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

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：20

、訪客IP：3.12.153.79

姓名

張翃(Hung-Chang) 查詢紙本館藏

畢業系所

系統生物與生物資訊研究所

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)

相關論文

★ 細菌物種基因體中非編碼小片段核糖核酸之預測	★ 從年齡動態網路探討疾病盛行率
★ 藉由比較基因表現資料研究次世代定序與晶片技術分析差異	★ 啟動子甲基化與對應之基因表現微陣列資訊整合分析
★ 乾燥綜合症與非病毒型肝炎之相關因子分析	★ 氣候變遷對人類疾病網路造成衝擊
★ 台北和中壢地區不孕症分佈與共病探討	★ 探討台灣的門診疾病與環境空氣品質的濃度變化之相關性
★ 以地區醫院病例探討桃園之地域族群與疾病之差別	★ 桃園地區之區域與疾病盛行率之關聯
★ CyTOF之生物標記篩選與分析	★ 透明細胞腎細胞癌質譜流式細胞儀資料分析與視覺化
★ 使用支持向量機預測蛋白質醣基化位置	★ 使用基因表現資料預測基因轉錄調控網路
★ RNA Riboswitch搜尋系統之設計與實作	★ 人類疾病差異表現基因與調控網路之整合系統

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

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。

摘要(英)

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.

關鍵字(中)

★ 疾病網路
★ 基因共表達網路
★ 阿茲罕默症
★ 第二型糖尿病

關鍵字(英)

★ disease network
★ gene co-expression network
★ Alzheimer’s diseases
★ Type 2 diabetes

