细胞周期调控蛋白质相互作用网络及接头分子TANK募集细胞周期激酶PLK1抑制NF-κB信号通路的机制研究
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摘要
细胞周期是生命的基本特征之一,其研究一直是生命科学领域的重点。详细了解细胞周期的功能、调控及其它潜在的分子机制,对生物工程及医药开发领域有巨大的应用价值。蛋白质-蛋白质相互作用是生命活动的基础,研究细胞周期调控蛋白质在相互作用中形成的复杂网络是全面分析和认识细胞周期分子调控机理的重要手段。开展肝脏中细胞周期相关的蛋白质相互作用网络研究也是中国人类肝脏蛋白质组计划(Chinese human liver proteome project, CNHLPP)之蛋白质相互作用连锁图研究专题的重要组成部分。
本研究采用ProQuestTM酵母双杂交(yeast two-hybrid,Y2H)系统,选择与细胞周期驱动和调控紧密相关且在肝脏中表达的蛋白质作为诱饵,对成人肝脏cDNA文库进行筛选。细胞周期的驱动及调控蛋白主要包括：细胞周期动力系统即细胞周期蛋白依赖性激酶(cyclin dependent kinase,CDK)和对其进行控制的细胞周期蛋白(cyclin)、细胞周期蛋白依赖性激酶抑制子(cyclin dependent kinase inhibitor, CKI);控制遗传物质复制起始的细胞周期正调控系统即周期限制点调控蛋白；保证细胞遗传物质复制和分配质量的细胞周期负调控系统即周期检查点调控蛋白;对细胞周期进行质量控制的翻译后修饰酶类和细胞周期相关生长因子及受体等。我们最终成功构建25个诱饵载体对应于24个细胞周期蛋白,主要集中在细胞周期动力系统(11个)和细胞周期检查点系统(5个)。我们用其中符合筛选标准的23个诱饵进行Y2H筛选,获604个候选猎物阳性克隆,其中336个获得测序结果。去除非编码区和不符合读框的序列,并去除冗余后,得到101个蛋白质间的90对相互作用。其中已知相互作用8对,说明我们的Y2H筛选系统所获相互作用数据真实可靠。而大量未知相互作用的发现提示,在细胞周期调控网络中可能还存在很多尚未揭示的调控机制。
随后,我们采用实验验证和生物信息学方法对相互作用的可信度进行评价。通过对所得90对相互作用进行酵母回转验证,总体回转阳性率为49%；随机挑取回转阳性相互作用中的16对,分别采用免疫共沉淀(co-immunoprecipitation,CoIP).芯片免疫共沉淀和GST-pull down的方法验证,其中至少在一种方法中验证为阳性的比率是81%,高于已报道的人类相互作用组中高可信度数据的共亲和纯化实验的阳性比例(62%)。继而,我们采用多种生物信息学方法对相互作用的生物学假阳性进行了评价,包括PubMed和HPRD(human protein reference database)中的已知相互作用(lierature curated interactions,LCI).基因敲除表型、基因共表达、比较基因组学、GO(gene ontology)功能注释、相互作用结构域、模式生物同源性比较和网络拓扑结构分析等。发现有16对(18%,16/90)相互作用的诱饵和猎物的基因名共同出现在PubMed的文献中,提示它们具有较高的功能相关性；诱饵和猎物都有基因敲除表型记录的相互作用对中,16对(46%,16/35)相互作用具有相同的基因敲除表型；诱饵和猎物都有GO注释的58对相互作用中,共24对(41%,24/58)相互作用的诱饵和猎物具有相同的亚细胞定位,17对(29%,17/58)相互作用的诱饵和猎物具有相同的分子功能,21对(36%,21/58)相互作用的诱饵和猎物参与相同的生物学过程；18对(20%,18/90)相互作用的诱饵和猎物有相互作用结构域存在；10对(11%,10/90)相互作用可以形成网络拓扑结构。为从总体上描述相互作用的可信度,我们参考Stelzl U等人在蛋白质相互作用网络研究中的打分标准[1],整合实验数据和各种生物学相关性分析结果,采用简单投票的方式,建立了一个含有10个标准的可信度评分体系。通过评分,低、中、高可信度的相互作用分别占20%、42%和38%。
我们采用可视化软件Osprey进行了相互作用网络绘制和分析,从获得的相互作用网络来看,新获得的相互作用对原有网络进行了有效的补充和拓展。获得多个相互作用结果的细胞周期蛋白家族新成员：CCNG1和CCNH可能是细胞周期调控网络中的新的中心蛋白。新的相互作用增强了细胞周期调控两大功能子网络：Cyclin-CDK-CKI动力系统和细胞周期检查点系统内部和之间的联系,例如：GADD45A-RCHY1-TP53和CCNB1-MAGED1-PLK1,提示二个子网络功能上的密切相关,这些相互作用蛋白可能形成细胞周期调控网络中新的功能复合体。新的相互作用还揭示了细胞周期检查点网络和NF-κB (nuclear factorκB)信号通路网络的新的交联,例如：PLK1-TANK, BRCA1-JUP-IKBE等,将两个响应DNA损伤信号的下游分支通路联系了起来,发现了二者之间新的功能联系。结合相互作用实验验证结果,我们针对高可信度的相互作用进行了深入的文献挖掘。我们发现通过GADD45A与凋亡蛋白Bax相互作用蛋白SH3GLB1的相互作用,提示了GADD45A诱导凋亡的新的分子机制；通过CDKN1A与有丝分裂原诱导的GTP酶激活蛋白SIPA1的相互作用提示了SIPA1参与细胞周期负调控的新的功能角色；PLK1与NF-κB信号通路接头蛋白TANK的相互作用介导了细胞周期和NF-κB信号通路之间潜在的交联。我们的工作为进一步的相互作用功能研究提供了重要线索。
