TNF-α刺激下的蛋白质相互作用与细胞耐受响应的定量蛋白质组学研究
摘要
本论文主要以基于质谱的细胞培养的稳定同位素氨基酸标记技术作为蛋白质组学定量研究的技术平台,系统研究了肿瘤坏死因子(TNF-α)刺激下14-3-3epsilon相互作用复合物的变化情况；以及细胞对于TNF-α耐受性响应的全谱研究和该特定条件下与14-3-3 epsilon相互作用蛋白的变化,对于研究癌逃逸机制有深远的意义。同时,我们还应用了iTRAQ标记技术研究了TNF-a不同时间点刺激下的14-3-3epsilon复合物的动态响应过程。
论文的第一章综述了近年来蛋白质组学在方法、技术以及应用上的研究成果和进展,同时阐述了蛋白质相互作用技术的进展,特别强调了目前应用比较广泛的基于稳定同位素氨基酸标记技术的定量蛋白质组学和蛋白相互作用组学研究。总结了各种技术的优缺点及应用领域,并由此引出了本论文的研究背景,阐述了本论文的研究目的和意义。
本论文的主要研究工作分为三章,第一章涉及了TNF-α诱导的／可诱导的14-3-3ε相互作用蛋白,以及特定复合物对于NF-κB活性调节的深入研究；第二章全面研究了细胞对于TNF-α刺激的耐受响应变化以及在此过程中,与14-3-3ε相互作用蛋白的变化情况；第三章研究了14-3-3 epsilon复合物在TNF-α不同时间点刺激下的动态变化。下面对各章内容进行详细介绍：
1.TNF-α引起的14-3-3 epsilon复合物变化及其关键相互作用蛋白对NF-κB活性的选择性调节
炎症是由NF-κB严格调控的,未加抑制的NF-κB的过度活化会导致细胞因子的生产过剩,由此引发包括癌症在内的各种致病后果。肿瘤坏死因子(tumor necrosis factor-α, TNF-α)作为一个促炎症因子,可用于探索在NF-κB调节的炎症响应过程中TNF-α诱导的但是未知的,却起重要作用的通路对于NF-κB活性调节的可能机制。14-3-3蛋白是一种高度保守的酸性可溶性蛋白,现有的研究表明：14-3-3蛋白通过调节细胞内多个信号转导通路而参与多种细胞生命活动的调控,涉及炎症响应、有丝分裂、细胞生存、细胞周期、凋亡、生长、增殖、DNA复制、DNA损伤、信号转导等。鉴于14-3-3家族蛋白的多功能特性,且最近有报道发现14-3-3家族蛋白出现在TNF-α/NF-κB通路网络中,我们应用了双标签定量蛋白质组学的策略,以稳定表达含有flag标签的14-3-3epsilon的大量293T细胞为研究对象,采用免疫共沉淀技术,首次研究了TNF-α可诱导的14-3-3ε相互作用蛋白,而14-3-3ε是14-3-3蛋白家族中特征性最少的亚型。表位抗原标签技术可有效提高免疫共沉淀的效率,增加复合物的鉴定几率；而基于质谱的稳定同位素氨基酸标记技术可有效去除非特异性的背景蛋白。基于这种双标记策略,我们鉴定到了包括“诱饵”蛋白在内的55个TNF-α可诱导的14-3-3ε相互作用蛋白。其中有17个是数据库己知的14-3-3家族成员或14-3-3ε的相互作用蛋白或复合物组成部分,如CDC37、Hsp90、RuvBL2等,而TAK1、PPMIB、TBK1等均是首次被报道,并对所鉴定的蛋白进行了免疫印迹(IB)验证和生物学功能分类分析。我们选择了已知的相互作用蛋白如Hsp90、RuvBL2,以及未知的DDX21、PPM1B、TAK1、TBK1等进行了IB验证,结果均与质谱结果一致,充分证明了基于质谱的双标记策略对于研究蛋白一蛋白相互作用的可行性与可靠性。并且我们的研究首次发现了TNF-α的刺激增强了14-3-3ε与MAPK信号通路中的某些关键组份的相互作用,其中包括转化生长因子一B活化激酶-1(TAK1)及其相互作用蛋白——蛋白磷酸酶2Cβ(PPMIB),而MAPK正是NF-κB的最直接的上游信号通路。通过共聚焦激光扫描,我们观察到了TNF-α诱导的14-3-3￡与TAK1和PPMIB的共定位,并且在其它细胞类型中同样存在这些TNF-α诱导的相互作用。此外,我们还发现在细胞响应TNF-α刺激的全过程中,14-3-3ε与这两个蛋白的相互作用是动态变化的,且与NF-κB依赖于时间过程的活性变化紧密相关。这暗示了这些14-3-3￡相互作用是集合TNF-α信号以调节NF-κB活性的关键点。之后我们假设了14-3-3ε如何通过协调与TAK1和PPM1B的动态相互作用来分别调节TNF-α诱导的NF-K B活性变化。利用生物信息学工具,应用我们蛋白质组学方法所鉴定到的大部分14-3-3￡相互作用蛋白,构建了相互作用网络图。从中揭示出14-3-3ε协调MAPK信号通路与其它分子通路／生物进程的交互响应(cross talk),其中主要包括蛋白新陈代谢与合成、DNA修复以及细胞周期调控等,其中可以系统地找寻用于干预治疗的药物标靶。
2.定量蛋白质组学解析细胞对TNF-α持久刺激的耐受响应以及在此过程中14-3-3 epsilon复合物的变化
