Experimental and computational analysis of a large protein network that controls fat storage reveals the design principles of a signaling network.
Experimental and computational analysis of a large protein network that controls fat storage reveals the design principles of a signaling network.
复制标题
DOI：
10.1371/journal.pcbi.1004264
复制
发表时间：
2015-05
影响因子：
4.3
通讯作者：
Zinn K
中科院分区：
文献类型：
--
作者：
Al-Anzi B;Arpp P;Gerges S;Ormerod C;Olsman N;Zinn K
一种结合遗传、蛋白质组学、计算和生理分析的方法被用来定义一个调节出芽酵母（Saccharomyces cerevisiae）脂肪储存的蛋白质网络。对该网络的计算分析表明，它不是无标度的，并且最近似于Watts-Strogatz模型，该模型产生具有高聚类和短路径长度的“小世界”网络。该网络也是模块化的，包含能量水平感应蛋白，连接到四个输出过程：自噬、脂肪酸合成、mRNA加工和MAP激酶信号。每种蛋白质对网络功能的重要性取决于其Katz中心性评分，该评分既与蛋白质在模块中的位置有关，也与模块与整个网络的关系有关。该网络也可分为跨模块边界的子网络，并调节脂肪代谢的不同方面。我们结合遗传学和药理学来同时阻断来自多个网络节点的输出。这种阻塞的表型结果定义了远距离网络节点之间的通信模式，这些模式与Watts-Strogatz模型一致。我们发现了一个巨大的蛋白质网络，它调节着芽殖酵母中脂肪的储存。这个网络包含94种蛋白质，几乎所有这些蛋白质都与网络中的其他蛋白质结合。为了理解诸如此类的大型蛋白质集合的功能，有必要从单个蛋白质的逐个分析转向创建整个网络的计算模型。这将允许将网络分类，并允许研究人员在理论基础上识别关键的网络蛋白质。我们在这里展示了脂肪调节网络符合Watts-Strogatz小世界模型。该模型旨在解释在真实网络中经常观察到的聚类现象，但以前尚未应用于细胞内的信号网络。Watts-Strogatz拓扑的短路径长度和高聚类系数特性允许远距离节点之间的快速通信，并将网络划分为执行不同功能的模块。脂肪调节网络具有模块性，可分为跨模块边界的子网络，对脂肪代谢的不同方面进行调节。我们使用遗传学和药理学结合的方法实验检查了网络中节点之间的通信，并表明通信模式与Watts-Strogatz拓扑结构一致。
An approach combining genetic, proteomic, computational, and physiological analysis was used to define a protein network that regulates fat storage in budding yeast (Saccharomyces cerevisiae). A computational analysis of this network shows that it is not scale-free, and is best approximated by the Watts-Strogatz model, which generates “small-world” networks with high clustering and short path lengths. The network is also modular, containing energy level sensing proteins that connect to four output processes: autophagy, fatty acid synthesis, mRNA processing, and MAP kinase signaling. The importance of each protein to network function is dependent on its Katz centrality score, which is related both to the protein’s position within a module and to the module’s relationship to the network as a whole. The network is also divisible into subnetworks that span modular boundaries and regulate different aspects of fat metabolism. We used a combination of genetics and pharmacology to simultaneously block output from multiple network nodes. The phenotypic results of this blockage define patterns of communication among distant network nodes, and these patterns are consistent with the Watts-Strogatz model. We discovered a large protein network that regulates fat storage in budding yeast. This network contains 94 proteins, almost all of which bind to other proteins in the network. To understand the functions of large protein collections such as these, it will be necessary to move away from one-by-one analysis of individual proteins and create computational models of entire networks. This will allow classification of networks into categories and permit researchers to identify key network proteins on theoretical grounds. We show here that the fat regulation network fits a Watts-Strogatz small-world model. This model was devised to explain the clustering phenomena often observed in real networks, but has not been previously applied to signaling networks within cells. The short path length and high clustering coefficients characteristic of the Watts-Strogatz topology allow for rapid communication between distant nodes and for division of the network into modules that perform different functions. The fat regulation network has modules, and it is divisible into subnetworks that span modular boundaries and regulate different aspects of fat metabolism. We experimentally examined communication between nodes within the network using a combination of genetics and pharmacology, and showed that the communication patterns are consistent with the Watts-Strogatz topology.
登录
查看更多内容
苍蝇和蠕虫中脂肪储存的遗传学：出了什么问题？
影响因子：
3.7
作者：
Al-Anzi B;Zinn K
通讯作者：
Zinn K
影响因子：
9.8
作者：
Beller M;Sztalryd C;Southall N;Bell M;Jäckle H;Auld DS;Oliver B
通讯作者：
Oliver B
影响因子：
14.9
作者：
Fortney K;Xie W;Kotlyar M;Griesman J;Kotseruba Y;Jurisica I
通讯作者：
Jurisica I
影响因子：
4.3
作者：
Batada NN;Hurst LD;Tyers M
通讯作者：
Tyers M
影响因子：
3.1
作者：
Cui, Yajun;Ramnarain, Deepti B.;Denis, Clyde L.
通讯作者：
Denis, Clyde L.