最后,我们选取高可信度相互作用—细胞周期激酶PLK1 (polo-like kinase 1)与NF-κB信号通路接头蛋白TANK (TRAF family member associated NF-κB activator)进行了深入的功能研究。细胞周期调控激酶参与NF-κB信号通路的调节,是重要生物学过程细胞周期和NF-κB信号通路的新的功能联系。实验结果表明支架分子TANK通过募集PLK1与IκB激酶(IκB kinases, IKKs)复合体的调控亚基IKKγ即NEMO (NF-κB essential modulator)结合,并通过抑制NEMO的泛素化来负调控NF-κB关键调控激酶IKKs复合体的活性。
综上所述,本研究采用酵母双杂交方法,对细胞周期调控网络进行了初步研究,发现了多个细胞周期动力系统和细胞周期检查点系统之间的新的分子关联,为研究细胞周期调控的精细分子机制提供了素材。新发现的多个相互作用还将细胞周期检查点调控系统和NF-κB信号通路联系了起来,揭示了基本生物学过程与重要信号通路之间的功能相关性。细胞周期调控蛋白参与先天免疫相关信号通路的调控是进一步功能研究的重要提示。新发现的细胞周期蛋白与凋亡途径蛋白的相互作用(例如：GADD45A-SH3GLB1),与癌症等疾病相关蛋白的相互作用(例如：CCNB1-MAGED1),为从分子水平上探究细胞周期调控机制及与疾病的关系提供了线索。在我们构建的细胞周期调控蛋白质的相互作用网络中,多个网络核心分子及其作用分子都有望成为药物筛选的靶点,对未来的药物开发可能具有重要意义。
Cell cycle is a basic process and important character of the life. To understand its function and regulation mechanisms is a key field of the life science, and is critical to many applications in biotechnology and medicine. Most proteins exert their functions through protein-protein interaction. The research of the network of cell cycle proteins, might uncover the underlying molecular mechanisms involved. Large-scale screening of protein-protein interactions of cell cycle-associated proteins provides a comprehensive analysis of cell cycle and is also an important part of the Chinese Human Liver Proteome Project (CNHLPP).
In this dissertation, we successfully cloned 25 genes that have important roles in cell cycle regulation which include cyclins, cyclin dependent kinases (CDKs), cyclin dependent kinase inhibitors (CKIs), cell cycle restriction point regulators, cell cycle check point regulators, kinds of enzymes and growth factors/receptors. Twenty-two of them were screened against human adult liver cDNA library with ProQuestTM yeast two-hybrid system. About 604 candidate colonies were identified and 336 preys (AD-Y) identities were determined with interaction sequence tags (ISTs) by sequencing. Finally,90 different protein interaction pairs including 101 proteins were obtained, among which 8 interactions were reported. To estimate the technical false positive rate, all interactions were verified by retransformation in yeast cells. The total recovery rate for the interactions is 49%. To evaluate the accuracy of the Y2H database, we randomly selected Y2H interaction pairs with positive result in retransformation assay, and tested them by co-immunoprecipitation (CoIP), chip-CoIP (a co-immunoprecipitation assay based on protein chips technology) or GST-pull down assays.13 out of 16 interaction pairs were validated by at least one of the three different assays. The positive rate is 81%, which is higher than those reported in other human interactome research.