炎症响应是对于感染和组织损伤的复杂响应过程。而肿瘤坏死因子(tumor necrosis factor, TNF)是迄今发现的直接杀伤恶性肿瘤作用最强的生物活性因子,可以通过不同的信号途径导致细胞的生长或凋亡。由于炎症响应可引起组织生理学的明显变化,因此其失调会导致多种病理学情况。炎症响应的范围和持续时间是由多种机制调控的,正是由于这些机制的响应,因此会出现-种耐受现象：细胞或生物体对于反复的或持久的刺激出现的暂时不响应状态。借助于SILAC三标标记定量蛋白质组学技术,我们系统地研究了293T细胞对于TNF-α重复持续刺激的响应情况。在TNF-α的重复持续刺激的全过程中,我们鉴定到了706个有变化的蛋白,这些蛋白涉及了新陈代谢、细胞生物合成、基因表达、转运、程序细胞死亡、催化活性调控、细胞周期、应激反应、DNA损伤刺激响应、细胞功能负调控、细胞死亡负调控等生物过程。对TNF-a的重复持续刺激有耐受响应的蛋白有225个,主要涉及新陈代谢、细胞生物合成、基因表达、转运、有丝分裂细胞周期、染色体组成、细胞功能负调控以及有丝分裂细胞周期中泛素蛋白连接酶活性调控等。而对TNF-a的重复持续刺激始终有响应的蛋白有159个,参与了转运、蛋白定位、细胞分解代谢过程、新陈代谢、DNA损伤刺激响应等。通过耐受蛋白和不耐受蛋白的功能涉及异同可发现,细胞为了在TNF-α的重复持续刺激下存活下来,调动耐受蛋白进行基因表达、生物合成、细胞周期等功能,而不耐受的蛋白对DNA损伤刺激的响应,参与分解代谢等生物过程,由此可见,两组蛋白可能通过相互协调而使细胞避免凋亡,这也为我们深入研究癌细胞的逃逸机制提供了有效的模型和研究策略。
由于14-3-3蛋白的多功能性,且参与炎症响应、细胞周期、有丝分裂、凋亡等过程。因此采用稳定同位素氨基酸标记和抗原表位标签的双标记技术,以稳定表达flag-14-3-3ε的293T细胞为研究对象,研究在TNF-α重复持续刺激细胞的过程中,与“诱饵”蛋白14-3-3ε相互作用蛋白的变化情况。共鉴定到295个相互作用蛋白,这些蛋白主要涉及了细胞新陈代谢、基因表达、细胞生物合成、复合物聚集、染色体组成、应激响应、DNA损伤刺激响应、细胞周期等。与全谱数据相结合,可发现与14-3-3￡相互作用的蛋白所涉及的生物学过程很多与全谱中蛋白响应过程相同,暗示了14-3-3ε的多功能性及其相互作用蛋白在响应TNF-α的重复持续刺激过程中所起的关键作用。
3.TNF-α不同时间点刺激下的14-3-3 epsilon相互作用蛋白的动态变化
以往定量标记技术的局限在于只有两到三个标签,这导致了当有多于两个的样品需要比较时,需要进行多次实验才可完成。而细胞生物过程是一个动态变化的过程,对细胞某一时间点的蛋白相互作用研究只能说明该特定时间蛋白复合物的变化情况,这只是细胞某一生物过程中的一小部分,虽然能揭示一定的生物现象却无法得到全面的认识。因此我们应用iTRAQ标记技术,以稳定表达flag-14-3-3ε的293T细胞为研究对象,研究在TNF-α不同时间点刺激下,与“诱饵”蛋白14-3-3ε相互作用蛋白的动态变化情况。在此过程中,发现参与细胞死亡相关调控和有丝分裂细胞周期的蛋白均占有一定的比例,这暗示14-3-3ε很有可能通过调节与这两类蛋白的相互作用变化,而对细胞是生存还是走向死亡起着关键的作用。
The thesis mainly focused on the quantitative proteomics study by using amino acid-coded tagging in cell culture (AACT, also known as SILAC) strategy with the help of the mass spectrum. Based on this platform, three systematical researches were studied as follows:1) Investigated the TNF-αstimulated 14-3-3εinteracting partners changes; 2) Studied the global profile of cells resistance to TNF-a prolonged stimulation, also the 14-3-3εcomplex changes under this situation, and through this study it would provide more profound meaning for exploring cancer escape mechanism; Meanwhile,3) Applied iTRAQ labeling strategy to study the dynamic changes of 14-3-3εinteracting partners at different time points under the stimulation of TNF-α.
