建立癌症擾動蛋白質的交互作用細胞凋零網路於藥物標靶發現

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研究生：	褚亮翬 Liang-Hui Chu
論文名稱：	建立癌症擾動蛋白質的交互作用細胞凋零網路於藥物標靶發現 Construction of Cancer-Perturbed Protein-Protein Interaction Network of Apoptosis for Drug Target Discovery
指導教授：	陳博現 Bor-Sen Chen
口試委員:
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 電機工程學系 Department of Electrical Engineering
論文出版年：	2007
畢業學年度：	95
語文別：	英文
中文關鍵詞：	蛋白質網路、細胞凋亡
外文關鍵詞：	protein-protein interaction, apoptosis
相關次數：	點閱：2 下載：0
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背景
癌症主要是由於oncogene和tumor suppressor gene的突變及失調導致一連串下游訊號傳遞的改變。因此，在癌細胞監控蛋白質的交互作用，並與正常細胞比較，能夠幫助我們更瞭解正常細胞變成癌細胞的過程。
結果
藉由人類yeast-two-hybrid的實驗和Himap, HPRD, BIND等資料庫，我們可以先建立粗略的網路(rough protein-protein interaction network)。由於有許多錯誤存在於這些資料庫，我們使用非線性隨機模型，maximum likelihood 參數估測，和Akaike Information Criteria (AIC) 去更進一步的刪除其中的假的交互作用。藉由比較癌細胞和正常細胞的機制，我們可以更進一步的瞭解細胞凋亡(apoptosis)，並找到可能的癌症藥物標靶(cancer drug target)。而我們的方法也可應用在動態的交互網路，例如在caspase蛋白質網路中尋找其中心(hub)。
結論
我們提出的動態模型以及AIC的方法，結合了網路生物學與系統生物學，不但成功的建立癌症的蛋白質網路，亦在細胞凋零的路徑中找到可能的藥物標靶，同時這方法也可應用在其他的蛋白質網路，例如細胞週期。

第一章簡介

由於基因體以及蛋白質體的研究，系統生物學能夠幫助解開癌細胞的蛋白質網路。癌症的起因包括多個基因的突變，以及蛋白質網路的改變。在許多資料庫與實驗的幫助下，藉由我們所發展出來的方法，能夠比較蛋白質網路在癌細胞和正常細胞的不同，例如細胞凋亡。逃脫細胞凋亡是癌細胞生長的一個重要的機制，而比較蛋白質網路在癌細胞與正常細胞的不同，以系統的方式尋找適合的藥物標靶，正是系統生物學在癌症標靶藥物的重要應用。
第二章結果與討論

我們以參與細胞凋亡的蛋白質為中心，往外擴大蛋白質網路，並藉由許多網路資料庫我們可先建立粗略的網路。藉由基因晶片的實驗，我們可以建立較為精確的癌細胞與正常細胞的蛋白質網路比較其不同，解釋細胞凋亡的生物意義，並找到可能的藥物標靶和他文獻比較。而我們也利用我們的方法成功的找到動態的蛋白質網路中心點。

第三章結論

我們不但成功的建立凋亡的蛋白質網路，比較癌細胞和正常細胞的細胞不同，以系統化的方式解釋細胞凋亡，並找到可能的癌症藥物標靶。動態的蛋白質網路的中心點也藉由我們的方法得到印證。而這正是系統生物學於癌症藥物設計上的重要應用。

第四章方法

藉由許多大規模的生物實驗和網站，我們可先建立粗略的網路。利用非線性隨機模型代入實驗數據，利用系統估測的方法估參數，並利用統計方法Akaike information criterion (AIC)得到龐大的交互作用資料庫，並藉由邏輯AND的關係決定是否保留此交互作用，進而得到癌細胞和正常細胞的蛋白質網路。

