藉由疾病網路和基因共表達網路分析阿茲罕默症和第二型糖尿病

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研究生：	張翃 Hung-Chang
論文名稱：	藉由疾病網路和基因共表達網路分析阿茲罕默症和第二型糖尿病 Analysis of disease and gene co-expression networks of Type 2 diabetes and Alzheimer’s diseases
指導教授：	吳立青 Li-Ching Wu
口試委員:
學位類別：	碩士 Master
系所名稱：	生醫理工學院 - 系統生物與生物資訊研究所 Graduate Institute of Systems Biology and Bioinformatics
論文出版年：	2016
畢業學年度：	104
語文別：	英文
論文頁數：	49
中文關鍵詞：	疾病網路、基因共表達網路、阿茲罕默症、第二型糖尿病
外文關鍵詞：	disease network, gene co-expression network, Alzheimer’s diseases, Type 2 diabetes
相關次數：	點閱：9 下載：0
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老化常伴隨一些常見疾病，例如:白內障、第二型糖尿病、阿茲海默症和高血壓…等。第二型糖尿病為常見的一種代謝性疾病，約占糖尿病診斷90~95%的病例。阿茲海默症則是癡呆的常見原因，並且導致心血管疾病，例如: 冠狀心臟疾病、中風、糖尿病和高血壓…等。儘管有許多研究指出阿茲海默症的潛在危險因素，但此病在流行病學的研究尚未清楚。
我們感興趣的一個研究課題為第二型糖尿病和阿茲海默症，分別在彼此間或其他相關疾病的合併症。透過已知的第二型糖尿病和阿茲海默症共享的病理和生理因素進行探討，包括胰島素、膽固醇、β類澱粉蛋白堆積和tau蛋白。最新證據指出胰島素的功能受損與阿茲海默症有關，並且指出它可能是新型的”第三型”糖尿病。
在本文的研究中，我們通過有系統的方法，深入了解阿茲海默症和第二型糖尿病之間可能出現的新連結。首先，我們著重於阿茲海默症和第二型糖尿病在臨床病患者的資料和相關疾病，藉此確定和分析這兩種疾病網路的變化，其結果顯示兩個疾病子網路病患資料並不相交的。其次，我們透過基因共表達網路，在阿茲海默症(大腦特定區域)和第二型糖尿病(特定組織)之間，去尋找逆向表現的基因對。總體來說，我們的目標是整合這兩種方法，來評估阿茲海默症和第二型糖尿病相關的風險、趨勢和可能的預防及治療訊息，並作為早期診斷的依據。

Aging often accompanied with some common diseases like Cataract, type 2 diabetes (T2D), Alzheimer’s disease (AD), and hypertension. T2D is a common of metabolic disorders, and accounts for about 90–95% of diagnosed cases of diabetes. AD is most common cause of dementia, and it is also more frequently related cardiovascular diseases such as coronary heart disease, stroke, diabetes mellitus and hypertension. In spite of such research efforts the underlying risk factors, epidemiologic and progression of AD are not well clear.
An issue of interest is the comorbidity of AD and T2D, respectively between each other and with other diseases. Known pathophysiological factors shared by AD and T2D include insulin, cholesterol, β-amyloid aggregation and tau. Recently, evidences connecting AD to impaired function of insulin/IGF and suggest AD might be a new type of “type 3” diabetes.
In this work, we gain insight into possible new connections between Alzheimer’s disease (AD) and type 2 diabetes (T2D) by taking a systems approach. First, we focus on AD and T2D patient's information and on related diseases, and identifying and analyzing changes in both of disease network that can be attributed to comorbidities are disjoint. Second, we identify gene-pairs that are inverse connections between brain region-specific ADs and tissue-specific T2Ds through gene co-expression networks. More specifically, we aim to integrate information related to risk, trend, and possible prevention and treatment of AD and T2D.

English abstract    i
Chinese abstract    ii
誌謝    iii
目錄    iv
表目錄 List of Tables    vi
圖目錄 List of Figures    vii
Chapter 1 Introduction    1
Chapter 2 Materials and Methods    3
2-1 Gene expression profile    3
2-1-1 Gene expression microarray    3
2-1-2 Microarray data preprocessing    3
2-1-3 DEG sets of AG, AD, and T2D    3
2-1-4 Collections of known AD and T2D genes    3
2-1-5 Calculate DEGs set in overlap    4
2-2 Disease network    4
2-2-1 Construct the diseases network    4
2-2-2 Clinical records of patients from the hospital    4
2-2-3 Construct AD and T2D disease subnetwork    5
2-3 Gene networks    5
2-3-1 Construct dysfunctional gene networks    5
2-3-2 Construct of differentially co-expressed gene pairs    5
2-3-3 Selection of DC gene co-expression networks by Q-value    6
2-3-4 Construct AD and T2D gene subnetworks    6
Chapter 3 Results    7
3-1 DEGs and its overlap    7
3-2 Comorbidity networks of neurological diseases    7
3-3 The AD and T2D in disease comorbidity network    7
3.4 Inverse connections between AD and T2D    8
3-5 The AD and T2D in dysfunctional gene pair network    8
3-6 The Statistical data between two DC gene subnetworks via separated DEG sets.    8
3-7 Overlaps of AD and T2D subnetworks.    8
Chapter 4 Discussion    9
Chapter 5 Conclusion    10
Chapter 6 Table and Figure    11
Supplementary data    31
Reference    34

                                

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