In Chapter 1, recent research results and developments of proteomics and protein-protein interaction in strategy, technology and application fields were reviewed, especially emphasized currently broadly applied amino acid-coded mass tagging (AACT/SILAC) based quantitative proteomics and interactomics approaches. Summarized the advantages/weaknesses of each technology, thus lead to the background of this thesis and illustrated the purpose and significance of our study.
The research work of this dissertation was composed of three parts. The first part involved in TNF-αintroduced/introducible 14-3-3εinteracting partners, and in-depth study of particular complex in NF-κB activity regulation. The second part comprehensively studied cell tolerant response to TNF-αprolonged stimulation, and the 14-3-3εinteracting complex changes during this process. The third part studied the dynamic changes of 14-3-3εcomplex at different time points of TNF-αstimulation. The details were described in the fallowing:
1.14-3-3 epsilon dynamically interacts with key components of MAPK signal module for selective modulation of the TNF-α-induced time course-dependent NF-κB activity
Inflammation is tightly regulated by NF-κB if left unchecked excessive NF-κB activation for cytokine overproduction can lead to various pathogenic consequences including carcinogenesis. A proinflammatory cytokine, tumor necrosis factor-α(TNF-a), can be used to explore possible mechanisms whereby unknown functional pathways modulate the NF-κB activity for regulating TNF-α-induced inflammation. 14-3-3 proteins are highly conserved acidic soluble proteins, existing studies have shown that 14-3-3 proteins involve in the regulation of variety cellular biological process by mediating multiple intracellular signal transduction pathways, including inflammatory responses, mitogenic and cell survival signaling, cell cycle, apoptosis, growth, proliferation, DNA replication, DNA damage, signal transduction, etc. Given the multi-functional nature of 14-3-3 family proteins and recent finding of their presence in the TNF-α/NF-κB pathway network, we used a dual-tagging quantitative proteomic method, and stably expressed flag-tagged 14-3-3ε293T cells as the study object, using co-immunoprecipitation technology, to first profile the TNF-αinducible interacting partners of 14-3-3ε, the least characterized 14-3-3 isomer in the family. The epitope tag approach can increase the efficiency of co-immunoprecipitation and therefore enhance the possibilities of protein complex being identified; and the quantitative strategy of MS-based AACT/SILAC can effectively distinguish the specific protein partners from the non-specific backgrounds. Based on this dual-tagging strategy, we identified 55 TNF-αinducible 14-3-3εinteracting partners including the bait protein,17 proteins among them were the previously known interactors or the complex components with either 14-3-3 family members or 14-3-3ε, such as CDC37, Hsp90, RuvBL2, etc, new interactors such as TAK1, PPM1B, TBK1, etc were first reported by our study. We did immunoblot to validate the identified proteins and classified their biological process and functions. We chose the already known interactors Hsp90, RuvBL2, and also unknown ones such as DDX21, PPM1B, TAK1, TBK1 to do western blot, all results were consistent with the Mass data, fully proved the feasibility and reliability of mass-based dual tagging strategy in protein protein interaction study. And for the first time we found that TNF-αstimulation enhances the interactions between 14-3-3εand some key components in the MAPK signal module which is located at the immediate upstream of NF-κB, including transforming growth factor-beta activated kinase-1 (TAK1) and its interacting protein, protein phosphatase 2Cβ(PPM1B). By using confocal laser scanning we observed the TNF-α-induced co-localizations among 14-3-3ε, TAK1, and PPM1B and these interactions were also TNF-a-inducible in different cell types. Further, we found that during the full course of cellular response to TNF-αthe interactions between 14-3-3εand these two proteins were dynamic and were closely correlated with the time course-dependent changes in NF-κB activity, suggesting these 14-3-3εinteractions are the critical points of convergence for TNF-a signaling for modulating NF-κB activity. We then postulated a mechanistic view describing how 14-3-3εcoordinates its dynamic interactions with TAK1 and PPM1B for differentially modulating TNF-α-induced changes in NF-κB activity. By using bioinformatics tools we constructed the network involving most of 14-3-3εinteracting proteins identified in our proteomic study. We revealed that 14-3-3εcoordinate the cross talks between the MAPK signal module and other molecular pathways/biological processes primarily including protein metabolism and synthesis, DNA repair, and cell cycle regulation where pharmacological targets for therapeutic intervention could be systematically located.