Several independent bioinformatics methods were used to evaluate the potential biological relevance of the identified interactions. By searching the PubMed, HPRD and STRING databases for co-occurrence of the corresponding gene symbols, we found that 16 interactions (18%) of their partners show linkage of the corresponding gene symbols. According to the mouse genome informatics (MGI) data, we showed that 16 interactions of their partners show similar gene knockout phenotype. Our interactions were also investigated by GO annotation. We found that 24 interactions share same cellular component,17 interactions share same molecular function, and 21 interactions participate in the same biological process. Moreover, we found 18 interactions contain interacting domains in the interacting domain analysis. These results show that our interactions data are of high confidence. With experimental and bioinformatics information, we established a scoring system for confidence evaluation. Each interaction was scored with this system and then grouped into three confidence sets according to their scores, resulting in 34 interactions (38%) with high confidence, 38 interactions (42%) with medium and 18 (20%) with low confidence.
Subsequently, we presented the interactions in visible network graphs with Osprey network visualization system. Through integrating LCI (Literature Curated Interactions) data, two main sub-networks were found in cell cycle regulation system which includes Cyclin-CDK-CKI regulation system and cell cycle check point associated system. The two sub-networks were connected by interactions such as GADD45A-RCHY1-TP53 and CCNB 1-MAGED 1-PLK1 obtained from our study. We also found the interactions among cell cycle regulated proteins and NF-κB signaling pathway, suggesting new crosstalks between cell cycle process and NF-κB signaling pathway. The results indicate that our dataset is valuable complement to the existent network. By integrating the experimental and various confidence evaluation information, as well as literature research, we performed a comprehensive analysis of the biological relevance of some interactions.
Then, we confirmed that cell cycle regulated kinase PLK1 (polo-like kinase 1) interacts with adaptor molecule TANK (TRAF family member associated NF-κB activator) in vitro and in vivo. In addition, overexpression of PLK1 inhibits NF-κB activation induced by upstream stimulators including cytokines, activators, but not by p65, and this negative regulation depend on the interaction with TANK. TANK is a TRAF interacting protein that may negatively regulates NF-κB activation. But the underlying mechanism remains unclear. Our results showed that TANK recruited PLK1 binding to NEMO (NF-κB essential modulator), the mediator subunit of IKKs (IκB kinases) complex, and negatively regulates TNF-induced IKK activation through the inhibiting effect of PLK1 on NEMO ubiquitination.
In conclusion, this dissertation presents a primary interaction network of cell cycle regulation associated proteins by Y2H library screening, which might be meaningful to understand the regulation mechanisms of cell cycle and provides potential clues to functional research.
