miRDRN-疾病相關微 RNA 之調控網路:應用於探索 疾病與組織特異性微 RNA 調控網路的工具

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：29

、訪客IP：18.190.176.244

姓名

劉學銓(Hsueh-Chuan Liu) 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

miRDRN-疾病相關微 RNA 之調控網路:應用於探索疾病與組織特異性微 RNA 調控網路的工具
(miRDRN－miRNA Disease Regulatory Network: A tool for exploring disease and tissue-specific microRNA regulatory networks)

相關論文

★ 人類陰道滴蟲之Myb2蛋白質動態性質研究	★ 分析原核生物基因體複製起點與終點的反向對偶對稱現象
★ 分析基因體拷貝數變異所使用的兩種方法比較：隱藏馬可夫模型與成對高斯合併法	★ 使用兩種方法偵測基因體拷貝數變異：成對高斯合併法與隱藏馬可夫模型
★ 以整體晶片數據為母體應用於分析基因差異表達的z檢定方法	★ GSLHC －運用基因組及層次類聚以生物功能群將有生物活性的複合物定性的方法
★ 一個檢定測量微晶片基因表達數據靈敏度的全統計計算法	★ 運用嶄新抗體固著策略發展及驗證新式抗體微晶片平台
★ Drug-resistant colon cancer cells produce high carcinoembryonic antigen and might not be cancer-initiating cells	★ 創傷性關節炎軟骨之退化進程－大鼠模型基因體圖譜研究
★ 基因體功能統合分析在阿茲海默症和大腦老化－近年阿茲海默症研發藥物失敗的理論問題探討	★ 運用時間序列微陣列資料來預測調控基因
★ 以大鼠嗜鉻性瘤細胞株建立神經訊號傳遞之細胞分子生物學模型	★ 一種找尋再利用藥物複合物來系統性治療複雜疾病的架構：大腸直腸腺瘤的應用
★ 以上皮細胞間質化與增生相關功能來描述癌症幹細胞之基因型	★ 從共表達差異基因對導出正常腦老化及因阿茲海默症特定腦區導致在功能性基因途徑與樞紐基因子網絡之變化

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)

微 RNA 透過調控特定的靶基因來達到調節細胞過程。當前已有許多已知數百種微 RNA 和其調控的靶基因，以及許多跟疾病相關的微 RNA 資訊。跟疾病相關的生成，在細胞過程中，通常是經過基因之間的相互作用與基因作用的產物，進而組織並共同參與同一功能反應路徑上，包括一連串複雜的交互作用。大型的蛋白質交互作用資料庫是一個非有有用的資源。在這個研究中，透過彙整上述的相關資訊，我們建構了一個網路服務平台，我們稱為 miRDRN (miRNA Disease Regulatory Network)。它可提供使用者進行建構疾病特異性相關微 RNA 調控網路。該平台公開網址為 http://mirdrn.ncu.edu.tw/mirdrn/，此平台具備兩個特色: (1) 擁有 6,973,875 筆 P 值子路徑資料庫，子路徑是由微 RNA 調控的靶基因-基因 1-基因 2 所構成的鏈，由 78 種組織類型中的 116 種疾病相關 207 種微 RNA，所調控的 389 個靶基因所建構的子路徑清單。(2) 應用於建構疾病和組織特異性相關微 RNA-蛋白質調控網路的可視化工具，可應用於單一疾病或兩種不同疾病的共病研究。依據使用者在搜尋介面上的輸入條件，以及具備互動式的可視化圖形工具，呈現微 RNA-蛋白質調控網路結果。 miRDRN 應用示範，示範一:在大腸癌(colorectal cancer; CRC)單一疾病的研究，識別出 34 個目前未列為 CRC 靶基因的新基因，其中有 26 個基因具有與 CRC 相關的文獻支持。示範二:在阿茲海默症(Alzheimer′s disease; AD)與二型糖尿病(Type 2 diabetes; T2D)的共病研究中，其中 20 個基因是已知的 AD 靶基因或 T2D 的靶基因，並非同時共存在兩個疾病之間。而在我們的研究結果中，其中有 18 個基因是被文獻支持，視為共病的相關基因。在另一個議題上，關於阿茲海默症的抑制劑藥物 BACE1 在晚期的試驗中，最近公告為失敗。為了探究其原因，我們建構以 BACE1 為中心的腦組織特異性微 RNA-蛋白質調控子網路，結果顯示，BACE1 的下游基因，對其抑制可能影響腦神經傳遞的受損。

