Identification of genes and networks driving cardiovascular and metabolic phenotypes in a mouse F2 intercross.
Identification of genes and networks driving cardiovascular and metabolic phenotypes in a mouse F2 intercross.
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鉴定驱动小鼠F2间交叉中心血管和代谢表型的基因和网络。
DOI：
10.1371/journal.pone.0014319
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发表时间：
2010-12-14
期刊：
影响因子：
3.7
通讯作者：
Schadt EE
中科院分区：
文献类型：
--
作者：
Derry JM;Zhong H;Molony C;MacNeil D;Guhathakurta D;Zhang B;Mudgett J;Small K;El Fertak L;Guimond A;Selloum M;Zhao W;Champy MF;Monassier L;Vogt T;Cully D;Kasarskis A;Schadt EE
为了鉴定心血管和代谢表型的基因和途径，我们通过将来自脂肪、肾脏和肝脏组织的全基因组基因表达数据与在群体中测量的生理终点相关联，对小鼠C57 BL/6 J x A/J F2（B6 AF 2）杂交进行了综合分析。我们已经确定了大量的性状QTL，包括基因座驱动的心脏功能的变化在染色体2和6和一个热点的肥胖，能量代谢和葡萄糖性状的染色体8。整合脂肪基因表达数据确定了驱动8号染色体肥胖QTL的核心基因集。该8号染色体transeQTL签名包含与线粒体功能和氧化磷酸化相关的基因，并映射到先前与人类肥胖有关的人类保守功能的子网络。此外，对应于来自签名的正向同源基因的人类eSNP在DIAGRAM队列中显示出与II型糖尿病相关的富集，这支持了以下观点：染色体8基因座扰乱了人类感知DNA变异的分子网络，进而影响代谢疾病风险。我们在功能上验证了这种方法的预测，通过展示来自transeQTL签名，Akr 1b 8，Emr 1和Rgs 2的三个基因在敲除小鼠中的代谢表型。此外，我们表明，敲除这些基因中的两个，Akr 1b 8和Rgs 2的转录签名，映射到与染色体8 transeQTL签名相关的F2网络模块，这些模块反过来又与F2群体中的肥胖非常显着相关。总的来说，这项研究证明了如何将基因表达数据与基于网络的框架中的QTL分析相结合，可以帮助阐明可以从小鼠翻译到人类的疾病的分子驱动因素。
To identify the genes and pathways that underlie cardiovascular and metabolic phenotypes we performed an integrated analysis of a mouse C57BL/6J x A/J F2 (B6AF2) cross by relating genome-wide gene expression data from adipose, kidney, and liver tissues to physiological endpoints measured in the population. We have identified a large number of trait QTLs including loci driving variation in cardiac function on chromosomes 2 and 6 and a hotspot for adiposity, energy metabolism, and glucose traits on chromosome 8. Integration of adipose gene expression data identified a core set of genes that drive the chromosome 8 adiposity QTL. This chromosome 8 trans eQTL signature contains genes associated with mitochondrial function and oxidative phosphorylation and maps to a subnetwork with conserved function in humans that was previously implicated in human obesity. In addition, human eSNPs corresponding to orthologous genes from the signature show enrichment for association to type II diabetes in the DIAGRAM cohort, supporting the idea that the chromosome 8 locus perturbs a molecular network that in humans senses variations in DNA and in turn affects metabolic disease risk. We functionally validate predictions from this approach by demonstrating metabolic phenotypes in knockout mice for three genes from the trans eQTL signature, Akr1b8, Emr1, and Rgs2. In addition we show that the transcriptional signatures for knockout of two of these genes, Akr1b8 and Rgs2, map to the F2 network modules associated with the chromosome 8 trans eQTL signature and that these modules are in turn very significantly correlated with adiposity in the F2 population. Overall this study demonstrates how integrating gene expression data with QTL analysis in a network-based framework can aid in the elucidation of the molecular drivers of disease that can be translated from mice to humans.
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