Genetic networks of liver metabolism revealed by integration of metabolic and transcriptional profiling.
Genetic networks of liver metabolism revealed by integration of metabolic and transcriptional profiling.
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DOI：
10.1371/journal.pgen.1000034
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发表时间：
2008-03-14
期刊：
影响因子：
4.5
通讯作者：
Attie AD
中科院分区：
文献类型：
--
作者：
Ferrara CT;Wang P;Neto EC;Stevens RD;Bain JR;Wenner BR;Ilkayeva OR;Keller MP;Blasiole DA;Kendziorski C;Yandell BS;Newgard CB;Attie AD
虽然许多数量性状基因座（QTL）影响疾病相关的表型已经通过基因定位和定位克隆检测，个别基因和分子途径导致这些表型的鉴定往往是难以捉摸的。提高对遗传结构的理解的一种方法是通过包括转录和代谢谱来更深入地对表型进行分类。在目前的研究中，我们已经生成并分析了在糖尿病抗性C57 BL/6 leptinob/ob和糖尿病易感BTBR leptinob/ob小鼠品系之间的F2杂交中获得的肝脏样品中的mRNA表达和代谢谱。这种杂交，分离的基因型和生理性状，以前被用来确定几个糖尿病相关的QTL。我们目前的研究包括超过40，000个探针集的微阵列分析，以及三个不同类别（氨基酸，有机酸和酰基肉毒碱）的六十七种中间代谢物的定量质谱测量。我们发现，肝脏代谢物映射到不同的遗传区域，从而表明组织代谢物是可遗传的。我们还证明，基因组分析可以与肝脏mRNA表达和代谢产物分析数据相结合，以构建因果网络来控制肝脏中的特定代谢过程。作为这种综合方法的实际意义的原则的证明，我们说明了一个特定的因果网络，链接基因表达和代谢变化的背景下，谷氨酸代谢的建设，并证明其有效性，通过显示，在网络中的基因响应谷氨酰胺和谷氨酸的可用性的变化。因此，本文所述的方法有可能揭示导致慢性、复杂和高度流行的疾病和病症（如肥胖症和糖尿病）的调控网络。虽然许多数量性状位点（QTL）影响疾病相关的表型已被检测到通过基因定位和定位克隆，确定个别基因及其潜在的作用，导致疾病的分子途径仍然是一个挑战。在这项研究中，我们在基因组分析中包括转录和代谢分析，以解决这一限制。我们研究了耐糖尿病的C57 BL/6 leptinob/ob和糖尿病易感的BTBR leptinob/ob小鼠品系之间的F2杂交，所述品系分离基因型和糖尿病相关的生理性状;血糖、血浆胰岛素和体重。我们的研究表明，肝脏代谢物（包括氨基酸，有机酸和酰基肉毒碱）映射到不同的遗传区域，从而表明组织代谢物是可遗传的。我们还证明，基因组分析可以与肝脏mRNA表达和代谢产物分析数据相结合，以构建因果关系，可测试的网络，用于控制肝脏中的特定代谢过程。我们应用体外研究来证实这种综合方法的有效性，从而提供了一种新的方法来揭示导致慢性，复杂和高度流行的疾病和病症（如肥胖和糖尿病）的调控网络。
Although numerous quantitative trait loci (QTL) influencing disease-related phenotypes have been detected through gene mapping and positional cloning, identification of the individual gene(s) and molecular pathways leading to those phenotypes is often elusive. One way to improve understanding of genetic architecture is to classify phenotypes in greater depth by including transcriptional and metabolic profiling. In the current study, we have generated and analyzed mRNA expression and metabolic profiles in liver samples obtained in an F2 intercross between the diabetes-resistant C57BL/6 leptinob/ob and the diabetes-susceptible BTBR leptinob/ob mouse strains. This cross, which segregates for genotype and physiological traits, was previously used to identify several diabetes-related QTL. Our current investigation includes microarray analysis of over 40,000 probe sets, plus quantitative mass spectrometry-based measurements of sixty-seven intermediary metabolites in three different classes (amino acids, organic acids, and acyl-carnitines). We show that liver metabolites map to distinct genetic regions, thereby indicating that tissue metabolites are heritable. We also demonstrate that genomic analysis can be integrated with liver mRNA expression and metabolite profiling data to construct causal networks for control of specific metabolic processes in liver. As a proof of principle of the practical significance of this integrative approach, we illustrate the construction of a specific causal network that links gene expression and metabolic changes in the context of glutamate metabolism, and demonstrate its validity by showing that genes in the network respond to changes in glutamine and glutamate availability. Thus, the methods described here have the potential to reveal regulatory networks that contribute to chronic, complex, and highly prevalent diseases and conditions such as obesity and diabetes. Although numerous quantitative trait loci (QTL) influencing disease-related phenotypes have been detected through gene mapping and positional cloning, identifying individual genes and their potential roles in molecular pathways leading to disease remains a challenge. In this study, we include transcriptional and metabolic profiling in genomic analyses to address this limitation. We investigated an F2 intercross between the diabetes-resistant C57BL/6 leptinob/ob and the diabetes-susceptible BTBR leptinob/ob mouse strains that segregates for genotype and diabetes-related physiological traits; blood glucose, plasma insulin and body weight. Our study shows that liver metabolites (comprised of amino acids, organic acids, and acyl-carnitines) map to distinct genetic regions, thereby indicating that tissue metabolites are heritable. We also demonstrate that genomic analysis can be integrated with liver mRNA expression and metabolite profiling data to construct causal, testable networks for control of specific metabolic processes in liver. We apply an in vitro study to confirm the validity of this integrative method, and thus provide a novel approach to reveal regulatory networks that contribute to chronic, complex, and highly prevalent diseases and conditions such as obesity and diabetes.
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影响因子：
30.8
作者：
通讯作者：
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影响因子：
2.5
作者：
Drake, Thomas A.;Schadt, Eric E.;Lusis, Aldons J.
通讯作者：
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影响因子：
4.2
作者：
Brosnan, JT
通讯作者：
Brosnan, JT
DOI：
10.1152/ajpendo.1996.271.4.e748
发表时间：
1996-10-01
影响因子：
5.1
作者：
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通讯作者：
Darmaun, D
影响因子：
30.8
作者：
Chesler, EJ;Lu, L;Williams, RW
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Williams, RW