2. Quantitative study of cell tolerance to TNF-αprolonged stimulation and the 14-3-3εinteracting partners changes during this process
Inflammation is a complex response to infection and tissue injury. By far, TNF that was found to have the strongest activity in directly killing tumor cells, can lead cells to grow or apoptosis through different signal pathways. Because the inflammatory response causes marked changes in tissue physiology, dysregulated inflammation can lead to a variety of pathological conditions. Multiple mechanisms control the extent and duration time of inflammation response. Because of the regulations of these mechanisms, that contributes to the phenomenon 'tolerance' :the transient unresponsiveness of cells or organisms to repeated or prolonged stimulation. With SILAC triple-labeling quantitative proteomics approach, we systematically studied the response of 293T cells to TNF-a prolonged stimulation. During the whole process of TNF-a prolonged stimulation, we finally identified 701 proteins changed. These proteins involved in metabolic process, cellular biosynthetic process, gene expression, transport, programmed cell death, regulation of catalytic activity, cell cycle, cellular response to stress, response to DNA damage stimulus, negative regulation of cellular function, negative regulation of cell death and other biological processes.225 proteins were resistant to TNF-a prolonged stimulation, mainly involved in metabolic process, cellular biosynthetic process, gene expression, transport, mitotic cell cycle, chromosome organization, negative regulation of cellular function, as well as the regulation of ubiquitin-protein ligase activity during mitotic cell cycle and so on. While there were 159 proteins continuing responsed to TNF-a prolonged stimulation, participating in transport, protein localization, cellular catabolic process, metabolic process, response to DNA damage stimulus, etc. Comparing the differences between tolerant proteins and non-tolerant proteins, we found that in order to survive from TNF-a prolonged stimulation, cell mobilized tolerant proteins to progress gene expression, biosynthesis, cell cycle and other similar functions, while the non-tolerant proteins involved in response to DNA damage stimulus and cellular catabolic process. All of these showed that these two groups of proteins might coordinate with each other to avoid apoptosis, which provides us an effective model and strategy to in-depth study the escape mechanism of cancer cells.
Because of the multifunction of 14-3-3 proteins, they involve in many processes such as inflammation response, cell cycle, mitosis, apoptosis, etc. Therefore we applied mass spectrum-based in vivo dual-tagging strategy, using stably expressed flag-tagged 14-3-3ε293T cells to study 14-3-3εinteracting proteins' changes during TNF-αprolonged stimulation. We identified 295 interacting proteins, they mainly involved in cellular metabolic process, gene expression, cellular biosynthetic process, complex assembly, chromosome organization, cellular response to stress, response to DNA damage stimulus, cell cycle, etc. Combined with the global profile data, we found that there were many overlaps between 14-3-3εinteracting proteins and whole cell proteins responsed to TNF-αprolonged stimulation in biological process. This suggests the multifunctional character of 14-3-3εand the crucial roles of 14-3-3εcomplex during TNF-αprolonged stimulation.
3. The dynamic changes of 14-3-3εinteracting partners during different time points of TNF-αstimulation
The limitation of the past quantitative strategies is they only have two or three tags, which means you have to do more experiments to compare more than two samples. Whereas cellular biological process is dynamic, study protein interaction at a time point only shows the complex changes at that particular time, it's just a small part of a cellular process, although it will reveal some phenomenon we still can't get the whole picture. So we applied iTRAQ labeling strategy, using stably expressed flag-14-3-3ε293T cells, studied the dynamic changes of 14-3-3εinteracting partners during different time points of TNF-αstimulation. We found that proteins involved in regulation of cell death or mitotic cell cycle have a position in the changed proteins, indicating that 14-3-3εprobably plays key roles in cell surviving/dying through regulating its interactions with these two kinds of proteins during this process.
