酵母和大肠杆菌基因表达谱与蛋白质相互作用的相关性分析
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摘要
生物体受到外界环境的刺激是多种多样的,要对如此多样的刺激作出应答以维持自身的稳定就需要多个蛋白质之间的相互配合,即蛋白质相互作用,这是近十几年来蛋白质组学研究的重点之一。经外界环境刺激后引发的蛋白质相互作用,按照作用程度可以分为瞬时型作用和牢固型作用。有研究显示,蛋白质相互作用通常对外界不同刺激的应答相对稳定,非相互作用的蛋白其变化幅度却相对明显,对瞬时刺激引起的应答调节有显著效应。因此,我们推测在生物体受到外界刺激后,非相互作用的蛋白往往通过各种联系调控细胞应答以应对外界的瞬间刺激。针对上述问题,我们利用生物信息学的方法,结合基因表达谱与蛋白质相互作用的数据进行深入研究。
本研究以酵母和大肠杆菌作为研究对象。首先,通过整合两种模式生物的基因表达谱与蛋白质相互作用关系的数据,构建了蛋白质相互作用正负样本集,结合基于Pearson相关系数的共表达基因,分析了受到外界刺激后,生物体内有相互作用的蛋白对与非相互作用的蛋白对之间的关系。结果显示,两物种在应激反应之后至恢复稳态的时间内,相互作用的蛋白对与非相互作用的蛋白对之间的变化差异极显著(P<2.2e-16),且通过对比发现非相互作用的蛋白对的基因表达谱具有较高的变化幅度。
然后,设定阂值,筛选出Pearson相关系数r≥0.75且经受环境刺激后共表达系数上升幅度大于0.1的基因,用于构建网络。通过对网络中的基因进行富集分析,我们发现大部分基因主要富集在核糖体的合成,氨基酸的合成/代谢,能量代谢等途径,而这些途径均与蛋白降解和蛋白合成有关,即通过蛋白替代的方式快速的完成细胞状态的改变。
最后,为了寻找共表达调控的共性,我们将共表达基因与对应的蛋白质相互作用网络相结合。通过对蛋白质相互作用网络的深入分析以及对基因共表达的研究,我们更清楚的认识到细胞的表达调控不是对单个基因的调控,而是以某种机制将一簇基因关联起来,从而对整体进行调控。
本研究不仅为理解生物体应答外界环境刺激的作用机制提供了重要线索,而且通过整合与分析不同类型的数据,加深了对共表达机制在生物大分子网络中重要性的认识。
Organisms can live with the various outside stimuli. In order to respond to so many stresses and to maintain the homeostasis, it is indispensable with the coordinating protein-protein interactions which were a hot issue of proteomics research over the last decade. According to the extent of the role, protein-protein interactions stimulated by the external environment can be divided into transient-type and solid-type actions. It was reported that responses caused by protein-protein interactions coping with different stimuli were relatively stable, but non-protein interactions changed obviously which were favorable to regulate responses caused by the transient stimulation. Thereby, we speculate that, when organisms are affected by external stimulation, non-protein interactions usually regulate cell responses through a variety of linkages in response to external transient stimuli. In order to address this phenomenon, based on the bioinformatics approaches, a comprehensive analysis was carried out by combining the data of gene expression profiles with protein-protein interactions.
Firstly, in this thesis two model organisms, yeast and E. coli were selected as research subjects to establish protein-protein interaction positive and negative sample sets through investigating the relationship between gene expression profiles and protein-protein interactions, also the relationship between protein-protein interactions with non-protein interactions when suffered the outside simulation was explored combining with co-expression genes based on Pearson correlation coefficients. The results showed that there were significant differences (P< 2.2e-16) between protein-protein interactions and non-protein interactions during the time when stress response first came up until the relatively steady-state time appeared. Also the changing range of non-protein interactions on the gene expression profiles was higher than that of protein-protein interactions.
Secondly, the threshold value was set, genes with Pearson correlation coefficient r≥0.75 and genes which co-expression coefficient increased more than 0.1 after external stimuli were selected to construct a co-expression network. The gene enrichment analysis showed that, most of the genes mainly enriched in the processes of ribosome synthesis, amino acid synthesis/metabolism and energy metabolism, and all of these processes were related to protein synthesis and degradation, and by rapidly changing the protein alternative method to finish the process of cell condition transformation when stimulated by the external stimuli.