摘要(英)

miRNA regulate cellular processes through acting on specific target genes. Hundreds of miRNA genes and their target genes are known, as are many miRNA-disease associations. Cellular processes, including those related to disease, proceed through multiple interactions, often organized into pathways among genes and gene products. Large databases on proteinprotein interactions are available. Here, through integration of the information mentioned above, we have constructed a web service platform, miRNA Disease Regulatory Network (miRDRN) that constructs disease specific miRNA-protein regulatory networks. The platform, publicly accessible at http://mirdrn.ncu.edu.tw/mirdrn/, contains two parts: (a) a database that contains 6,973,875 p-valued sub-pathways, in the form of miRNA-target genegene-gene, associated with 116 diseases in 78 tissue types built from 207 diseases-associated miRNAs regulating 389 genes, and (b) a tool that facilitates the construction and visualization of disease and tissue specific miRNA-protein regulatory networks, for single diseases, or disease pairs for the case of comorbidity studies. Results are presented in the form of userinput enabled tables and interactive visualization of the entire constructed miRNA-protein regulatory networks, or parts thereof, such as a sub-regulatory network connected to a specific gene. As demonstrations, miRDRN was applied: to study the single disease colorectal cancer (CRC), in which 34 novel genes not currently listed as CRC target genes were identified, 26 of which have literature support as being CRC related; to study the comorbidity of the disease pair Alzheimer′s disease-Type 2 diabetes (AD-T2D), in which 20 genes that are known as either AD or T2D target genes but not both were identified, 18 of which have literature support to be comorbid; and, for exploring possible causes that may shed light on recent failures of late-phase trials of anti-AD, BACE1 inhibitor drugs, to construct an AD, brain tissue-specific miRNA-protein regulatory sub-network centered on BACE1, in which genes downstream to BACE1 whose suppression may affect signal transduction were identified.

關鍵字(中)

★ 共病基因
★ 大腸癌
★ 阿茲海默症
★ 二型糖尿病
★ 疾病與組織特異性微 RNA 調控網路
★ 資料庫與網路服務工具

關鍵字(英)

★ comorbidity gene
★ colorectal cancer
★ Alzheimer′s disease
★ Type 2 diabetes
★ anti-AD BACE1 inhibitor drug
★ disease and tissue-specific miRNA-protein regulatory network
★ disease-miRNA association
★ target-specific regulatory pathway
★ miRNA-target association
★ database and web service tool

論文目次

Table of Contents
摘要 ............................................................................................ i
Abstract ........................................................................................ ii
Table of Contents ............................................................................... iv
List of Figures ................................................................................. vi
List of Tables .................................................................................. vii
List of Abbreviations ........................................................................... viii
Chapter 1. Introduction ......................................................................... 1
1.1 Interactome resources: Protein-Protein interaction databases ................................ 1
1.2 MicroRNA regulatory network ................................................................. 2
1.3 Molecular and physiopathological mechanisms of diseases ..................................... 3
1.4 A web-accessible interface .................................................................. 4
1.5 Significance and Project Aims ............................................................... 4
Chapter 2. Materials and methods ................................................................ 6
2.1 Data integration ............................................................................ 6
2.2 Construction of miRNA-associated target-specific regulatory sub-pathways .................... 6
2.3 Jaccard score of a regulatory sub-pathway ................................................... 7
2.4 P-value of a regulatory sub-pathway ......................................................... 8
2.5 Assembly and storage of target-specific regulatory sub-pathways ............................. 8
2.6 Construction of disease-specific miRNA regulatory network.................................... 9
2.7 Environment of the service platform ......................................................... 9
2.7.1 Operating system. ......................................................................... 9
2.7.2 Development script ........................................................................ 9
2.7.3 Network visualization ..................................................................... 10
Chapter 3. Results .............................................................................. 11
3.1 miRNA Disease Regulatory Network (miRDRN) – A database and web service platform ............ 11
3.2 Comparison of miRDRN with other miRNA related databases ..................................... 11
3.3 Brief description of usage of miRDRN ........................................................ 11
Chapter 4. Three applications of miRDRN and Discussion........................................... 14
4.1 Case 1: A single disease study of colorectal neoplasm ....................................... 14
4.2 Case 2: A Comorbidity study of the disease-pair Alzheimer′s disease-Type 2 diabetes (AD-T2D) ................................................................................................. 15
4.3 Case 3: A sub-RRN centered on the AD-associated gene BACE1 .................................. 16
Chapter 5. Conclusion ........................................................................... 18
References ...................................................................................... 19