论文的第一章综述了近年来蛋白质组学在方法、技术以及应用上的研究成果和进展,同时阐述了蛋白质相互作用技术的进展,特别强调了目前应用比较广泛的基于稳定同位素氨基酸标记技术的定量蛋白质组学和蛋白相互作用组学研究。总结了各种技术的优缺点及应用领域,并由此引出了本论文的研究背景,阐述了本论文的研究目的和意义。
本论文的主要研究工作分为三章,第一章涉及了TNF-α诱导的／可诱导的14-3-3ε相互作用蛋白,以及特定复合物对于NF-κB活性调节的深入研究；第二章全面研究了细胞对于TNF-α刺激的耐受响应变化以及在此过程中,与14-3-3ε相互作用蛋白的变化情况；第三章研究了14-3-3 epsilon复合物在TNF-α不同时间点刺激下的动态变化。下面对各章内容进行详细介绍：
1.TNF-α引起的14-3-3 epsilon复合物变化及其关键相互作用蛋白对NF-κB活性的选择性调节
炎症是由NF-κB严格调控的,未加抑制的NF-κB的过度活化会导致细胞因子的生产过剩,由此引发包括癌症在内的各种致病后果。肿瘤坏死因子(tumor necrosis factor-α, TNF-α)作为一个促炎症因子,可用于探索在NF-κB调节的炎症响应过程中TNF-α诱导的但是未知的,却起重要作用的通路对于NF-κB活性调节的可能机制。14-3-3蛋白是一种高度保守的酸性可溶性蛋白,现有的研究表明：14-3-3蛋白通过调节细胞内多个信号转导通路而参与多种细胞生命活动的调控,涉及炎症响应、有丝分裂、细胞生存、细胞周期、凋亡、生长、增殖、DNA复制、DNA损伤、信号转导等。鉴于14-3-3家族蛋白的多功能特性,且最近有报道发现14-3-3家族蛋白出现在TNF-α/NF-κB通路网络中,我们应用了双标签定量蛋白质组学的策略,以稳定表达含有flag标签的14-3-3epsilon的大量293T细胞为研究对象,采用免疫共沉淀技术,首次研究了TNF-α可诱导的14-3-3ε相互作用蛋白,而14-3-3ε是14-3-3蛋白家族中特征性最少的亚型。表位抗原标签技术可有效提高免疫共沉淀的效率,增加复合物的鉴定几率；而基于质谱的稳定同位素氨基酸标记技术可有效去除非特异性的背景蛋白。基于这种双标记策略,我们鉴定到了包括“诱饵”蛋白在内的55个TNF-α可诱导的14-3-3ε相互作用蛋白。其中有17个是数据库己知的14-3-3家族成员或14-3-3ε的相互作用蛋白或复合物组成部分,如CDC37、Hsp90、RuvBL2等,而TAK1、PPMIB、TBK1等均是首次被报道,并对所鉴定的蛋白进行了免疫印迹(IB)验证和生物学功能分类分析。我们选择了已知的相互作用蛋白如Hsp90、RuvBL2,以及未知的DDX21、PPM1B、TAK1、TBK1等进行了IB验证,结果均与质谱结果一致,充分证明了基于质谱的双标记策略对于研究蛋白一蛋白相互作用的可行性与可靠性。并且我们的研究首次发现了TNF-α的刺激增强了14-3-3ε与MAPK信号通路中的某些关键组份的相互作用,其中包括转化生长因子一B活化激酶-1(TAK1)及其相互作用蛋白——蛋白磷酸酶2Cβ(PPMIB),而MAPK正是NF-κB的最直接的上游信号通路。通过共聚焦激光扫描,我们观察到了TNF-α诱导的14-3-3￡与TAK1和PPMIB的共定位,并且在其它细胞类型中同样存在这些TNF-α诱导的相互作用。此外,我们还发现在细胞响应TNF-α刺激的全过程中,14-3-3ε与这两个蛋白的相互作用是动态变化的,且与NF-κB依赖于时间过程的活性变化紧密相关。这暗示了这些14-3-3￡相互作用是集合TNF-α信号以调节NF-κB活性的关键点。之后我们假设了14-3-3ε如何通过协调与TAK1和PPM1B的动态相互作用来分别调节TNF-α诱导的NF-K B活性变化。利用生物信息学工具,应用我们蛋白质组学方法所鉴定到的大部分14-3-3￡相互作用蛋白,构建了相互作用网络图。从中揭示出14-3-3ε协调MAPK信号通路与其它分子通路／生物进程的交互响应(cross talk),其中主要包括蛋白新陈代谢与合成、DNA修复以及细胞周期调控等,其中可以系统地找寻用于干预治疗的药物标靶。
2.定量蛋白质组学解析细胞对TNF-α持久刺激的耐受响应以及在此过程中14-3-3 epsilon复合物的变化
