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研究生： 鍾明勳
Chung, Ming-Hsun
論文名稱： 藉由大數據探勘、高通量資料估算與系統識別方法探討阿茲海默症病理機制
Investigating the Pathogenetic Mechanisms of Alzheimer's Disease via Big Data Mining, High throughput Data Estimation, and System Identification
指導教授： 陳博現
Chen, Bor-Sen
口試委員: 張兗君
Zhang, Yan-Jun
汪宏達
Wang, Hong-Da
周姽嫄
Zhou, Gui-Yuan
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： 48
中文關鍵詞： 阿茲海默症 、基因與表觀遺傳網路 、系統識別方法 、藥物目標 、藥物設計
外文關鍵詞： Alzheimer's disease, genetic and epigenetic networks, system identification, drug targets, drugs design
相關次數： 點閱：2  下載：0 
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    • 阿茲海默症簡稱阿氏症(AD)是失智症最常見的成因之一，其特徵是漸進的認知能力下降與神經退化性障礙。異常堆積的胞內神經原纖維纏結(NFTs)與不正常積聚的澱粉樣蛋白β(Aβ)胜肽是阿氏症大腦最重要的兩個病理特徵。然而，儘管已經進行了大規模的臨床研究與生物資訊的分析，阿氏症發展與進程的分子機制仍然還不是非常清楚。在本研究中，我們將所有的樣本分成兩個組別，早期組(Braak分級1-3)與晚期組(Braak分級4-6)。藉由使用大數據分析，兩期的候選基因與表觀遺傳網路(GENs)已經被分別建立。為了在基因與表觀遺傳網路中找出顯著成員，我們藉由系統識別方法與系統階層測定法來刪除網路中的偽陽性。基於在主要網路投影方法(PNP)中奇異值表現量的準則，主要奇異值包含85%的系統能量以及奇異值向量構成85%的GEN主要網路結構。因此，具有投影值排名前2,000名的核心GENs利用PNP方法從候選GENs中萃取出來。在每個階段中，通過建立核心GENs與分析核心訊號傳遞路徑，數個早期與晚期的阿氏症事件已經被識別出來。顯示出鈣離子穩態失調、氧化壓力、慢性發炎與過度磷酸化tau蛋白發生在阿氏症非常早的時期。隨著疾病惡化，後期阿氏症大腦則被多重反應包含細胞週期變異、DNA損傷、腦內障礙與細胞凋亡嚴重影響。其結果就是，表觀遺傳調控與微環境因子在阿氏症的發展與進程中扮演了重要角色。此外，根據這些進展機制，我們識別出了數個關鍵的生物標記作為藥物標靶，以設計潛在藥物用於治療早期與晚期阿氏症。最後，這些研究可望有助於識別阿氏症早期的指標並且發展出新穎的療法去對抗像阿氏症般的神經退化性疾病。

    Alzheimer's disease (AD) is the most common cause of dementia, characterized by progressive cognitive decline and neurodegenerative disorder. Abnormal aggregations of intracellular neurofibrillary tangles (NFTs) and unusual accumulations of extracellular amyloid-β (Aβ) peptides are two important pathological features in AD brains. However, in spite of large-scale clinical studies and bioinformatic simulations, the molecular mechanism of AD development and progression is still unclear. In this approach, we divided all of the samples into two groups, early stages (Braak score I-III) and later stages (Braak score IV-VI). By using a big data mining analysis, candidate genetic and epigenetic networks (GENs) in two stages of AD have been constructed respectively. In order to find out the real GENs in candidate GENs, we have pruned false-positives in candidate GENs by the corresponding microarray data through system identification method and system order detection scheme. On the basis of criterion of eigenexpression fraction in principal network projection (PNP) method, the principal singular values include 85% of the systematic energy and their singular vectors construct principal network structure (85%) of GEN. Therefore, core GENs would be extracted from GENs with top 2,000 projection values by using PNP method. Through establishing the core GENs and analyzing the core pathways in each stage, several early and later events have been identified in AD. Calcium disequilibrium, oxidative stress, chronic inflammation, hyperphosphorylated tau protein, and nerve transmission are shown to onset in the very beginning of AD. With the deterioration of the disease, the later stage AD brain is heavily influenced by multiple impacts including cell cycle alteration, DNA damage, brain disorder, and apoptosis. As a result, epigenetic regulations and microenvironmental factors play important roles in AD development and progression. In addition, we identified several critical biomarkers as drug targets to design potential drugs in the treatment of early and later AD based on these progression mechanisms. Finally, these studies may contribute to recognizing early signs of AD and exploring novel treatments to combat neurodegenerative disease such as AD.

    摘要 I Abstract II 誌謝 III Contents IV Chapter 1. Introduction 1 Chapter 2. Materials and Methods 4 2.1 Overview of system identification method and construction of GEN in early and later stage AD 4 2.2 Big data mining and genome-wide microarray data of AD 4 Chapter 3. Results 6 3.1 The identified GENs and core GENs in early and later stage of Alzheimer's disease 6 3.2 Core pathways in early stage Alzheimer's disease (Figure 4) 7 3.3 Core pathways in later stage Alzheimer's disease (Figure 5) 10 3.4 Pathogenetic mechanism of Alzheimer's disease (Figure 6) 11 3.5 Identification of multiple drug targets and multiple drugs design via drug data mining 13 Chapter 4. Discussion 16 Chapter 5. Conclusion 20 Chapter 6. Appendix 21 6.1 Building the systematic model to construct GENs via patients’ microarray data 21 6.2 Utilizing system identification to identify the parameters in the candidate GEN via microarray data of early and later stage AD 24 6.3 Applying PNP method to extract core GEN from GEN 31 Figures 34 Tables 40 References 43

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