成年双生子肥胖与DNA甲基化的相关性研究---博士研究生论文

中文摘要中国成年双生子肥胖与DNA甲基化的相关性研究一、 研究背景：肥胖是全球和中国面临的日趋严重的健康问题，它不但是一种严重危害人们健康的疾病，而且是高血压、2型糖尿病、心脑血管疾病等慢性病的重要危险因素，从而导致巨大的疾病负担。WHO估计目前全球及中国成人肥胖率分别为13.0%和7.3%，每年至少280万成年人死于由超重或肥胖导致的疾病。深入了解探讨肥胖的病因及发病机制对开展有针对性的预防与控制有着十分重要的意义。大量研究发现，肥胖是由环境因素、遗传因素以及二者交互作用决定的复杂性疾病。与肥胖相关的环境因素包括膳食、体力活动、吸烟、饮酒等生活方式以及社会经济状况等。截至2015年，全基因组关联研究（GWAS）及其Meta分析研究发现，与肥胖相关单核苷酸多态性位点（SNPs）有97个。所有这些基因多态性累计对肥胖变异的解释程度约为2.7%。研究表明，遗传对肥胖的作用不局限于基因结构变异，而是表现在基因-环境的交互作用或基因功能变异。已有队列研究及临床试验发现，膳食和或体力活动与肥胖相关基因存在交互作用。然而这些基因-环境交互作用背后的原因或机制尚不明确。表观遗传现象可能是解释基因-环境交互作用背后原因的重要因素，也有可能是独立于基因-环境交互作用对肥胖产生影响的基因功能变异。表观遗传学是研究基因核苷酸序列不发生改变的情况下，基因表达发生可遗传的变化。表观遗传变异包括DNA甲基化、基因组印记、染色质重塑等方面，其中DNA甲基化是目前研究最多的一种表观遗传现象。近年来，有关肥胖的表观遗传学研究受到广泛关注，探讨相关基因DNA甲基化水平与肥胖的相关性及其规律与特点已成为本领域热点。本研究采用全基因组DNA甲基化关联研究策略，以中国双生子人群作为发现人群，以4个中国一般人群为验证人群，以期发现在中国人群中与肥胖相关的DNA甲基化位点及其作用机制。二、 研究目的：1. 探索特异DNA甲基化位点与肥胖及其相关指标的相关性，环境因素对肥胖与DNA甲基化位点关系的影响；2. 探索多个DNA甲基化位点所在基因构成的生物学途径与肥胖的关系；3. 探索肥胖相关DNA甲基化位点与基因结构及基因表达的关系。三、 研究内容与方法：本研究以中国双生子登记系统的双生子为发现人群，以另外4个中国一般人群队列为验证人群，在全基因组范围分析与肥胖相关的特异DNA甲基化位点、涉及生物学途径及其与基因结构和基因表达之间的作用规律。1. 研究对象采用双生子人群作为全基因组DNA甲基化位点发现人群，一般人群作为全基因组DNA甲基化位点验证人群：（1） 双生子人群中国双生子登记系统是目前中国最大的双生子登记平台，有超过3万对有效登记的双生子。本研究纳入符合以下所有标准的双生子：2013年居住在山东、江苏、浙江和四川地区，年龄18岁，在系统中自报BMI或腰围满足一定条件（双生子中一人BMI27kg/m2，另一人BMI25kg/m2，且二者相差至少为3kg/m2；或男性同性别双生子中一人腰围90cm，另一人腰围90cm；女性同性别双生子中一人腰围80cm，另一人腰围80cm），从未患有肿瘤、冠心病（包括心绞痛、心梗发作、急性冠脉综合症等）和或脑卒中的双生子。（2） 一般人群验证人群来自东风同济队列、焦炉职业工人队列、武汉珠海社区人群队列以及十堰东风医院健康体检人群这4个一般人群队列。纳入所有在基线收集信息时满足年龄18岁，调查时期并未患有急性或慢性疾病，未有任何身体不适感及近2周未发生发热或感染状况的研究对象。2. 资料收集双生子人群用问卷调查收集一般人口学特征、生活方式及疾病状况等信息，包括性别、年龄、吸烟、饮酒、膳食、体力活动、近期用药情况和社会经济状况等。用体格检查收集BMI、腰围、腰臀比等信息。收集1ml外周血血清测定血脂指标（总胆固醇TC、甘油三酯TG、低密度脂蛋白胆固醇LDL-C、高密度脂蛋白胆固醇HDL-C）和血清血糖指标（空腹血糖、空腹血清胰岛素、胰岛素抵抗稳态模型评估HOMA-IR）。收集2ml全血测定糖化血红蛋白并用于全基因组DNA和DNA甲基化检测。一般人群采用与双生子人群相类似的方式进行问卷调查、体格检查及DNA样本检测。3. 样本检测方法（1） 生化指标检测双生子人群TC、TG采用酶比色法测定，LDL-C和HDL-C采用试剂直接测定，空腹血糖采用改进的己糖激酶酶法测定，空腹血清胰岛素采用ADVIA Centaur免疫分析系统的化学发光免疫分析法测定，HOMA-IR根据空腹血糖和胰岛素水平计算得出（HOMA-IR= 空腹血糖（mmol/L）胰岛素（U/ml） / 22.5），糖化血红蛋白采用TosoH G7糖化血红蛋白仪高压液相法检测。上述指标主要采用瑞典Roche公司试剂及标准方法。（2） DNA样本检测利用BioTeke全血DNA提取试剂盒提取全血DNA。DNA甲基化检测采用含亚硫酸氢盐的试剂盒（Zymo EZ DNA Methylation kit）将未甲基化的胞嘧啶C转换成胸腺嘧啶T，而甲基化的胞嘧啶C不会转换，从而区分甲基化位点与未甲基化位点。转换后样本采用Infinium HumanMethylation 450K芯片对外周血全血白细胞进行全基因组DNA甲基化检测。最后通过iScan仪器进行芯片扫描及成像。全基因组DNA检测在全血DNA浓度和纯度达标后，用Human Omni ZhongHua-8 BeadChip芯片对外周血全血白细胞进行全基因组基因型检测并进行卵型鉴定。基因表达检测采用TRIZOL LS溶液（Invitrogen）将白细胞总RNA从全血白细胞分离，用HumanHT-12 v4 Expression BeadChip测定基因表达的信息。4. 数据统计分析策略与方法采用R（3.1.2）语言minfi程序包读取DNA甲基化信号并转化成值（值定义为甲基化信号占甲基化和非甲基化信号之和的比例），进行样本和位点质量控制后，用DASEN函数对数据进行标准化，标准化后的数据用sva函数进行代理变量分析，调整潜在混杂因素。对连续变量（BMI、腰围、腰臀比）采用nlme函数进行混合效应模型分析，用Manhattan图和q-q图分别描述甲基化位点所在位置及人群分层。分类变量（是否肥胖）采用ebayes函数进行经验贝叶斯配对调整的t检验，用volcano图描述甲基化水平在肥胖组与对照组的差异。另外，在富集分析中分别采用GOrilla软件和自编程序进行Fishers精确检验和置换检验，分析多个位点构成的生物学途径与肥胖的关系。甲基化位点与邻近SNPs关联分析采用Locus Zoom图进行描述。全基因组分析采用FDR（Benjamini & Hochberg法）矫正显著性水平。验证人群采用R（3.1.2）语言lm函数分别在单个人群中将DNA甲基化与BMI（或腰围、腰臀比）进行线性回归，再用Metafor程序包进行Meta分析（固定效应模型）合并回归结果（显著性水平按Bonforroni矫正）。DNA甲基化与基因表达关系分析采用quantile-quantile标准化及逆正态性转换后，再做线性回归的方法（显著性水平按Bonforroni矫正）。四、 研究结果：本研究收集双生子人群研究对象469名，平均年龄44.8岁（年龄范围18.0岁到80.9岁），65.9%为男性，52.7%为同卵双生子。一般人群研究对象1307名，分别为东风同济队列765人（平均年龄64.5岁，51.2%为男性）、焦炉职业工人队列137人（平均年龄46.5岁，78.1%为男性）、武汉珠海社区人群队列（武汉162人，珠海99人，平均年龄分别为50.4岁和59.5岁，男性分别占77.8%和80.8%）、十堰东风医院健康体检人群144人（平均年龄41.2岁，74.3%为男性）。