本研究采用ProQuestTM酵母双杂交(yeast two-hybrid,Y2H)系统,选择与细胞周期驱动和调控紧密相关且在肝脏中表达的蛋白质作为诱饵,对成人肝脏cDNA文库进行筛选。细胞周期的驱动及调控蛋白主要包括：细胞周期动力系统即细胞周期蛋白依赖性激酶(cyclin dependent kinase,CDK)和对其进行控制的细胞周期蛋白(cyclin)、细胞周期蛋白依赖性激酶抑制子(cyclin dependent kinase inhibitor, CKI);控制遗传物质复制起始的细胞周期正调控系统即周期限制点调控蛋白；保证细胞遗传物质复制和分配质量的细胞周期负调控系统即周期检查点调控蛋白;对细胞周期进行质量控制的翻译后修饰酶类和细胞周期相关生长因子及受体等。我们最终成功构建25个诱饵载体对应于24个细胞周期蛋白,主要集中在细胞周期动力系统(11个)和细胞周期检查点系统(5个)。我们用其中符合筛选标准的23个诱饵进行Y2H筛选,获604个候选猎物阳性克隆,其中336个获得测序结果。去除非编码区和不符合读框的序列,并去除冗余后,得到101个蛋白质间的90对相互作用。其中已知相互作用8对,说明我们的Y2H筛选系统所获相互作用数据真实可靠。而大量未知相互作用的发现提示,在细胞周期调控网络中可能还存在很多尚未揭示的调控机制。
随后,我们采用实验验证和生物信息学方法对相互作用的可信度进行评价。通过对所得90对相互作用进行酵母回转验证,总体回转阳性率为49%；随机挑取回转阳性相互作用中的16对,分别采用免疫共沉淀(co-immunoprecipitation,CoIP).芯片免疫共沉淀和GST-pull down的方法验证,其中至少在一种方法中验证为阳性的比率是81%,高于已报道的人类相互作用组中高可信度数据的共亲和纯化实验的阳性比例(62%)。继而,我们采用多种生物信息学方法对相互作用的生物学假阳性进行了评价,包括PubMed和HPRD(human protein reference database)中的已知相互作用(lierature curated interactions,LCI).基因敲除表型、基因共表达、比较基因组学、GO(gene ontology)功能注释、相互作用结构域、模式生物同源性比较和网络拓扑结构分析等。发现有16对(18%,16/90)相互作用的诱饵和猎物的基因名共同出现在PubMed的文献中,提示它们具有较高的功能相关性；诱饵和猎物都有基因敲除表型记录的相互作用对中,16对(46%,16/35)相互作用具有相同的基因敲除表型；诱饵和猎物都有GO注释的58对相互作用中,共24对(41%,24/58)相互作用的诱饵和猎物具有相同的亚细胞定位,17对(29%,17/58)相互作用的诱饵和猎物具有相同的分子功能,21对(36%,21/58)相互作用的诱饵和猎物参与相同的生物学过程；18对(20%,18/90)相互作用的诱饵和猎物有相互作用结构域存在；10对(11%,10/90)相互作用可以形成网络拓扑结构。为从总体上描述相互作用的可信度,我们参考Stelzl U等人在蛋白质相互作用网络研究中的打分标准[1],整合实验数据和各种生物学相关性分析结果,采用简单投票的方式,建立了一个含有10个标准的可信度评分体系。通过评分,低、中、高可信度的相互作用分别占20%、42%和38%。
我们采用可视化软件Osprey进行了相互作用网络绘制和分析,从获得的相互作用网络来看,新获得的相互作用对原有网络进行了有效的补充和拓展。获得多个相互作用结果的细胞周期蛋白家族新成员：CCNG1和CCNH可能是细胞周期调控网络中的新的中心蛋白。新的相互作用增强了细胞周期调控两大功能子网络：Cyclin-CDK-CKI动力系统和细胞周期检查点系统内部和之间的联系,例如：GADD45A-RCHY1-TP53和CCNB1-MAGED1-PLK1,提示二个子网络功能上的密切相关,这些相互作用蛋白可能形成细胞周期调控网络中新的功能复合体。新的相互作用还揭示了细胞周期检查点网络和NF-κB (nuclear factorκB)信号通路网络的新的交联,例如：PLK1-TANK, BRCA1-JUP-IKBE等,将两个响应DNA损伤信号的下游分支通路联系了起来,发现了二者之间新的功能联系。结合相互作用实验验证结果,我们针对高可信度的相互作用进行了深入的文献挖掘。我们发现通过GADD45A与凋亡蛋白Bax相互作用蛋白SH3GLB1的相互作用,提示了GADD45A诱导凋亡的新的分子机制；通过CDKN1A与有丝分裂原诱导的GTP酶激活蛋白SIPA1的相互作用提示了SIPA1参与细胞周期负调控的新的功能角色；PLK1与NF-κB信号通路接头蛋白TANK的相互作用介导了细胞周期和NF-κB信号通路之间潜在的交联。我们的工作为进一步的相互作用功能研究提供了重要线索。
最后,我们选取高可信度相互作用—细胞周期激酶PLK1 (polo-like kinase 1)与NF-κB信号通路接头蛋白TANK (TRAF family member associated NF-κB activator)进行了深入的功能研究。细胞周期调控激酶参与NF-κB信号通路的调节,是重要生物学过程细胞周期和NF-κB信号通路的新的功能联系。实验结果表明支架分子TANK通过募集PLK1与IκB激酶(IκB kinases, IKKs)复合体的调控亚基IKKγ即NEMO (NF-κB essential modulator)结合,并通过抑制NEMO的泛素化来负调控NF-κB关键调控激酶IKKs复合体的活性。
综上所述,本研究采用酵母双杂交方法,对细胞周期调控网络进行了初步研究,发现了多个细胞周期动力系统和细胞周期检查点系统之间的新的分子关联,为研究细胞周期调控的精细分子机制提供了素材。新发现的多个相互作用还将细胞周期检查点调控系统和NF-κB信号通路联系了起来,揭示了基本生物学过程与重要信号通路之间的功能相关性。细胞周期调控蛋白参与先天免疫相关信号通路的调控是进一步功能研究的重要提示。新发现的细胞周期蛋白与凋亡途径蛋白的相互作用(例如：GADD45A-SH3GLB1),与癌症等疾病相关蛋白的相互作用(例如：CCNB1-MAGED1),为从分子水平上探究细胞周期调控机制及与疾病的关系提供了线索。在我们构建的细胞周期调控蛋白质的相互作用网络中,多个网络核心分子及其作用分子都有望成为药物筛选的靶点,对未来的药物开发可能具有重要意义。
Cell cycle is a basic process and important character of the life. To understand its function and regulation mechanisms is a key field of the life science, and is critical to many applications in biotechnology and medicine. Most proteins exert their functions through protein-protein interaction. The research of the network of cell cycle proteins, might uncover the underlying molecular mechanisms involved. Large-scale screening of protein-protein interactions of cell cycle-associated proteins provides a comprehensive analysis of cell cycle and is also an important part of the Chinese Human Liver Proteome Project (CNHLPP).