參考文獻

1. Rual JF, Venkatesan K, Hao T, et al. Towards a proteome-scale map of the human protein-protein interaction network. Nature. 2005; 437(7062):1173-8.
2. Liang H1, Li WH. MicroRNA regulation of human protein protein interaction network. RNA. 2007; 13(9):1402-8.
3. Riley R, Lee C, Sabatti C, et al. Inferring protein domain interactions from databases of interacting proteins. Genome Biol. 2005; 6(10):R89.
4. Mishra GR, Suresh M, Kumaran K, et al. Human protein reference database--2006 update. Nucleic Acids Res. 2006; 34(Database issue):D411-4.
5. Bader GD, Betel D, Hogue CW. BIND: the Biomolecular Interaction Network Database. Nucleic Acids Res. 2003; 31(1):248-50.
6. Mewes HW, Frishman D, Güldener U, et al. MIPS: a database for genomes and protein sequences. Nucleic Acids Res. 2002; 30(1):31-4.
7. Chatr-aryamontri A, Ceol A, Palazzi LM, et al. MINT: the Molecular INTeraction database. Nucleic Acids Res. 2007; 35(Database issue):D572-4.
8. Xenarios I, Salwínski L, Duan XJ, et al. DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions. Nucleic Acids Res. 2002; 30(1):303-5.
9. Hermjakob H, Montecchi-Palazzi L, Lewington C, et al. IntAct: an open source molecular interaction database. Nucleic Acids Res. 2004; 32(Database issue):D452-5. 10. Chatr-Aryamontri A, Breitkreutz BJ, Heinicke S, Boucher L, Winter A, Stark C, Nixon J, Ramage L, Kolas N, O′Donnell L, et al. The BioGRID interaction database: 2013 update. Nucleic Acids Res. 2013, 41: D816-23.
11. Bartel DP. MicroRNAs: target recognition and regulatory functions. Cell. 2009; 136(2):215-33.
12. Landgraf P, Rusu M, Sheridan R, et al. A mammalian microRNA expression atlas based on small RNA library sequencing. Cell. 2007; 129(7):1401-14.
13. Hsu CW, Juan HF, Huang HC. Characterization of microRNA-regulated protein-protein interaction network. Proteomics. 2008; 8(10):1975-9.
14. Zhang W, Dahlberg JE, Tam W. MicroRNAs in tumorigenesis: a primer. Am J Pathol. 2007; 171(3):728-38.
15. Zhang S, Jin G, Zhang XS, et al. Discovering functions and revealing mechanisms at molecular level from biological networks. Proteomics. 2007; 7(16):2856-69.
16. Botstein D, Risch N. Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease. Nature Genetics. 2003; 33: 228–237.
17. Kann MG. Advances in translational bioinformatics: computational approaches for the hunting of disease genes. Brief Bioinform. 2010; 11: 96–110.
18. Scriver CR, Waters PJ. Monogenic traits are not simple: lessons from phenylketonuria. Trends Genet. 1999; 15: 267–272.
19. Hirschhorn JN, Daly MJ. Genome-wide association studies for common diseases and complex traits. Nat Rev Genet. 2005; 6: 95–108.
20. Barabási AL, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nat Rev. Genet. 201; 12(1): 56-68.
21. Goh KI, Cusick ME, Valle D, et al. The human disease network. Proc Natl Acad Sci U S A. 2007; 104(21): 8685-90.
22. Lee DS, Park J, Kay KA, et al. The implications of human metabolic network topology for disease comorbidity. Proc Natl Acad Sci U S A. 2008; 105(29): 9880-5.
23. Ideker T, Sharan R. Protein networks in disease. Genome Res. 2008; 18(4): 644-52.
24. Lim J, Hao T, Shaw C, et al. A protein-protein interaction network for human inherited ataxias and disorders of Purkinje cell degeneration. Cell. 2006; 125(4): 801-14.
25. Lee R, Karr JR, Covert MW. WholeCellViz: data visualization for whole-cell models. BMC Bioinformatics. 2013; 14:253.
26. Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003; 13(11):2498-504.
27. Bhagat J, Tanoh F, Nzuobontane E, et al. BioCatalogue: a universal catalogue of web services for the life sciences. Nucleic Acids Res. 2010; 38(Web Server issue):W689-94.
