Multi-tissue coexpression networks reveal unexpected subnetworks associated with disease.
Multi-tissue coexpression networks reveal unexpected subnetworks associated with disease.
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DOI：
10.1186/gb-2009-10-5-r55
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发表时间：
2009
期刊：
影响因子：
12.3
通讯作者：
Schadt EE
中科院分区：
文献类型：
--
作者：
Dobrin R;Zhu J;Molony C;Argman C;Parrish ML;Carlson S;Allan MF;Pomp D;Schadt EE
下丘脑、肝脏或脂肪组织中基因之间的组织间共表达网络使得能够鉴定肥胖特异性基因。肥胖是一种特别复杂的疾病，至少部分涉及遗传和环境对连接下丘脑和几个代谢组织的基因网络的干扰，导致系统水平的能量失衡。为了提供一个组织间的观点，肥胖症的分子状态，与生理状态相关，我们开发了一个框架，用于构建下丘脑，肝脏或脂肪组织中的基因之间的组织到组织的共表达网络。这些网络具有无标度的结构，并且显著地独立于基因-基因共表达网络，基因-基因共表达网络是从更标准的单个组织分析中构建的。这是第一次系统地研究组织间关系，并突出了下丘脑中的基因，这些基因在肥胖小鼠的外周组织控制中充当信息中继。被鉴定为特定于组织间相互作用的子网络富含具有肥胖相关生物功能的基因，如昼夜节律，能量平衡，应激反应或免疫反应。组织到组织网络使得能够识别响应于由不同组织诱导的变化的疾病特异性基因，并且它们还提供关于在全基因组关联研究中识别的肥胖症候选基因的独特细节。从单个组织分析中识别这样的基因是困难的或不可能的。
Tissue-to-tissue coexpression networks between genes in hypothalamus, liver or adipose tissue enable identification of obesity-specific genes. Obesity is a particularly complex disease that at least partially involves genetic and environmental perturbations to gene-networks connecting the hypothalamus and several metabolic tissues, resulting in an energy imbalance at the systems level. To provide an inter-tissue view of obesity with respect to molecular states that are associated with physiological states, we developed a framework for constructing tissue-to-tissue coexpression networks between genes in the hypothalamus, liver or adipose tissue. These networks have a scale-free architecture and are strikingly independent of gene-gene coexpression networks that are constructed from more standard analyses of single tissues. This is the first systematic effort to study inter-tissue relationships and highlights genes in the hypothalamus that act as information relays in the control of peripheral tissues in obese mice. The subnetworks identified as specific to tissue-to-tissue interactions are enriched in genes that have obesity-relevant biological functions such as circadian rhythm, energy balance, stress response, or immune response. Tissue-to-tissue networks enable the identification of disease-specific genes that respond to changes induced by different tissues and they also provide unique details regarding candidate genes for obesity that are identified in genome-wide association studies. Identifying such genes from single tissue analyses would be difficult or impossible.
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影响因子：
120.7
作者：
Hedley, AA;Ogden, CL;Flegal, KM
通讯作者：
Flegal, KM
影响因子：
30.8
作者：
Chambers, John C.;Elliott, Paul;Kooner, Jaspal S.
通讯作者：
Kooner, Jaspal S.
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
影响因子：
56.9
作者：
Barabási, AL;Albert, R
通讯作者：
Albert, R
影响因子：
56.9
作者：
Frayling, Timothy M.;Timpson, Nicholas J.;McCarthy, Mark I.
通讯作者：
McCarthy, Mark I.