In this dissertation, we successfully cloned 25 genes that have important roles in cell cycle regulation which include cyclins, cyclin dependent kinases (CDKs), cyclin dependent kinase inhibitors (CKIs), cell cycle restriction point regulators, cell cycle check point regulators, kinds of enzymes and growth factors/receptors. Twenty-two of them were screened against human adult liver cDNA library with ProQuestTM yeast two-hybrid system. About 604 candidate colonies were identified and 336 preys (AD-Y) identities were determined with interaction sequence tags (ISTs) by sequencing. Finally,90 different protein interaction pairs including 101 proteins were obtained, among which 8 interactions were reported. To estimate the technical false positive rate, all interactions were verified by retransformation in yeast cells. The total recovery rate for the interactions is 49%. To evaluate the accuracy of the Y2H database, we randomly selected Y2H interaction pairs with positive result in retransformation assay, and tested them by co-immunoprecipitation (CoIP), chip-CoIP (a co-immunoprecipitation assay based on protein chips technology) or GST-pull down assays.13 out of 16 interaction pairs were validated by at least one of the three different assays. The positive rate is 81%, which is higher than those reported in other human interactome research.
Several independent bioinformatics methods were used to evaluate the potential biological relevance of the identified interactions. By searching the PubMed, HPRD and STRING databases for co-occurrence of the corresponding gene symbols, we found that 16 interactions (18%) of their partners show linkage of the corresponding gene symbols. According to the mouse genome informatics (MGI) data, we showed that 16 interactions of their partners show similar gene knockout phenotype. Our interactions were also investigated by GO annotation. We found that 24 interactions share same cellular component,17 interactions share same molecular function, and 21 interactions participate in the same biological process. Moreover, we found 18 interactions contain interacting domains in the interacting domain analysis. These results show that our interactions data are of high confidence. With experimental and bioinformatics information, we established a scoring system for confidence evaluation. Each interaction was scored with this system and then grouped into three confidence sets according to their scores, resulting in 34 interactions (38%) with high confidence, 38 interactions (42%) with medium and 18 (20%) with low confidence.
Subsequently, we presented the interactions in visible network graphs with Osprey network visualization system. Through integrating LCI (Literature Curated Interactions) data, two main sub-networks were found in cell cycle regulation system which includes Cyclin-CDK-CKI regulation system and cell cycle check point associated system. The two sub-networks were connected by interactions such as GADD45A-RCHY1-TP53 and CCNB 1-MAGED 1-PLK1 obtained from our study. We also found the interactions among cell cycle regulated proteins and NF-κB signaling pathway, suggesting new crosstalks between cell cycle process and NF-κB signaling pathway. The results indicate that our dataset is valuable complement to the existent network. By integrating the experimental and various confidence evaluation information, as well as literature research, we performed a comprehensive analysis of the biological relevance of some interactions.
Then, we confirmed that cell cycle regulated kinase PLK1 (polo-like kinase 1) interacts with adaptor molecule TANK (TRAF family member associated NF-κB activator) in vitro and in vivo. In addition, overexpression of PLK1 inhibits NF-κB activation induced by upstream stimulators including cytokines, activators, but not by p65, and this negative regulation depend on the interaction with TANK. TANK is a TRAF interacting protein that may negatively regulates NF-κB activation. But the underlying mechanism remains unclear. Our results showed that TANK recruited PLK1 binding to NEMO (NF-κB essential modulator), the mediator subunit of IKKs (IκB kinases) complex, and negatively regulates TNF-induced IKK activation through the inhibiting effect of PLK1 on NEMO ubiquitination.
In conclusion, this dissertation presents a primary interaction network of cell cycle regulation associated proteins by Y2H library screening, which might be meaningful to understand the regulation mechanisms of cell cycle and provides potential clues to functional research.
引文
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