炎症响应是对于感染和组织损伤的复杂响应过程。而肿瘤坏死因子(tumor necrosis factor, TNF)是迄今发现的直接杀伤恶性肿瘤作用最强的生物活性因子,可以通过不同的信号途径导致细胞的生长或凋亡。由于炎症响应可引起组织生理学的明显变化,因此其失调会导致多种病理学情况。炎症响应的范围和持续时间是由多种机制调控的,正是由于这些机制的响应,因此会出现-种耐受现象：细胞或生物体对于反复的或持久的刺激出现的暂时不响应状态。借助于SILAC三标标记定量蛋白质组学技术,我们系统地研究了293T细胞对于TNF-α重复持续刺激的响应情况。在TNF-α的重复持续刺激的全过程中,我们鉴定到了706个有变化的蛋白,这些蛋白涉及了新陈代谢、细胞生物合成、基因表达、转运、程序细胞死亡、催化活性调控、细胞周期、应激反应、DNA损伤刺激响应、细胞功能负调控、细胞死亡负调控等生物过程。对TNF-a的重复持续刺激有耐受响应的蛋白有225个,主要涉及新陈代谢、细胞生物合成、基因表达、转运、有丝分裂细胞周期、染色体组成、细胞功能负调控以及有丝分裂细胞周期中泛素蛋白连接酶活性调控等。而对TNF-a的重复持续刺激始终有响应的蛋白有159个,参与了转运、蛋白定位、细胞分解代谢过程、新陈代谢、DNA损伤刺激响应等。通过耐受蛋白和不耐受蛋白的功能涉及异同可发现,细胞为了在TNF-α的重复持续刺激下存活下来,调动耐受蛋白进行基因表达、生物合成、细胞周期等功能,而不耐受的蛋白对DNA损伤刺激的响应,参与分解代谢等生物过程,由此可见,两组蛋白可能通过相互协调而使细胞避免凋亡,这也为我们深入研究癌细胞的逃逸机制提供了有效的模型和研究策略。
由于14-3-3蛋白的多功能性,且参与炎症响应、细胞周期、有丝分裂、凋亡等过程。因此采用稳定同位素氨基酸标记和抗原表位标签的双标记技术,以稳定表达flag-14-3-3ε的293T细胞为研究对象,研究在TNF-α重复持续刺激细胞的过程中,与“诱饵”蛋白14-3-3ε相互作用蛋白的变化情况。共鉴定到295个相互作用蛋白,这些蛋白主要涉及了细胞新陈代谢、基因表达、细胞生物合成、复合物聚集、染色体组成、应激响应、DNA损伤刺激响应、细胞周期等。与全谱数据相结合,可发现与14-3-3￡相互作用的蛋白所涉及的生物学过程很多与全谱中蛋白响应过程相同,暗示了14-3-3ε的多功能性及其相互作用蛋白在响应TNF-α的重复持续刺激过程中所起的关键作用。
3.TNF-α不同时间点刺激下的14-3-3 epsilon相互作用蛋白的动态变化
以往定量标记技术的局限在于只有两到三个标签,这导致了当有多于两个的样品需要比较时,需要进行多次实验才可完成。而细胞生物过程是一个动态变化的过程,对细胞某一时间点的蛋白相互作用研究只能说明该特定时间蛋白复合物的变化情况,这只是细胞某一生物过程中的一小部分,虽然能揭示一定的生物现象却无法得到全面的认识。因此我们应用iTRAQ标记技术,以稳定表达flag-14-3-3ε的293T细胞为研究对象,研究在TNF-α不同时间点刺激下,与“诱饵”蛋白14-3-3ε相互作用蛋白的动态变化情况。在此过程中,发现参与细胞死亡相关调控和有丝分裂细胞周期的蛋白均占有一定的比例,这暗示14-3-3ε很有可能通过调节与这两类蛋白的相互作用变化,而对细胞是生存还是走向死亡起着关键的作用。
The thesis mainly focused on the quantitative proteomics study by using amino acid-coded tagging in cell culture (AACT, also known as SILAC) strategy with the help of the mass spectrum. Based on this platform, three systematical researches were studied as follows:1) Investigated the TNF-αstimulated 14-3-3εinteracting partners changes; 2) Studied the global profile of cells resistance to TNF-a prolonged stimulation, also the 14-3-3εcomplex changes under this situation, and through this study it would provide more profound meaning for exploring cancer escape mechanism; Meanwhile,3) Applied iTRAQ labeling strategy to study the dynamic changes of 14-3-3εinteracting partners at different time points under the stimulation of TNF-α.