論文目次

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

參考文獻

[1] Ford ES, Giles WH, Dietz WH (2002) Prevalence of the metabolic syndrome among US adults: findings from the third National Health and Nutrition Examination Survey. Jama 287, 356-359.
[2] Jellinger KA (2008) Neuropathological aspects of Alzheimer disease, Parkinson disease and frontotemporal dementia. Neurodegenerative Diseases 5, 118-121.
[3] de la Monte SM, Wands JR (2005) Review of insulin and insulin-like growth factor expression, signaling, and malfunction in the central nervous system: relevance to Alzheimer′s disease. Journal of Alzheimer′s Disease 7, 45-61.
[4] Nagele E, Han M, DeMarshall C, Belinka B, Nagele R (2011) Diagnosis of Alzheimer’s disease based on disease-specific autoantibody profiles in human sera. PLoS One 6, e23112.
[5] Stampfer M (2006) Cardiovascular disease and Alzheimer′s disease: common links. Journal of internal medicine 260, 211-223.
[6] Whitmer RA (2007) Type 2 diabetes and risk of cognitive impairment and dementia. Current neurology and neuroscience reports 7, 373-380.
[7] Iwangoff P, Armbruster R, Enz A, Meier-Ruge W (1980) Glycolytic enzymes from human autoptic brain cortex: normal aged and demented cases. Mechanisms of ageing and development 14, 203-209.
[8] Sims N, Smith C, Davison A, Bowen D, Flack R, Snowden J, Neary D (1980) Glucose metabolism and acetylcholine synthesis in relation to neuronal activity in Alzheimer′s disease. The Lancet 315, 333-336.
[9] Akter K, Lanza EA, Martin SA, Myronyuk N, Rua M, Raffa RB (2011) Diabetes mellitus and Alzheimer′s disease: shared pathology and treatment? British journal of clinical pharmacology 71, 365-376.
[10] Sun XJ, Crimmins D, Myers M, Miralpeix M, White M (1993) Pleiotropic insulin signals are engaged by multisite phosphorylation of IRS-1. Molecular and Cellular Biology 13, 7418-7428.
[11] Reed B, Villeneuve S, Mack W, DeCarli C, Chui HC, Jagust W (2014) Associations between serum cholesterol levels and cerebral amyloidosis. JAMA neurology 71, 195-200.
[12] Basu R, Chandramouli V, Dicke B, Landau B, Rizza R (2005) Obesity and type 2 diabetes impair insulin-induced suppression of glycogenolysis as well as gluconeogenesis. Diabetes 54, 1942-1948.
[13] Rönnemaa E, Zethelius B, Sundelöf J, Sundström J, Degerman-Gunnarsson M, Berne C, Lannfelt L, Kilander L (2008) Impaired insulin secretion increases the risk of Alzheimer disease. Neurology 71, 1065-1071.
[14] Kivipelto M, Ngandu T, Fratiglioni L, Viitanen M, Kåreholt I, Winblad B, Helkala E-L, Tuomilehto J, Soininen H, Nissinen A (2005) Obesity and vascular risk factors at midlife and the risk of dementia and Alzheimer disease. Archives of neurology 62, 1556-1560.
[15] Schubert M, Brazil DP, Burks DJ, Kushner JA, Ye J, Flint CL, Farhang-Fallah J, Dikkes P, Warot XM, Rio C (2003) Insulin receptor substrate-2 deficiency impairs brain growth and promotes tau phosphorylation. The Journal of neuroscience 23, 7084-7092.
[16] Lester-Coll N, Rivera EJ, Soscia SJ, Doiron K, Wands JR, de la Monte SM (2006) Intracerebral streptozotocin model of type 3 diabetes: relevance to sporadic Alzheimer′s disease. Journal of Alzheimer′s disease: JAD 9, 13-33.
[17] Talan J (2015) Cerebrovascular Infarcts Increase the Associated Risk Between Diabetes and Alzheimer′s. Neurology Today 15, 1-4.
[18] Li J-M, Liu C, Hu X, Cai Y, Ma C, Luo X-G, Yan X-X (2014) Inverse correlation between Alzheimer’s disease and cancer: implication for a strong impact of regenerative propensity on neurodegeneration? BMC neurology 14, 211.
[19] Liu T, Ren D, Zhu X, Yin Z, Jin G, Zhao Z, Robinson D, Li X, Wong K, Cui K (2013) Transcriptional signaling pathways inversely regulated in Alzheimer′s disease and glioblastoma multiform. Scientific reports 3.
[20] Ou SM, Lee YJ, Hu YW, Liu CJ, Chen TJ, Fuh JL, Wang SJ (2013) Does Alzheimer′s disease protect against cancers? A nationwide population-based study. Neuroepidemiology 40, 42-49.
[21] Huang CC, Chung CM, Leu HB, Lin LY, Chiu CC, Hsu CY, Chiang CH, Huang PH, Chen TJ, Lin SJ, Chen JW, Chan WL (2014) Diabetes mellitus and the risk of Alzheimer′s disease: a nationwide population-based study. PLoS One 9, e87095.
[22] Choi M, Lee DW, Cho MJ, Park JE, Gim M (2015) Disease network of mental disorders in Korea. Soc Psychiatry Psychiatr Epidemiol 50, 1905-1914.
[23] Yang J, Wu SJ, Yang SY, Peng JW, Wang SN, Wang FY, Song YX, Qi T, Li YX, Li YY (2016) DNetDB: The human disease network database based on dysfunctional regulation mechanism. BMC Syst Biol 10, 36.
[24] Chu G, Narasimhan B, Tibshirani R, Tusher V (2002) SAM,“Significance Analysis of Microarrays”: Users Guide and Technical Document. Stanford University.
[25] Amar D, Safer H, Shamir R (2013) Dissection of regulatory networks that are altered in disease via differential co-expression. PLoS Comput Biol 9, e1002955.
[26] Daviglus ML, Plassman BL, Pirzada A, Bell CC, Bowen PE, Burke JR, Connolly ES, Dunbar-Jacob JM, Granieri EC, McGarry K (2011) Risk factors and preventive interventions for Alzheimer disease: state of the science. Archives of Neurology 68, 1185-1190.