28. Li Y, Qiu C, Tu J, Geng B, Yang J, Jiang T, Cui Q. HMDD v2.0: a database for experimentally supported human microRNA and disease associations. Nucleic Acids Res. 2014, 42: D1070-4.
29. Vlachos IS, Paraskevopoulou MD, Karagkouni D, Georgakilas G, Vergoulis T, Kanellos I, Anastasopoulos IL, Maniou S, Karathanou K, Kalfakakou D, et al. DIANA-TarBase v7.0: indexing more than half a million experimentally supported miRNA:mRNA interactions. Nucleic Acids Res. 2015, 43: D153-9.
30. NCBI Resource Coordinators. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2018, 46(D1): D8-D13.
31. Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017, 45(D1): D353D361.
32. Chen JS, Hung WS, Chan HH, Tsai SJ, Sun HS. In silico identification of oncogenic potential of fyn-related kinase in hepatocellular carcinoma. Bioinformatics. 2013, 29(4): 420-7.
33. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000, 25(1): 25-9.
34. Ng KL, Liu HC, Lee SC. ncRNAppi-a tool for identifying disease-related miRNA and siRNA targeting pathways. Bioinformatics. 2009, 25(23): 3199-201.
35. Ruepp A, Kowarsch A, Theis F. PhenomiR: microRNAs in human diseases and biological processes. Methods Mol Biol. 2012, 822: 249-60.
36. Das SS, Saha P, Chakravorty N. miRwayDB: a database for experimentally validated microRNA-pathway associations in pathophysiological conditions. Database (Oxford). 2018, 2018.
37. Backes C, Kehl T, Stöckel D, Fehlmann T, Schneider L, Meese E, Lenhof HP, Keller A. miRPathDB: a new dictionary on microRNAs and target pathways. Nucleic Acids Res. 2017, 45(D1): D90-D96.
38. Agarwal R, Kumar B, Jayadev M, Raghav D, Singh A. CoReCG: a comprehensive database of genes associated with colon-rectal cancer. Database (Oxford). 2016, 2016. 39. Kang MH, Moon SU, Sung JH, Kim JW, Lee KW, Lee HS, Lee JS, Kim JH. Antitumor Activity of HM781-36B, alone or in Combination with Chemotherapeutic Agents, in Colorectal Cancer Cells. Cancer Res Treat. 2016, 48(1): 355-64.
40. Sirvent A, Bénistant C, Pannequin J, Veracini L, Simon V, Bourgaux JF, Hollande F, Cruzalegui F, Roche S. Src family tyrosine kinases-driven colon cancer cell invasion is induced by Csk membrane delocalization. Oncogene. 2010, 29(9): 1303-15.
41. Jeong D, Kim H, Kim D, Ban S, Oh S, Ji S, Kang D, Lee H, Ahn TS, Kim HJ, et al. Protein kinase, membrane‑associated tyrosine/threonine 1 is associated with the progression of colorectal cancer. Oncol Rep. 2018, 39(6): 2829-2836.
42. Xie T, D′ Ario G, Lamb JR, Martin E, Wang K, Tejpar S, Delorenzi M, Bosman FT, Roth AD, Yan P, et al. A comprehensive characterization of genome-wide copy number aberrations in colorectal cancer reveals novel oncogenes and patterns of alterations. PLoS One. 2012, 7(7): e42001.
43. Wu S, Wu F, Jiang Z. Identification of hub genes, key miRNAs and potential molecular mechanisms of colorectal cancer. Oncol Rep. 2017, 38(4): 2043-2050.
44. Xiang Z, Wang S, Xiang Y. Up-regulated microRNA499a by hepatitis B virus induced hepatocellular carcinogenesis via targeting MAPK6. PLoS One. 2014, 9(10): e111410.
45. Masuda M, Yamada T. The emergence of TNIK as a therapeutic target for colorectal cancer. Expert Opin Ther Targets. 2017, 21(4): 353-355.
46. Ali RH, Marafie MJ, Bitar MS, Al-Dousari F, Ismael S, Bin Haider H, Al-Ali W, Jacob SP, Al-Mulla F. Gender-associated genomic differences in colorectal cancer: clinical insight from feminization of male cancer cells. Int J Mol Sci. 2014, 15(10): 17344-65.
47. Yun CW, Kim S, Lee JH, Lee SH. Melatonin Promotes Apoptosis of Colorectal Cancer Cells via Superoxide-mediated ER Stress by Inhibiting Cellular Prion Protein Expression. Anticancer Res. 2018, 38(7): 3951-3960.