甲基化与肥胖相关分析结果可分为以下3个部分：1. DNA甲基化位点与肥胖及其相关指标的相关分析结果：用CNTR作为发现人群构建混合效应模型分析单个DNA甲基化位点与BMI、腰围、腰臀比的相关，分别发现有9个、4个和1个与这些指标相关的位点（显著性水平FDR0.05）。验证人群Meta分析结果发现与BMI、腰围和腰臀比相关的位点分别有6个（cg00574958位于CPT1A基因、cg06500161位于ABCG1基因、cg17061862、cg06012428位于ARID1B基因、cg17156534位于TOP1基因、cg11024682位于SREBF1基因）、3个（cg00574958位于CPT1A基因、cg06500161位于ABCG1基因、cg17061862）和1个（cg00574958位于CPT1A基因）。CPT1A、TOP1、ARID1B和cg17061862与肥胖相关指标呈负相关，ABCG1和SREBF1与肥胖相关指标呈正相关。另外，在CNTR人群发现部分位点和BMI的关系与年龄、饮酒等环境因素有交互作用。2. DNA甲基化位点与肥胖的富集分析结果：富集分析分别采用阳性位点富集及简单基因集合富集分析的方法（显著性水平FDR 27kg/m2 and another person BMI 25kg/m2, with a difference at least 3kg/m2; or male same-sex twins with one waist 90cm, another waist 90cm; female same-sex twins with one waist 80cm, another person waist 80cm), not suffering from cancer, coronary heart disease (including angina, myocardial infarction, acute coronary syndrome, etc.) and/or a history of stroke.(2) General populationsThere were four general population: Dongfeng tongji cohort (DFTJ), the coke oven workers cohort (COW), Wuhan-Zhuhai community based cohort (WHZH) and Shiyan health examination data (SY). Include criterias were: aged more than 18 years, not suffering from acute or chronic illness at the time of investigation and the absence of any physical discomfort or infection status in recent two weeks.2. Data CollectionWe collected twins information about demographic characteristics, lifestyle and disease conditions and other information by questionnaires, including gender, age, smoking, drinking, diet, physical activity, recent drug use and socio-economic status. BMI, waist circumference, waist-hip ratio were collected by physical examination. Lipid and glucose levels were determined by 1ml peripheral blood serum (total cholesterol TC, triglyceride TG, low density lipoprotein cholesterol LDL-C, high-density lipoprotein cholesterol HDL-C, fasting glucose, fasting serum insulin, HOMA- IR). We collected 2ml whole blood for testing glycated hemoglobin, whole-genome genotyping and DNA methylation detection. Analogous methods were used by questionnaires, physical examination and DNA detection in replication populations. 3. Sample Detection(1) Biochemical parametersIn twins population, we used enzyme colorimetry for TC and TG, direct measurement for LDL-C and HDL-C, improved fasting blood glucose hexokinase enzymatic assay for glucose, ADVIA Centaur Immunoassay System chemiluminescence immunoassay for fasting serum insulin, and TosoH G7 glycated hemoglobin meter with high pressure liquid chromatography (HPLC) for hemoglobin. HOMA-IR was calculated by function with fasting blood glucose and insulin. These indicators mainly conducted by Swedish company Roche reagents and standard methods.(2) DNA samples testedWe used BioTeke whole blood DNA extraction kit to extract blood DNA.DNA methylation was detected by containing sodium bisulfite kit (Zymo EZ DNA Methylation kit) to convert unmethylated cytosine to thymidine. After conversion, we used Infinium HumanMethylation 450K chip to detect peripheral blood leukocyte genome-wide DNA methylation then scanned and imaged the chip information through iScan instrument.Whole blood DNA concentration and purity were tested prior to genome-wide DNA detecting. The Human Omni ZhongHua-8 BeadChip was for genome-wide genotyping and twin zygosity determination.It was