Adipose methylome integrative-omic analyses reveal genetic and dietary metabolic health drivers and insulin resistance classifiers.
Adipose methylome integrative-omic analyses reveal genetic and dietary metabolic health drivers and insulin resistance classifiers.
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DOI：
10.1186/s13073-022-01077-z
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发表时间：
2022-07-18
期刊：
影响因子：
12.3
通讯作者：
Bell JT
中科院分区：
文献类型：
--
作者：
Christiansen C;Tomlinson M;Eliot M;Nilsson E;Costeira R;Xia Y;Villicaña S;Mompeo O;Wells P;Castillo-Fernandez J;Potier L;Vohl MC;Tchernof A;Moustafa JE;Menni C;Steves CJ;Kelsey K;Ling C;Grundberg E;Small KS;Bell JT
有相当多的证据表明DNA甲基化组在代谢健康中的重要性，例如，稳健的甲基化特征与体重指数（BMI）相关。然而，内脏脂肪（VF）质量积累是一个更大的危险因素，代谢疾病比体重指数单独。在这项研究中，我们通过关注VF作为代谢疾病的主要危险因素来剖析与代谢健康相关的皮下脂肪组织（SAT）甲基化组特征。我们将结果与遗传，血液甲基化，SAT基因表达，血液代谢组学，饮食摄入和代谢表型数据相结合，以评估和量化所识别信号的遗传和环境驱动因素，以及它们的潜在功能作用。进行表观基因组关联分析，以确定来自538名TwinsUK参与者的SAT样本中的内脏脂肪量相关差异甲基化位置（VF-DMPs）。在来自3个独立队列的333名个体中进行验证和复制。为了评估VF-DMP的功能影响，在注释到VF-DMP的基因处确定VF和基因表达之间的关联，并进行关联分析以确定VF-DMP处的甲基化是否与基因表达相关。进行了进一步的表观遗传分析，以比较作为响应变量的VF-DMP的甲基化水平和一系列不同的代谢健康表型，包括android：gynoid脂肪比（AGR）、脂质、血液代谢组学谱、胰岛素抵抗、T2 D和饮食摄入变量。将所有分析的结果进行整合，以识别显示SAT功能改变并与代谢健康密切相关的信号。我们在788个基因中鉴定了1181个CpG位点，这些基因被VF（VF-DMPs）差异甲基化，在胰岛素信号传导途径中具有显著富集。整合遗传学、基因表达、代谢组学、饮食和代谢性状的VF-DMPs的后续交叉组学分析突出了位于与代谢疾病机制强相关的9个基因中的VF-DMPs，其中在FRES 1、SREBF 1、TAGLN 2、PC和CFAP 410中复制信号。PC甲基化显示了饮食对VF的介导作用的证据。与单独的BMI或VF相比，FIGH DNA甲基化表现出对VF的推定因果作用，其也与胰岛素抵抗和FIGH中的甲基化水平更好地分类胰岛素抵抗（AUC=0.91）密切相关。我们的研究结果有助于验证SAT的肥胖相关甲基化特征，并对代谢疾病风险有深入了解。在线版本包含补充材料，可通过10.1186/s13073-022-01077-z获得。
There is considerable evidence for the importance of the DNA methylome in metabolic health, for example, a robust methylation signature has been associated with body mass index (BMI). However, visceral fat (VF) mass accumulation is a greater risk factor for metabolic disease than BMI alone. In this study, we dissect the subcutaneous adipose tissue (SAT) methylome signature relevant to metabolic health by focusing on VF as the major risk factor of metabolic disease. We integrate results with genetic, blood methylation, SAT gene expression, blood metabolomic, dietary intake and metabolic phenotype data to assess and quantify genetic and environmental drivers of the identified signals, as well as their potential functional roles. Epigenome-wide association analyses were carried out to determine visceral fat mass-associated differentially methylated positions (VF-DMPs) in SAT samples from 538 TwinsUK participants. Validation and replication were performed in 333 individuals from 3 independent cohorts. To assess functional impacts of the VF-DMPs, the association between VF and gene expression was determined at the genes annotated to the VF-DMPs and an association analysis was carried out to determine whether methylation at the VF-DMPs is associated with gene expression. Further epigenetic analyses were carried out to compare methylation levels at the VF-DMPs as the response variables and a range of different metabolic health phenotypes including android:gynoid fat ratio (AGR), lipids, blood metabolomic profiles, insulin resistance, T2D and dietary intake variables. The results from all analyses were integrated to identify signals that exhibit altered SAT function and have strong relevance to metabolic health. We identified 1181 CpG positions in 788 genes to be differentially methylated with VF (VF-DMPs) with significant enrichment in the insulin signalling pathway. Follow-up cross-omic analysis of VF-DMPs integrating genetics, gene expression, metabolomics, diet, and metabolic traits highlighted VF-DMPs located in 9 genes with strong relevance to metabolic disease mechanisms, with replication of signals in FASN, SREBF1, TAGLN2, PC and CFAP410. PC methylation showed evidence for mediating effects of diet on VF. FASN DNA methylation exhibited putative causal effects on VF that were also strongly associated with insulin resistance and methylation levels in FASN better classified insulin resistance (AUC=0.91) than BMI or VF alone. Our findings help characterise the adiposity-associated methylation signature of SAT, with insights for metabolic disease risk. The online version contains supplementary material available at 10.1186/s13073-022-01077-z.
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影响因子：
14.9
作者：
Buniello, Annalisa;MacArthur, Jacqueline A. L.;Parkinson, Helen
通讯作者：
Parkinson, Helen
影响因子：
9.8
作者：
Grundberg, Elin;Meduri, Eshwar;Deloukas, Panos
通讯作者：
Deloukas, Panos
DOI：
10.1038/oby.2002.164
发表时间：
2002-12-01
期刊：
OBESITY RESEARCH
影响因子：
--
作者：
Faria, AN;Ribeiro, FF;Zanella, MT
通讯作者：
Zanella, MT
影响因子：
5.8
作者：
Aryee, Martin J.;Jaffe, Andrew E.;Irizarry, Rafael A.
通讯作者：
Irizarry, Rafael A.
DOI：
10.1126/science.1246949
发表时间：
2014-03-07
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Fairfax BP;Humburg P;Makino S;Naranbhai V;Wong D;Lau E;Jostins L;Plant K;Andrews R;McGee C;Knight JC
通讯作者：
Knight JC