48. Vázquez-Cedeira M, Lazo PA. Human VRK2 (vaccinia-related kinase 2) modulates tumor cell invasion by hyperactivation of NFAT1 and expression of cyclooxygenase-2. J Biol Chem. 2012, 287(51): 42739-50.
49. Zhang YJ, Dai Q, Sun DF, Xiong H, Tian XQ, Gao FH, Xu MH, Chen GQ, Han ZG, Fang JY. mTOR signaling pathway is a target for the treatment of colorectal cancer. Send to Ann Surg Oncol. 2009, 16(9): 2617-28.
50. Csukasi F, Duran I, Barad M, Barta T, Gudernova I, Trantirek L, Martin JH, Kuo CY, Woods J, Lee H, et al. The PTH/PTHrP-SIK3 pathway affects skeletogenesis through altered mTOR signaling. Sci Transl Med. 2018, 10(459).
51. Rey C, Faustin B, Mahouche I, Ruggieri R, Brulard C, Ichas F, Soubeyran I, Lartigue L, De Giorgi F. The MAP3K ZAK, a novel modulator of ERK-dependent migration, is upregulated in colorectal cancer. Oncogene. 2016, 35(24): 3190-200.
52. Goyal P, Behring A, Kumar A, Siess W. Identifying and characterizing a novel protein kinase STK35L1 and deciphering its orthologs and close-homologs in vertebrates. PLoS One. 2009, 4(9): e6981.
53. Sabir SR, Sahota NK, Jones GD, Fry AM. Loss of Nek11 Prevents G2/M Arrest and Promotes Cell Death in HCT116 Colorectal Cancer Cells Exposed to Therapeutic DNA Damaging Agents. PLoS One. 2015, 10(10): e0140975.
54. Bjerrum JT, Nielsen OH, Riis LB, Pittet V, Mueller C, Rogler G, Olsen J. Transcriptional analysis of left-sided colitis, pancolitis, and ulcerative colitis-associated dysplasia. Inflamm Bowel Dis. 2014, 20(12): 2340-52.
55. Hanna DL, Loupakis F, Yang D, Cremolini C, Schirripa M, Li M, Matsusaka S, Berger MD, Miyamoto Y, Zhang W, , et al. Prognostic Value of ACVRL1 Expression in Metastatic Colorectal Cancer Patients Receiving First-line Chemotherapy With Bevacizumab: Results From the Triplet Plus Bevacizumab (TRIBE) Study. Clin Colorectal Cancer. 2018, 17(3): e471-e488.
56. Record CJ, Chaikuad A, Rellos P, Das S, Pike AC, Fedorov O, Marsden BD, Knapp S, Lee WH. Structural comparison of human mammalian ste20-like kinases. PLoS One. 2010, 5(8): e11905.
57. Zhou JK, Zheng YZ, Liu XS, Gou Q, Ma R, Guo CL, Croce CM, Liu L, Peng Y. ROR1 expression as a biomarker for predicting prognosis in patients with colorectal cancer. Oncotarget. 2017, 8(20): 32864-32872.
58. Gong H, Fang L, Li Y, Du J, Zhou B, Wang X, Zhou H, Gao L, Wang K, Zhang J. miR873 inhibits colorectal cancer cell proliferation by targeting TRAF5 and TAB1. Oncol Rep. 2018, 39(3): 1090-1098.
59. Yuan WC, Pepe-Mooney B, Galli GG, Dill MT, Huang HT, Hao M, Wang Y, Liang H, Calogero RA, Camargo FD. NUAK2 is a critical YAP target in liver cancer. Nat Commun. 2018, 9(1): 4834.
60. Kim ST, Ahn TJ, Lee E, Do IG, Lee SJ, Park SH, Park JO, Park YS, Lim HY, Kang WK, et al. Exploratory biomarker analysis for treatment response in KRAS wild type metastatic colorectal cancer patients who received cetuximab plus irinotecan. BMC Cancer. 2015, 15: 747.
61. Li BQ, Huang T, Zhang J, Zhang N, Huang GH, Liu L, Cai YD. An ensemble prognostic model for colorectal cancer. Send to PLoS One. 2013, 8(5): e63494.
62. Guo H, Hu X, Ge S, Qian G, Zhang J. Regulation of RAP1B by miR-139 suppresses human colorectal carcinoma cell proliferation. Int J Biochem Cell Biol. 2012, 44(9): 1465-72.
63. Peng H, Luo J, Hao H, Hu J, Xie SK, Ren D, Rao B. MicroRNA-100 regulates SW620 colorectal cancer cell proliferation and invasion by targeting RAP1B. Oncol Rep. 2014, 31(5): 2055-62.
64. Alonso MH, Aussó S, Lopez-Doriga A, Cordero D, Guinó E, Solé X, Barenys M, de Oca J, Capella G, Salazar R, Sanz-Pamplona R, Moreno V. Comprehensive analysis of copy number aberrations in microsatellite stable colon cancer in view of stromal component. Br J Cancer. 2017, 117(3): 421-431.