Abstract
Background
Cancer is known to occur due to mutations of oncogenes or tumor suppressor genes, which alter a series of downstream signal transduction pathways at molecular level. Therefore, inspecting interactive behaviors of proteins in cancer cells and comparing them with those in normal cells to obtain cancer-perturbed protein network can shed light on how a normal cell transforms into a cancer cell.
Results
Rough protein-protein interaction networks of apoptosis in cancer and normal cells are constructed according to human yeast-two-hybrid datasets and websites. Owing to high false positive rates in these large-scale interactome, the nonlinear stochastic model, maximum likelihood parameter estimation and Akaike Information Criteria (AIC) are employed to truncate fake protein-protein interactions of rough protein interaction networks. By comparing protein-protein interaction networks of apoptosis between HeLa cancer cells and normal cells, we can obtain cancer-perturbed networks and gain insight into the mechanism of apoptotic network in human cancer, which helps discovery of cancer drug targets. Furthermore, static and dynamic hubs in protein-protein interaction networks in caspase activation can be derived, too.

Abstract………………………………………………………………………………..i
Acknowledgements………………………………………………………….............iii
Contents……………………………………………………………………...............iv
Chapter 1 Introduction………………………………………………………………1
Chapter 2 Results and Discussion…………………………………………………...5
2.1 Construction of cancer-perturbed protein-protein interaction network in apoptosis……5
2.2 Cancer-perturbed apoptosis mechanism at systems level……………………………….7
  2.2.1 Extrinsic pathway, intrinsic pathway and crosstalk......…………………………….8
  2.2.2 Caspases family and caspases regulators………………………………..………….9
  2.2.3 Regulation of apoptosis at systems level…………………………………………10
  2.2.4 Apoptosis and cell cycle regulation………………………………………………10
2.3 Drug targets identification using the proposed cancer-perturbed protein……………..11
2.3.1 Intrinsic pathway: BCL2, BAX, BCL2L1, and CYCS……………………………13
2.3.2 Extrinsic pathway and crosstalk: TNF, TNFRSF6, and BID……………………14
2.3.3 Common pathway: CASP3 and CASP9………………………………………….14
2.3.4 Apoptosis regulators: TP53, MYC, CFLAR, and EGFR…………………………15
2.3.5 Stress-induced signaling and others: MAPK1, MAPK3, CDKN1A and CCND1..15
2.3.6 Prediction of cancer drug targets by degree of perturbation………………………16
2.4 Caspases activation through static and dynamic hubs…………………………….    …...17
Chapter 3 Conclusions……………………………………………………………...19
Chapter 4 Methods………………………………………………………………….20
4.1 Constructing rough protein-protein interaction network………………………………20
4.2 Selecting and processing experimental data…………………………………...............21
4.3 Nonlinear stochastic interaction model of rough protein interaction networks…..........22
4.4 Identification of interactive abilities of rough protein-protein interaction network…...24
4.5 Truncation of rough protein-protein interaction networks……………………………..27
Bibliography………………………………………………………………………...30
List of Table
Table 1 - Protein targets ranked by sum of perturbation degree  8 in Figures 2A and 2B ………………………………………………………………………..……….34
List of Figures
Figure 1 - Global protein-protein interactions of apoptosis between cancer and normal cells…………………………………………………………………….....35
Figure 2 - Cancer-perturbed apoptotic protein-protein interaction networks………...36
Figure 3 - Flow chart for drug target identification from cancer-perturbed protein-protein interaction network via microarray data………................37
Figure 4 - Dynamic protein-protein interactions in caspase formation………………38
Figure 5 - Graphical representation of the individual protein interactions and the cooperative protein interactions…………………………..……………...39

List of Supplementary Tables
Supplementary Table 1
Global apoptotic protein-protein interactions in cancer and normal cells
Supplementary Table 2
Gain-of-function proteins in Figure 2A ranked by degree of perturbation
Supplementary Table 3
Loss-of-function proteins in Figure 2B ranked by degree of perturbation
Supplementary Table 4
Dynamic caspases protein-protein interactions in cancer cells
Supplementary Table 5
Dynamic caspases protein-protein interactions in normal cells

                                

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