plasma glucose: a genome-wide DNA methylation analysis in Chinese monozygotic twins Wang, Weijing Yao, Wenqin Tan, Qihua Li, Shuxia Duan, Haiping Tian, Xiaocao Xu, Chunsheng Zhang, Dongfeng Diabetol Metab Syndr Research BACKGROUND: Elevated fasting plasma glucose (FPG) levels can increase morbidity and mortality even when it is below the diagnostic threshold of type 2 diabetes mellitus (T2DM). We conducted a genome-wide DNA methylation analysis to detect DNA methylation (DNAm) variants potentially related to FPG in Chinese monozygotic twins. METHODS: Genome-wide DNA methylation profiling in whole blood of twins was performed using Reduced Representation Bisulfite Sequencing (RRBS), yielding 551,447 raw CpGs. Association between DNAm of single CpG and FPG was tested using a generalized estimation equation. Differentially methylated regions (DMRs) were identified using comb-P approach. ICE FALCON method was utilized to perform the causal inference. Candidate CpGs were quantified and validated using Sequenom MassARRAY platform in a community population. Weighted gene co-expression network analysis (WGCNA) was conducted using gene expression data from twins. RESULTS: The mean age of 52 twin pairs was 52 years (SD: 7). The relationship between DNAm of 142 CpGs and FPG reached the genome-wide significance level. Thirty-two DMRs within 24 genes were identified, including TLCD1, MRPS31P5, CASZ1, and CXADRP3. The causal relationship of top CpGs mapped to TLCD1, MZF1, PTPRN2, SLC6A18, ASTN2, IQCA1, GRIN1, and PDE2A genes with FPG were further identified using ICE FALCON method. Pathways potentially related to FPG were also identified, such as phospholipid-hydroperoxide glutathione peroxidase activity and mitogen-activated protein kinase p38 binding. Three CpGs mapped to SLC6A18 gene were validated in a community population, with a hypermethylated direction in diabetic patients. The expression levels of 18 genes (including SLC6A18 and TLCD1) were positively correlated with FPG levels. CONCLUSIONS: We detect many DNAm variants that may be associated with FPG in whole blood, particularly the loci within SLC6A18 gene. Our findings provide important reference for the epigenetic regulation of elevated FPG levels and diabetes. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13098-023-01136-4. BioMed Central 2023-07-17 /pmc/articles/PMC10351111/ /pubmed/37461060 http://dx.doi.org/10.1186/s13098-023-01136-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/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 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Wang, Weijing
Yao, Wenqin
Tan, Qihua
Li, Shuxia
Duan, Haiping
Tian, Xiaocao
Xu, Chunsheng
Zhang, Dongfeng
Identification of key DNA methylation changes on fasting plasma glucose: a genome-wide DNA methylation analysis in Chinese monozygotic twins
title Identification of key DNA methylation changes on fasting plasma glucose: a genome-wide DNA methylation analysis in Chinese monozygotic twins
title_full Identification of key DNA methylation changes on fasting plasma glucose: a genome-wide DNA methylation analysis in Chinese monozygotic twins
title_fullStr Identification of key DNA methylation changes on fasting plasma glucose: a genome-wide DNA methylation analysis in Chinese monozygotic twins
title_full_unstemmed Identification of key DNA methylation changes on fasting plasma glucose: a genome-wide DNA methylation analysis in Chinese monozygotic twins
title_short Identification of key DNA methylation changes on fasting plasma glucose: a genome-wide DNA methylation analysis in Chinese monozygotic twins
title_sort identification of key dna methylation changes on fasting plasma glucose: a genome-wide dna methylation analysis in chinese monozygotic twins
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10351111/
https://www.ncbi.nlm.nih.gov/pubmed/37461060
http://dx.doi.org/10.1186/s13098-023-01136-4
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