In Chapter 1, recent research results and developments of proteomics and protein-protein interaction in strategy, technology and application fields were reviewed, especially emphasized currently broadly applied amino acid-coded mass tagging (AACT/SILAC) based quantitative proteomics and interactomics approaches. Summarized the advantages/weaknesses of each technology, thus lead to the background of this thesis and illustrated the purpose and significance of our study.
The research work of this dissertation was composed of three parts. The first part involved in TNF-αintroduced/introducible 14-3-3εinteracting partners, and in-depth study of particular complex in NF-κB activity regulation. The second part comprehensively studied cell tolerant response to TNF-αprolonged stimulation, and the 14-3-3εinteracting complex changes during this process. The third part studied the dynamic changes of 14-3-3εcomplex at different time points of TNF-αstimulation. The details were described in the fallowing:
1.14-3-3 epsilon dynamically interacts with key components of MAPK signal module for selective modulation of the TNF-α-induced time course-dependent NF-κB activity
Inflammation is tightly regulated by NF-κB if left unchecked excessive NF-κB activation for cytokine overproduction can lead to various pathogenic consequences including carcinogenesis. A proinflammatory cytokine, tumor necrosis factor-α(TNF-a), can be used to explore possible mechanisms whereby unknown functional pathways modulate the NF-κB activity for regulating TNF-α-induced inflammation. 14-3-3 proteins are highly conserved acidic soluble proteins, existing studies have shown that 14-3-3 proteins involve in the regulation of variety cellular biological process by mediating multiple intracellular signal transduction pathways, including inflammatory responses, mitogenic and cell survival signaling, cell cycle, apoptosis, growth, proliferation, DNA replication, DNA damage, signal transduction, etc. Given the multi-functional nature of 14-3-3 family proteins and recent finding of their presence in the TNF-α/NF-κB pathway network, we used a dual-tagging quantitative proteomic method, and stably expressed flag-tagged 14-3-3ε293T cells as the study object, using co-immunoprecipitation technology, to first profile the TNF-αinducible interacting partners of 14-3-3ε, the least characterized 14-3-3 isomer in the family. The epitope tag approach can increase the efficiency of co-immunoprecipitation and therefore enhance the possibilities of protein complex being identified; and the quantitative strategy of MS-based AACT/SILAC can effectively distinguish the specific protein partners from the non-specific backgrounds. Based on this dual-tagging strategy, we identified 55 TNF-αinducible 14-3-3εinteracting partners including the bait protein,17 proteins among them were the previously known interactors or the complex components with either 14-3-3 family members or 14-3-3ε, such as CDC37, Hsp90, RuvBL2, etc, new interactors such as TAK1, PPM1B, TBK1, etc were first reported by our study. We did immunoblot to validate the identified proteins and classified their biological process and functions. We chose the already known interactors Hsp90, RuvBL2, and also unknown ones such as DDX21, PPM1B, TAK1, TBK1 to do western blot, all results were consistent with the Mass data, fully proved the feasibility and reliability of mass-based dual tagging strategy in protein protein interaction study. And for the first time we found that TNF-αstimulation enhances the interactions between 14-3-3εand some key components in the MAPK signal module which is located at the immediate upstream of NF-κB, including transforming growth factor-beta activated kinase-1 (TAK1) and its interacting protein, protein phosphatase 2Cβ(PPM1B). By using confocal laser scanning we observed the TNF-α-induced co-localizations among 14-3-3ε, TAK1, and PPM1B and these interactions were also TNF-a-inducible in different cell types. Further, we found that during the full course of cellular response to TNF-αthe interactions between 14-3-3εand these two proteins were dynamic and were closely correlated with the time course-dependent changes in NF-κB activity, suggesting these 14-3-3εinteractions are the critical points of convergence for TNF-a signaling for modulating NF-κB activity. We then postulated a mechanistic view describing how 14-3-3εcoordinates its dynamic interactions with TAK1 and PPM1B for differentially modulating TNF-α-induced changes in NF-κB activity. By using bioinformatics tools we constructed the network involving most of 14-3-3εinteracting proteins identified in our proteomic study. We revealed that 14-3-3εcoordinate the cross talks between the MAPK signal module and other molecular pathways/biological processes primarily including protein metabolism and synthesis, DNA repair, and cell cycle regulation where pharmacological targets for therapeutic intervention could be systematically located.