65. Qi L, Ding Y. TNK2 as a key drug target for the treatment of metastatic colorectal cancer. Int J Biol Macromol. 2018, 119: 48-52.
66. Jin DH, Lee J, Kim KM, Kim S, Kim DH, Park J. Overexpression of MAPK15 in gastric cancer is associated with copy number gain and contributes to the stability of c-Jun. Oncotarget. 2015, 6(24): 20190-203.
67. Akter K, Lanza EA, Martin SA, Myronyuk N, Rua M, Raffa RB. Diabetes mellitus and Alzheimer′s disease: shared pathology and treatment? Br J Clin Pharmacol. 2011, 71(3): 365-76.
68. Reed B, Villeneuve S, Mack W, DeCarli C, Chui HC, Jagust W. Associations between serum cholesterol levels and cerebral amyloidosis. JAMA Neurol. 2014, 71(2): 195-200.
69. Basu R, Chandramouli V, Dicke B, Landau B, Rizza R. Obesity and type 2 diabetes impair insulin-induced suppression of glycogenolysis as well as gluconeogenesis. Diabetes. 2005, 54(7): 1942-8.
70. Rönnemaa E, Zethelius B, Sundelöf J, Sundström J, Degerman-Gunnarsson M, Berne C, Lannfelt L, Kilander L. Impaired insulin secretion increases the risk of Alzheimer disease. Neurology. 2008, 71(14): 1065-71.
71. Kivipelto M, Ngandu T, Fratiglioni L, Viitanen M, Kåreholt I, Winblad B, Helkala EL, Tuomilehto J, Soininen H, Nissinen A. Obesity and vascular risk factors at midlife and the risk of dementia and Alzheimer disease. Arch Neurol. 2005, 62(10): 1556-60.
72. Lester-Coll N, Rivera EJ, Soscia SJ, Doiron K, Wands JR, de la Monte SM. Intracerebral streptozotocin model of type 3 diabetes: relevance to sporadic Alzheimer′s disease. J Alzheimer’s Dis. 2006, 9(1): 13-33.
73. Abner EL, Nelson PT, Kryscio RJ, Schmitt FA, Fardo DW, Woltjer RL, et al. Diabetes is associated with cerebrovascular but not Alzheimer neuropathology. Alzheimers Dement. 2016, 12(8): 882–889.
74. Ahmed F, Ansari JA, Ansari ZE, Alam Q, Gan SH, Kamal MA, Ahmad E. A molecular bridge: connecting type 2 diabetes and Alzheimer′s disease. CNS Neurol Disord Drug Targets. 2014, 13(2): 312-21.
75. Love JE, Hayden EJ, Rohn TT. Alternative splicing in Alzheimer′s Disease. J Parkinsons Dis Alzheimers Dis. 2015, 2(2): 6.
76. Dlamini Z, Mokoena F, Hull R. Abnormalities in alternative splicing in diabetes: therapeutic targets. J Mol Endocrinol. 2017, 59(2): R93-R107.
77. Bertram L, McQueen MB, Mullin K, Blacker D, Tanzi RE. Systematic meta-analyses of Alzheimer disease genetic association studies: the AlzGene database. Nat Genet. 2007,39(1): 17-23.
78. Sun J, Feng X, Liang D, Duan Y, Lei H. Down-regulation of energy metabolism in Alzheimer′s disease is a protective response of neurons to the microenvironment. J Alzheimers Dis. 2012, 28(2): 389-402.
79. Bai Z, Han G, Xie B, Wang J, Song F, Peng X, Lei H. AlzBase: an integrative database for gene dysregulation in Alzheimer’s disease. Mol Neurobiol. 2016, 53(1): 310-319.
80. Dai HJ, Wu JC, Tsai RT, Pan WH, Hsu WL. T-HOD: a literature-based candidate gene database for hypertension, obesity and diabetes. Database (Oxford). 2013, 2013: bas061.
81. Postula M, Janicki PK, Rosiak M, Eyileten C, Zaremba M, Kaplon-Cieslicka A, Sugino S, Kosior DA, Opolski G, Filipiak KJ, Mirowska-Guzel D. Targeted deep resequencing of ALOX5 and ALOX5AP in patients with diabetes and association of rare variants with leukotriene pathways. Exp Ther Med. 2016, 12(1): 415-421.
82. Nejatian N, Penna-Martinez M, Steinhilber D, Badenhoop K. The association between vitamin D and the arachidonate 5-lipoxygenase (ALOX-5) gene polymorphism in type 2 diabetes. Diabetologie und Stoffwechsel. 2015, 10-P205.