used TRIZOL LS solution (Invitrogen) to isolate total RNA from whole blood white blood cells, then detected by HumanHT-12 v4 Expression BeadChip for gene expression testing.4. Statistical analysisWe used R (3.1.2) language minfi package to read DNA methylation signal and convert it to value ( value is defined as the proportions of methylated signal in all unmethylated and methylated signals), carried out sample and probes quality control then normalized the data by DASEN function. Normalized data was analyzed by surrogate variable analysis by sva function for adjusting potential confounding factors. For continuous variables (BMI, waist circumference, waist-hip ratio), we used nlme function of mixed effects model, with Manhattan and q-q plots to describe the location of methylation sites and population stratification. Categorical variables (being obese or not) were used ebayes function of empirical Bayes paired moderated t-test, describing the differences in methylation levels in obese and control groups with volcano plots. In addition, the enrichment analysis were conducted by GOrilla software and R codes by Fishers exact test and permutation test to find out obesity-related biological pathways. The asscociation of methylation sites and neighboring SNPs was analyzed using Locus Zoom plot. False discovery rate FDR (Benjamini & Hochberg method) was used to correct the level of significance.Replication populations were used R (3.1.2) lm function to conduct linear regression of DNA methylation and BMI (or waist circumference, waist-hip ratio) respectively, then metaed by Metafor package (fixed effects model) to summarized the four replication cohorts results (significant level corrected by Bonforroni method). Quantile-quantile normalization, inverse-normal transformation and linear regression were used in analysis of DNA methylation and gene expression (significance level corrected by Bonforroni method).Results: The study collected 469 twins, with an average age of 44.8 years (range 18.0 to 80.9 years), 65.9% were male and 52.7% were monozygotic twins. Totally 1307 people were from four general populations with 765 from DFTJ (average age 64.5 years, 51.2% male), 137 from COW (average age 46.5 years, 78.1% male), 261 from WHZH (162 from Wuhan, 99 from Zhuhai, average age was 50.4 years and 59.5 years, men accounted for 77.8% and 80.8%, respectively), and 144 people from SY (average age 41.2 years, 74.3% male). The results can be roughly divided into the following three sections:1. DNA methylation and its correlation with obesity or obese-related traits: We used mixed-effects model in analysis the CNTR data, and regressed DNA methylation sites (known as CpG sites) with BMI, waist circumference (WC) and waist-hip ratio (WHR). The result of discovery population was that BMI, WC and WHR were correlated with nine, four and one CpG sites respectively (significance level FDR 0.05). Linear regression model was conducted in each replication cohort, then meta those results by fixed effects model (significance level adjusted by Bonforroni correction), the number of replicated CpG sites which related with BMI was six (cg00574958 located in CPT1A, cg06500161 located in ABCG1, cg17061862, cg06012428 located in ARID1B, cg17156534 located in TOP1 and