The transcriptional landscape of age in human peripheral blood.
The transcriptional landscape of age in human peripheral blood.
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人类外周血中年龄的转录景观。
DOI：
10.1038/ncomms9570
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发表时间：
2015-10-22
影响因子：
16.6
通讯作者：
Johnson AD
中科院分区：
文献类型：
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作者：
Peters MJ;Joehanes R;Pilling LC;Schurmann C;Conneely KN;Powell J;Reinmaa E;Sutphin GL;Zhernakova A;Schramm K;Wilson YA;Kobes S;Tukiainen T;NABEC/UKBEC Consortium;Ramos YF;Göring HH;Fornage M;Liu Y;Gharib SA;Stranger BE;De Jager PL;Aviv A;Levy D;Murabito JM;Munson PJ;Huan T;Hofman A;Uitterlinden AG;Rivadeneira F;van Rooij J;Stolk L;Broer L;Verbiest MM;Jhamai M;Arp P;Metspalu A;Tserel L;Milani L;Samani NJ;Peterson P;Kasela S;Codd V;Peters A;Ward-Caviness CK;Herder C;Waldenberger M;Roden M;Singmann P;Zeilinger S;Illig T;Homuth G;Grabe HJ;Völzke H;Steil L;Kocher T;Murray A;Melzer D;Yaghootkar H;Bandinelli S;Moses EK;Kent JW;Curran JE;Johnson MP;Williams-Blangero S;Westra HJ;McRae AF;Smith JA;Kardia SL;Hovatta I;Perola M;Ripatti S;Salomaa V;Henders AK;Martin NG;Smith AK;Mehta D;Binder EB;Nylocks KM;Kennedy EM;Klengel T;Ding J;Suchy-Dicey AM;Enquobahrie DA;Brody J;Rotter JI;Chen YD;Houwing-Duistermaat J;Kloppenburg M;Slagboom PE;Helmer Q;den Hollander W;Bean S;Raj T;Bakhshi N;Wang QP;Oyston LJ;Psaty BM;Tracy RP;Montgomery GW;Turner ST;Blangero J;Meulenbelt I;Ressler KJ;Yang J;Franke L;Kettunen J;Visscher PM;Neely GG;Korstanje R;Hanson RL;Prokisch H;Ferrucci L;Esko T;Teumer A;van Meurs JB;Johnson AD
疾病的发病率随着年龄的增长而增加，但导致疾病易感性增加的衰老分子特征仍然没有得到充分的了解。在这里，我们对14，983名欧洲血统（包括复制）的个体进行了全血基因表达荟萃分析，并确定了1，497个随实际年龄差异表达的基因。与年龄相关的基因并不比其他基因具有更多的与年龄相关的CpG甲基化位点，而是在增强子和绝缘子区域中富集了与实际年龄和基因表达水平相关的潜在功能性CpG甲基化位点。我们进一步使用基因表达谱来计算个体的“转录组年龄”，并表明转录组年龄和实际年龄之间的差异与与衰老相关的生物学特征有关，如血压，胆固醇水平，空腹血糖和体重指数。转录组预测模型增加了生物相关性并补充了现有的表观遗传预测模型，并且可以被其他人用于计算外部队列中的转录组年龄。 老龄化增加了许多疾病的风险。在这里，作者比较了超过14，000个个体的血细胞转录组，并确定了一组约1，500个随年龄而不同表达的基因，揭示了与衰老过程和年龄相关疾病相关的转录程序。
Disease incidences increase with age, but the molecular characteristics of ageing that lead to increased disease susceptibility remain inadequately understood. Here we perform a whole-blood gene expression meta-analysis in 14,983 individuals of European ancestry (including replication) and identify 1,497 genes that are differentially expressed with chronological age. The age-associated genes do not harbor more age-associated CpG-methylation sites than other genes, but are instead enriched for the presence of potentially functional CpG-methylation sites in enhancer and insulator regions that associate with both chronological age and gene expression levels. We further used the gene expression profiles to calculate the ‘transcriptomic age' of an individual, and show that differences between transcriptomic age and chronological age are associated with biological features linked to ageing, such as blood pressure, cholesterol levels, fasting glucose, and body mass index. The transcriptomic prediction model adds biological relevance and complements existing epigenetic prediction models, and can be used by others to calculate transcriptomic age in external cohorts. Ageing increases the risk of many diseases. Here the authors compare blood cell transcriptomes of over 14,000 individuals and identify a set of about 1,500 genes that are differently expressed with age, shedding light on transcriptional programs linked to the ageing process and age-associated diseases.