83. Heemskerk MM, Giera M, Bouazzaoui FE, Lips MA, Pijl H, van Dijk KW, van Harmelen V. Increased PUFA Content and 5-Lipoxygenase Pathway Expression Are Associated with Subcutaneous Adipose Tissue Inflammation in Obese Women with Type 2 Diabetes. Nutrients. 2015, 7(9): 7676-90.
84. Greenbaum L, Ravona-Springer R, Lubitz I, Schmeidler J, Cooper I, Sano M, Silverman JM, Heymann A, Beeri MS. Potential contribution of the Alzheimer′s disease risk locus BIN1 to episodic memory performance in cognitively normal Type 2 diabetes elderly. Eur Neuropsychopharmacol. 2016, 26(4): 787-95.
85. Horn S, Kirkegaard JS, Hoelper S, Seymour PA, Rescan C, Nielsen JH, Madsen OD, Jensen JN, Krüger M, Grønborg M, Ahnfelt-Rønne J. Research Resource: A Dual Proteomic Approach Identifies Regulated Islet Proteins During β-Cell Mass Expansion In Vivo. Mol Endocrinol. 2016, 30(1): 133-43.
86. Wu DA, Bu X, Warden CH, Shen DD, Jeng CY, Sheu WH, Fuh MM, Katsuya T, Dzau VJ, Reaven GM, et al. Quantitative trait locus mapping of human blood pressure to a genetic region at or near the lipoprotein lipase gene locus on chromosome 8p22. J Clin Invest. 1996, 97(9): 2111-8.
87. Celikbilek A, Tanik N, Sabah S, Borekci E, Akyol L, Ak H, Adam M, Suher M, Yilmaz N. Elevated neurofilament light chain (NFL) mRNA levels in prediabetic peripheral neuropathy. Mol Biol Rep. 2014, 41(6): 4017-22.
88. Verma SK, Deshmukh V, Liu P, Nutter CA, Espejo R, Hung ML, Wang GS, Yeo GW, Kuyumcu-Martinez MN. Reactivation of fetal splicing programs in diabetic hearts is mediated by protein kinase C signaling. J Biol Chem. 2013, 288(49): 35372-86.
89. Belanger K, Nutter CA, Li J, Tasnim S, Liu P, Yu P, Kuyumcu-Martinez MN. CELF1 contributes to aberrant alternative splicing patterns in the type 1 diabetic heart. Biochem Biophys Res Commun. 2018, 503(4): 3205-3211.
90. Blom ES, Wang Y, Skoglund L, Hansson AC, Ubaldi M, Lourdusamy A, Sommer WH, Mielke M, Hyman BT, Heilig M, et al. Increased mRNA Levels of TCF7L2 and MYC of the Wnt Pathway in Tg-ArcSwe Mice and Alzheimer′s Disease Brain. Int J Alzheimers Dis. 2010, 2011: 936580.
91. Včelák J, Vejražková D, Vaňková M, Lukášová P, Bradnová O, Hálková T, Bešťák J, Andělová K, Kvasničková H, Hoskovcová P, et al. T2D risk haplotypes of the TCF7L2 gene in the Czech population sample: the association with free fatty acids composition. Physiol Res. 2012, 61(3): 229-40.
92. Arefin AS, Mathieson L, Johnstone D, Berretta R, Moscato P. Unveiling clusters of RNA transcript pairs associated with markers of Alzheimer′s disease progression. PLoS One. 2012, 7(9): e45535.
93. Riise J, Plath N, Pakkenberg B, Parachikova A. Aberrant Wnt signaling pathway in medial temporal lobe structures of Alzheimer′s disease. J Neural Transm (Vienna). 2015, 122(9): 1303-18.
94. Vollbach H, Heun R, Morris CM, Edwardson JA, McKeith IG, Jessen F, Schulz A, Maier W, Kölsch H. APOA1 polymorphism influences risk for early-onset nonfamiliar AD. Ann Neurol. 2005, 58(3): 436-41.
95. RRaygani AV, Rahimi Z, Kharazi H, Tavilani H, Pourmotabbed T. Association between apolipoprotein E polymorphism and serum lipid and apolipoprotein levels with Alzheimer′s disease. Neurosci Lett. 2006, 408(1): 68-72.
96. Lin Q, Cao Y, Gao J. Decreased expression of the APOA1-APOC3-APOA4 gene cluster is associated with risk of Alzheimer′s disease. Drug Des Devel Ther. 2015, 9: 5421-31.
97. Pallàs M, Verdaguer E, Jordà EG, Jiménez A, Canudas AM, Camins A. Flavopiridol: an antitumor drug with potential application in the treatment of neurodegenerative diseases. Med Hypotheses. 2005, 64(1): 120-3.
98. Gsell W, Conrad R, Hickethier M, Sofic E, Frölich L, Wichart I, Jellinger K, Moll G, Ransmayr G, Beckmann H, et al. Decreased catalase activity but unchanged superoxide dismutase activity in brains of patients with dementia of Alzheimer type. J Neurochem. 1995, 64(3): 1216-23.
