藉由大數據挖掘和全基因組辨別來探究糖尿病前期到一型糖尿病的進展機制

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研究生：	王正一 Wang, Zheng-Yi.
論文名稱：	藉由大數據挖掘和全基因組辨別來探究糖尿病前期到一型糖尿病的進展機制 Investigating the Progressive Mechanism from Prediabetes to Type 1 Diabetes by Big Data Mining and Genome-Wide Microarray Data Identification : Systems Biology Approach
指導教授：	陳博現 Chen, Bor-Sen
口試委員:	詹鴻霖 Chan, Hong-Lin 張晃猷 Chang, Hwan-You 高茂傑 Kao, Mou-Chieh
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：	2018
畢業學年度：	106
語文別：	英文
論文頁數：	52
中文關鍵詞：	糖尿病前期、一型糖尿病、基因調控網路、蛋白質交互作用網路、基因和表觀遺傳網路、進展機制
外文關鍵詞：	Prediabetes, Type_1_diabetes, gener_egulatory_network, protein-proteininteraction_network, genome-widegenetic-and-epigenetic_network, progression_mechanism
相關次數：	點閱：2 下載：0
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在本研究中，我們應用系統生物學方法構建蛋白質-蛋白質相互作用網絡（PPINs）和基因調控網絡（GRNs），然後將其整合成全基因組-遺傳和表觀遺傳網絡（GWGEN）以探討糖尿病前期到T1D的發病機制。利用相應的外周血β細胞微陣列數據建立GWGENs系統識別方法，然後我們通過提出的主要網絡投影（PNP）方法提取核心GWGENs。從糖尿病前期到T1D兩個階段的核心GWGENs被投影為KEGG途徑，以研究涉及前驅糖尿病到T1D的進展機制的重要蛋白質，miRNA，基因，轉錄因子（TF）。
根據糖尿病前期對T1D的早期進展機制，受體IL2RG，SEMA3A和TNFSF1A分別接受細胞因子IL2，SEMA3A，TNF和ROS，將信號轉運到TFRRARA，RELA，ATF2，ERG，ESR1，BRCA，和FOXL1;然後ATF2的乙酰化和磷酸化，ESR1的甲基化和RARA的去甲基化和microRNA MIR198可以激活或抑制靶基因MAP3K8，FNIP1，AR，SOD1，HBA1，NOS3，IL1B，TPO以使早期發展。這些基因具有多種細胞功能，包括炎症，適應性免疫，細胞增殖，凋亡，細胞因子（即TNF，IL32，ROS）的製造。根據糖尿病前期到T1D的後期結果，受體可通過TFs NFE2L1，TCF3，ESR1，GATA和MAFG等途徑接受細胞因子轉運信號以調控靶基因TP53，GLUD1，ACACB，IL32，VCAM1，TNFSF10。這些基因具有多種細胞功能，包括細胞葡萄糖飢餓反應，胰島素分泌，ATP製造，脂質儲存，葡萄糖代謝過程，適應性免疫以引起糖尿病前期進展為T1D。除了這些靶基因的功能障礙之外，表觀遺傳修飾在前驅糖尿病進展至T1D的致病機制中也起重要作用。最後，還開發了藥物數據挖掘方法來設計多分子藥物來恢復這些靶基因的正常功能。

In this study, we apply the system biology method to construct the protein-protein interaction networks (PPINs) and gene-regulatory networks (GRNs), which can then be integrated as genome-wide-genetic and epigenetic networks (GWGENs) for investigating the pathogenic mechanisms of progression from prediabetes to T1D. Using the corresponding microarray data in peripheral blood β-cells to establish the GWGENs by system identification method, we then extract the core GWGENs by a proposed principal network projection (PNP) method. The core GWGENs of two stages progression from prediabetes to T1D are projected to KEGG pathway to investigate important proteins, miRNAs, genes, transcription factors (TFs) involving in the progression mechanism of prediabetes to T1D.
According to the results of early stage progression mechanism of prediabetes to T1D, receptors IL2RG, SEMA3A and TNFSF1A can receive the cytokines IL2, SEMA3A, TNF and ROS, respectively to transport signals to TFs RARA, RELA, ATF2, ERG, ESR1, BRCA, and FOXL1; then the acetylation and phosphorylation of ATF2, methylation of ESR1 and demethylation of RARA and microRNA MIR198 can activate or repress target genes MAP3K8, FNIP1, AR, SOD1, HBA1, NOS3, IL1B, TPO to make an early stage progression. These genes have multiple cellular functions including inflammation, adaptive immunity, cell proliferation, apoptosis, fabrication of cytokines (i.e. TNF, IL32, ROS). According to the results of late stage progression of prediabetes to T1D, receptors can receive the cytokines to transport signals in order to regulate the target genes TP53, GLUD1, ACACB, IL32, VCAM1, TNFSF10 through TFs NFE2L1, TCF3, ESR1, GATA and MAFG. These genes have multiple cellular functions including cellular glucose starvation response, insulin secretion, fabrication of ATP, storage of lipid, glucose metabolic process, adaptive immunity to cause prediabetes progress to T1D. Besides the dysfunction of these target genes, the epigenetic modifications also play an important role in the pathogenic mechanism of prediabetes progress to T1D. Finally, drug data mining method is also developed to design a multiple molecule drugs to restore the normal functions of these target genes.

摘要    i
Abstract    ii
Introduction    1
Materials and Methods    3
2.1    Overview of the construction processes of real GWGENs of 2 stages progression of prediabetes to T1D    3
2.2    Big data mining and data processing for the information of constructing GWGENs of human    4
Results    6
3.1    The core pathways of early stage pathogenic progression of prediabetes to T1D    7
3.2    The core pathways of late stage pathogenic progression of prediabetes to T1D    8
Discussion    11
4.1  The main pathogenic mechanism of early stage of progression of prediabetes to T1D    12
4.2    The main pathogenic mechanism of late stage of progression of prediabetes to T1D    13
4.3    The overall progression mechanism from prediabetes to T1D    18
4.4    The multiple-molecules drug design for prediabetes by drug data mining method    19
Conclusion    21
Supplementary Material    23
6.1    Dynamic models of the candidate GWGEN for identifying early and late stage real GWGEN in the progression of prediabetes to T1D    23
6.2    System identification of candidate GWGEN via genome-wide microarray data    26
6.3    System order detection scheme for pruning the false positives of candidate to obtain the real GWGENs of early and late stage progression of prediabetes to T1D    32
Tables
Table 1. The numbers of edges and nodes of candidate GWGEN and real GWGEN    38
Table 2. Information of the top 5 molecule drugs for early stage progression of prediabetes to T1D    38
Table 3. Information of the top 5 molecule drugs for late stage progression of prediabetes to T1D    41
Figures
Figure 1. The flowchart of system biology method to identify the real GWGENs    44
Figure 2. The timeline of progression of prediabetes to T1D    44
Figure 3. Real GWGEN of early stage progression of prediabetes to T1D    45
Figure 4. Real GWGEN of late stage progression of prediabetes to T1D    45
Figure 5. Core GWGEN of early stage progression of prediabetes to T1D    46
Figure 6. Core GWGEN of late stage progression of prediabetes to T1D    46
Figure 7. The pathogenic mechanism based on core pathways of early stage progression of prediabetes to T1D    47
Figure 8. The pathogenic mechanism based on core pathways of late stage progression of prediabetes to T1D    48
Figure 9. The overall progression mechanisms of early to late stage of prediabetes to T1D    49




                                

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