cg11024682 located in SREBF1). Three CpG sites (cg00574958, cg06500161 and cg17061862) were correlated with WC and 1 site (cg00574958) with WC after replication. There are three (CPT1A, ABCG1 and SREBF1) genes have been reported in other studies, and their biological mechanisms were relatively clear. While the remaining three (TOP1, ARID1B and cg17061862) sites were newly discovered in Chinese population. CPT1A, TOP1, ARID1B and cg17061862 showed a negative relationship with BMI, yet ABCG1 and SREBF1 positively correlated with BMI. In addition, CNTR data found some obese-related CpG sites had interaction with age, alcohol consumption and other environmental factors. 2. DNA methylation sites enrichment analysis results: There were two statistical methods in analyzing enrichment, one was Fishers exact test and the other one was simple gene set enrichment analysis (significance level FDR 0.05). Fishers exact test found that significant sites enriched on cholesterol and lipid synthesis and metabolism related pathways (GO: 0045542, GO: 0090205, GO: 0045834, GO: 0007623, GO: 0010893, GO: 0045540 and GO: 0032365). Simple gene set enrichment analysis found sites enriched on protein glycosylation (GO: 0006486), negative regulation of adenylate cyclase activity pathway (GO: 0007194), cell proliferation, regulation of muscle tissue formation, cell cycle related pathways.3. Obesity-related DNA methylation and gene structure, gene expression correlation result: We extracted single nucleotide polymorphisms loci (SNPs) located within 1Mb to the CpG sites from genome-wide genotyping data. Mixed-effects model was used to identify the association between DNA methylation sites and extracted SNPs (additive model, significance level take P value 10-4). The results showed that the SNPs were associated with cg17061862 sites (rs7937639 with the minimum P value = 1.75E-13), but no association were found with SNPs and CpG sites located on CPT1A, ABCG1, ARID1B, TOP1 or SREBF1. Obesity-related DNA methylation sites cg06500161 was correlated with 2 gene expression site (ILMN_2329927 and ILMN_1794782 in ABCG1 with P values at 9.11E-05 and 5.14E-04 respectively). Site cg11024682 was related to two gene expression sites ILMN_2328986 and ILMN_1663035 located in SREBF1 (P values were 1.24E-02 and 4.79E-03). These methylation sites and gene expressions were negatively correlated.Conclusions: 1. There were six BMI-related DNA methylation sites in Chinese population, including three (CPT1A, ABCG1 and SREBF1) reported in other populations and other three (TOP1, ARID1B and cg17061862) newly discovered in Chinese population.2. The enrichment analysis showed that major pathways related to obesity involved in lipid and energy metabolism, and many other related biological pathways also had impact on obesity.3. We found no association of methylation sites in CPT1A, ABCG1, ARID1B, TOP1 or SREBF1 gene with adjacent SNPs. Besides, methylation sites on ABCG1 and SREBF1 gene were correlated with gene expression.In our study, we analyzed genome-wide DNA methylation and its relationship with obesity or obese-related outcomes. The results were discovered in one twin cohort and replicated by four Chinese general population cohorts. In addition, the combination of genomics (gene locus), ep