99. Lovell MA, Ehmann WD, Butler SM, Markesbery WR. Elevated thiobarbituric acidreactive substances and antioxidant enzyme activity in the brain in Alzheimer′s disease. Neurology. 1995, 45(8): 1594-601.
100. Kim TH, Hong JM, Oh B, Cho YS, Lee JY, Kim HL, Shin ES, Lee JE, Park EK, Kim SY. Genetic association study of polymorphisms in the catalase gene with the risk of osteonecrosis of the femoral head in the Korean population. Osteoarthritis Cartilage. 2008, 16(9): 1060-6.
101. Mizwicki MT, Liu G, Fiala M, Magpantay L, Sayre J, Siani A, Mahanian M, Weitzman R, Hayden EY, Rosenthal MJ, et al. 1α,25-dihydroxyvitamin D3 and resolvin D1 retune the balance between amyloid-β phagocytosis and inflammation in Alzheimer′s disease patients. J Alzheimers Dis. 2013, 34(1): 155-70.
102. Cifuentes RA, Murillo-Rojas J. Alzheimer′s disease and HLA-A2: linking neurodegenerative to immune processes through an in silico approach. Biomed Res Int. 2014, 2014: 791238.
103. Yang J, Li S, He XB, Cheng C, Le W. Induced pluripotent stem cells in Alzheimer’s disease: applications for disease modeling and cell-replacement therapy. Mol Neurodegener. 2016, 11(1): 39.
104. Xinzhong Li, Jintao Long, Taigang He, Robert Belshaw, and James Scott. Integrated genomic approaches identify major pathways and upstream regulators in late onset Alzheimer’s disease. Sci Rep. 2015, 5: 12393.
105. Engidawork E, Gulesserian T, Yoo BC, Cairns N, Lubec G. Alteration of caspases and apoptosis-related proteins in brains of patients with Alzheimer′s disease. Biochem Biophys Res Commun. 2001, 281(1): 84-93.
106. Reed JC. Mechanisms of apoptosis. Am J Pathol. 2000 Nov;157(5):1415-30.
107. Gopalraj RK, Zhu H, Kelly JF, Mendiondo M, Pulliam JF, Bennett DA, Estus S. Genetic association of low density lipoprotein receptor and Alzheimer′s disease. Neurobiol Aging. 2005, 26(1): 1-7.
108. Bowen RL, Isley JP, Atkinson RL. An association of elevated serum gonadotropin concentrations and Alzheimer disease? J Neuroendocrinol. 2000, 12(4): 351-4.
109. Vingtdeux V, Davies P, Dickson DW, Marambaud P. AMPK is abnormally activated in tangle- and pre-tangle-bearing neurons in Alzheimer′s disease and other tauopathies. Acta Neuropathol. 2011, 121(3): 337-49.
110. Greco SJ, Sarkar S, Johnston JM, Tezapsidis N. Leptin regulates tau phosphorylation and amyloid through AMPK in neuronal cells. Biochem Biophys Res Commun. 2009, 380(1): 98-104.
111. Salminen A, Kaarniranta K, Haapasalo A, Soininen H, Hiltunen M. AMP-activated protein kinase: a potential player in Alzheimer′s disease. J Neurochem. 2011, 118(4): 460-74.
112. Semagacestat | Alzforum. Retrieved April 8, 2019, from https://www.alzforum.org/therapeutics/semagacestat.
113. Verubecestat | Alzforum. Retrieved April 8, 2019, from https://www.alzforum.org/therapeutics/verubecestat.
114. Atabecestat | Alzforum. Retrieved April 8, 2019, from https://www.alzforum.org/therapeutics/atabecestat.
115. Chang Y, Paramasivam M, Girgenti MJ, Walikonis RS, Bianchi E, LoTurco JJ. RanBPM regulates the progression of neuronal precursors through M-phase at the surface of the neocortical ventricular zone. Dev Neurobiol. 2010, 70(1): 1-15.
116. Tufail Y, Cook D, Fourgeaud L, Powers CJ, Merten K, Clark CL, Hoffman E, Ngo A, Sekiguchi KJ, O′Shea CC, et al. Phosphatidylserine Exposure Controls Viral Innate Immune Responses by Microglia. Neuron. 2017, 93(3): 574-586.e8.
117. Rojiani MV, Alidina J, Esposito N, Rojiani AM. Expression of MMP-2 correlates with increased angiogenesis in CNS metastasis of lung carcinoma. Int J Clin Exp Pathol. 2010, 3(8): 775-81.

指導教授

李弘謙(Hoong-Chien Lee)

審核日期

2019-6-21

推文