2. Quantitative study of cell tolerance to TNF-αprolonged stimulation and the 14-3-3εinteracting partners changes during this process
Inflammation is a complex response to infection and tissue injury. By far, TNF that was found to have the strongest activity in directly killing tumor cells, can lead cells to grow or apoptosis through different signal pathways. Because the inflammatory response causes marked changes in tissue physiology, dysregulated inflammation can lead to a variety of pathological conditions. Multiple mechanisms control the extent and duration time of inflammation response. Because of the regulations of these mechanisms, that contributes to the phenomenon 'tolerance' :the transient unresponsiveness of cells or organisms to repeated or prolonged stimulation. With SILAC triple-labeling quantitative proteomics approach, we systematically studied the response of 293T cells to TNF-a prolonged stimulation. During the whole process of TNF-a prolonged stimulation, we finally identified 701 proteins changed. These proteins involved in metabolic process, cellular biosynthetic process, gene expression, transport, programmed cell death, regulation of catalytic activity, cell cycle, cellular response to stress, response to DNA damage stimulus, negative regulation of cellular function, negative regulation of cell death and other biological processes.225 proteins were resistant to TNF-a prolonged stimulation, mainly involved in metabolic process, cellular biosynthetic process, gene expression, transport, mitotic cell cycle, chromosome organization, negative regulation of cellular function, as well as the regulation of ubiquitin-protein ligase activity during mitotic cell cycle and so on. While there were 159 proteins continuing responsed to TNF-a prolonged stimulation, participating in transport, protein localization, cellular catabolic process, metabolic process, response to DNA damage stimulus, etc. Comparing the differences between tolerant proteins and non-tolerant proteins, we found that in order to survive from TNF-a prolonged stimulation, cell mobilized tolerant proteins to progress gene expression, biosynthesis, cell cycle and other similar functions, while the non-tolerant proteins involved in response to DNA damage stimulus and cellular catabolic process. All of these showed that these two groups of proteins might coordinate with each other to avoid apoptosis, which provides us an effective model and strategy to in-depth study the escape mechanism of cancer cells.
Because of the multifunction of 14-3-3 proteins, they involve in many processes such as inflammation response, cell cycle, mitosis, apoptosis, etc. Therefore we applied mass spectrum-based in vivo dual-tagging strategy, using stably expressed flag-tagged 14-3-3ε293T cells to study 14-3-3εinteracting proteins' changes during TNF-αprolonged stimulation. We identified 295 interacting proteins, they mainly involved in cellular metabolic process, gene expression, cellular biosynthetic process, complex assembly, chromosome organization, cellular response to stress, response to DNA damage stimulus, cell cycle, etc. Combined with the global profile data, we found that there were many overlaps between 14-3-3εinteracting proteins and whole cell proteins responsed to TNF-αprolonged stimulation in biological process. This suggests the multifunctional character of 14-3-3εand the crucial roles of 14-3-3εcomplex during TNF-αprolonged stimulation.
3. The dynamic changes of 14-3-3εinteracting partners during different time points of TNF-αstimulation
The limitation of the past quantitative strategies is they only have two or three tags, which means you have to do more experiments to compare more than two samples. Whereas cellular biological process is dynamic, study protein interaction at a time point only shows the complex changes at that particular time, it's just a small part of a cellular process, although it will reveal some phenomenon we still can't get the whole picture. So we applied iTRAQ labeling strategy, using stably expressed flag-14-3-3ε293T cells, studied the dynamic changes of 14-3-3εinteracting partners during different time points of TNF-αstimulation. We found that proteins involved in regulation of cell death or mitotic cell cycle have a position in the changed proteins, indicating that 14-3-3εprobably plays key roles in cell surviving/dying through regulating its interactions with these two kinds of proteins during this process.
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