A Protein Interaction Network Of Cell Cycle-related Proteins And The Mechanisms Of Scaffold Protein Tank/i-traf Inhibits NF-κB Activation By Recruiting Polo-like Kinase 1

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.