[27] Barrett T, Troup DB, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM (2011) NCBI GEO: archive for functional genomics data sets—10 years on. Nucleic acids research 39, D1005-D1010.
[28] Smyth GK (2005) Limma: linear models for microarray data In Bioinformatics and computational biology solutions using R and Bioconductor Springer, pp. 397-420.
[29] Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP (2003) Summaries of Affymetrix GeneChip probe level data. Nucleic acids research 31, e15-e15.
[30] Irizarry RA, Hobbs B, Collin F, Beazer‐Barclay YD, Antonellis KJ, Scherf U, Speed TP (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4, 249-264.
[31] Wood F (2009) Principal component analysis.
[32] Yeung KY, Ruzzo WL (2001) Principal component analysis for clustering gene expression data. Bioinformatics 17, 763-774.
[33] Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic acids research, gkv007.
[34] Bertram L, McQueen MB, Mullin K, Blacker D, Tanzi RE (2007) Systematic meta-analyses of Alzheimer disease genetic association studies: the AlzGene database. Nature genetics 39, 17-23.
[35] Sun J, Feng X, Liang D, Duan Y, Lei H (2012) Down-regulation of energy metabolism in Alzheimer′s disease is a protective response of neurons to the microenvironment. Journal of Alzheimer′s Disease 28, 389-402.
[36] Bai Z, Han G, Xie B, Wang J, Song F, Peng X, Lei H (2016) AlzBase: an integrative database for gene dysregulation in Alzheimer’s disease. Molecular neurobiology 53, 310-319.
[37] Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA (2005) Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic acids research 33, D514-D517.
[38] Dai H-J, Wu JC-Y, Tsai RT-H, Pan W-H, Hsu W-L (2013) T-HOD: a literature-based candidate gene database for hypertension, obesity and diabetes. Database: the journal of biological databases and curation 2013.
[39] Shen L SM, GeneOverlap: Test and visualize gene overlaps. R package version 1.6.0, http://shenlab-sinai.github.io/shenlab-sinai/,
[40] Hussain T, Gupta RK, Sweety K, Eswaran B, Vijayakumar M, Rao CV (2012) Nephroprotective activity of Solanum xanthocarpum fruit extract against gentamicin-induced nephrotoxicity and renal dysfunction in experimental rodents. Asian Pac J Trop Med 5, 686-691.
[41] Fan Y, Eswarappa SM, Hitomi M, Fox PL (2012) Myo1c facilitates G-actin transport to the leading edge of migrating endothelial cells. J Cell Biol 198, 47-55.
[42] Wang K, Narayanan M, Zhong H, Tompa M, Schadt EE, Zhu J (2009) Meta-analysis of inter-species liver co-expression networks elucidates traits associated with common human diseases. PLoS Comput Biol 5, e1000616.
[43] Narayanan M, Huynh JL, Wang K, Yang X, Yoo S, McElwee J, Zhang B, Zhang C, Lamb JR, Xie T (2014) Common dysregulation network in the human prefrontal cortex underlies two neurodegenerative diseases. Molecular systems biology 10, 743.
[44] Barbagallo M, Dominguez LJ (2014) Type 2 diabetes mellitus and Alzheimer’s disease. World J Diabetes 5, 889-893.
[45] Misu H, Takamura T, Takayama H, Hayashi H, Matsuzawa-Nagata N, Kurita S, Ishikura K, Ando H, Takeshita Y, Ota T (2010) A liver-derived secretory protein, selenoprotein P, causes insulin resistance. Cell metabolism 12, 483-495.
[46] Jin W, Goldfine AB, Tanner Boes RRH, Ciaraldi TP, Kim E-Y, Emecan M, Fitzpatrick C, Sen A, Shah A, Mun E (2011) Increased SRF transcriptional activity in human and mouse skeletal muscle is a signature of insulin resistance. The Journal of clinical investigation 121, 918.
[47] Bomfim TR, Forny-Germano L, Sathler LB, Brito-Moreira J, Houzel J-C, Decker H, Silverman MA, Kazi H, Melo HM, McClean PL (2012) An anti-diabetes agent protects the mouse brain from defective insulin signaling caused by Alzheimer’s disease–associated Aβ oligomers. The Journal of clinical investigation 122, 1339-1353.
[48] Lu T, Aron L, Zullo J, Pan Y, Kim H, Chen Y, Yang T-H, Kim H-M, Drake D, Liu XS (2014) REST and stress resistance in ageing and Alzheimer/′s disease. Nature 507, 448-454.
[49] Liang WS, Dunckley T, Beach TG, Grover A, Mastroeni D, Walker DG, Caselli RJ, Kukull WA, McKeel D, Morris JC (2007) Gene expression profiles in anatomically and functionally distinct regions of the normal aged human brain. Physiological genomics 28, 311-322.
[50] Liang WS, Reiman EM, Valla J, Dunckley T, Beach TG, Grover A, Niedzielko TL, Schneider LE, Mastroeni D, Caselli R (2008) Alzheimer′s disease is associated with reduced expression of energy metabolism genes in posterior cingulate neurons. Proceedings of the National Academy of Sciences 105, 4441-4446.
[51] Dominguez V, Raimondi C, Somanath S, Bugliani M, Loder MK, Edling CE, Divecha N, da Silva-Xavier G, Marselli L, Persaud SJ (2011) Class II phosphoinositide 3-kinase regulates exocytosis of insulin granules in pancreatic β cells. Journal of Biological Chemistry 286, 4216-4225.

指導教授

吳立青(Li-Ching Wu)

審核日期

2016-7-27

推文