Thirdly, in order to research the similarity of co-expression, the co-expression genes were combined with the corresponding protein-protein interactions network. In addition, the protein-protein interactions network and co-expression genes were deeply studied, we claimed that, the process of cell expression regulated from the whole level through marking a cluster gene with certain mechanism instead of regulation from a single gene level.
This research not only provided important clues to understand the mechanism of organisms response to the various outside stimuli, but also through the comprehensive analysis combining different types of data, the importance of co-expression mechanism on biological macromolecules network was deeply recognized.
本研究以酵母和大肠杆菌作为研究对象。首先,通过整合两种模式生物的基因表达谱与蛋白质相互作用关系的数据,构建了蛋白质相互作用正负样本集,结合基于Pearson相关系数的共表达基因,分析了受到外界刺激后,生物体内有相互作用的蛋白对与非相互作用的蛋白对之间的关系。结果显示,两物种在应激反应之后至恢复稳态的时间内,相互作用的蛋白对与非相互作用的蛋白对之间的变化差异极显著(P<2.2e-16),且通过对比发现非相互作用的蛋白对的基因表达谱具有较高的变化幅度。
然后,设定阂值,筛选出Pearson相关系数r≥0.75且经受环境刺激后共表达系数上升幅度大于0.1的基因,用于构建网络。通过对网络中的基因进行富集分析,我们发现大部分基因主要富集在核糖体的合成,氨基酸的合成/代谢,能量代谢等途径,而这些途径均与蛋白降解和蛋白合成有关,即通过蛋白替代的方式快速的完成细胞状态的改变。
最后,为了寻找共表达调控的共性,我们将共表达基因与对应的蛋白质相互作用网络相结合。通过对蛋白质相互作用网络的深入分析以及对基因共表达的研究,我们更清楚的认识到细胞的表达调控不是对单个基因的调控,而是以某种机制将一簇基因关联起来,从而对整体进行调控。
本研究不仅为理解生物体应答外界环境刺激的作用机制提供了重要线索,而且通过整合与分析不同类型的数据,加深了对共表达机制在生物大分子网络中重要性的认识。
Organisms can live with the various outside stimuli. In order to respond to so many stresses and to maintain the homeostasis, it is indispensable with the coordinating protein-protein interactions which were a hot issue of proteomics research over the last decade. According to the extent of the role, protein-protein interactions stimulated by the external environment can be divided into transient-type and solid-type actions. It was reported that responses caused by protein-protein interactions coping with different stimuli were relatively stable, but non-protein interactions changed obviously which were favorable to regulate responses caused by the transient stimulation. Thereby, we speculate that, when organisms are affected by external stimulation, non-protein interactions usually regulate cell responses through a variety of linkages in response to external transient stimuli. In order to address this phenomenon, based on the bioinformatics approaches, a comprehensive analysis was carried out by combining the data of gene expression profiles with protein-protein interactions.
Firstly, in this thesis two model organisms, yeast and E. coli were selected as research subjects to establish protein-protein interaction positive and negative sample sets through investigating the relationship between gene expression profiles and protein-protein interactions, also the relationship between protein-protein interactions with non-protein interactions when suffered the outside simulation was explored combining with co-expression genes based on Pearson correlation coefficients. The results showed that there were significant differences (P< 2.2e-16) between protein-protein interactions and non-protein interactions during the time when stress response first came up until the relatively steady-state time appeared. Also the changing range of non-protein interactions on the gene expression profiles was higher than that of protein-protein interactions.
Secondly, the threshold value was set, genes with Pearson correlation coefficient r≥0.75 and genes which co-expression coefficient increased more than 0.1 after external stimuli were selected to construct a co-expression network. The gene enrichment analysis showed that, most of the genes mainly enriched in the processes of ribosome synthesis, amino acid synthesis/metabolism and energy metabolism, and all of these processes were related to protein synthesis and degradation, and by rapidly changing the protein alternative method to finish the process of cell condition transformation when stimulated by the external stimuli.
Thirdly, in order to research the similarity of co-expression, the co-expression genes were combined with the corresponding protein-protein interactions network. In addition, the protein-protein interactions network and co-expression genes were deeply studied, we claimed that, the process of cell expression regulated from the whole level through marking a cluster gene with certain mechanism instead of regulation from a single gene level.
This research not only provided important clues to understand the mechanism of organisms response to the various outside stimuli, but also through the comprehensive analysis combining different types of data, the importance of co-expression mechanism on biological macromolecules network was deeply recognized.
引文
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