六種複雜疾病：第二型糖尿病，慢性腎臟病，阿茲海默症，甲狀腺癌，高雪氏症以及多發性硬化症之 共同與獨特的功能基因組和分子特徵

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姓名

田雅馨(Ya-Hsin Tien) 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

六種複雜疾病：第二型糖尿病，慢性腎臟病，阿茲海默症，甲狀腺癌，高雪氏症以及多發性硬化症之共同與獨特的功能基因組和分子特徵
(Common and unique functional genomic and molecular features of six complex diseases: type 2 diabetes, chronic kidney disorder, Alzheimer’s disease, thyroid cancer, Gaucher disease, and multiple sclerosis)

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摘要(中)

複雜疾病，如癌症、神經退化性疾病、糖尿病以及心血管疾病等都和數千個基因間的相互作用改變有著很密切的關係。雖然許多遺傳和環境因素上的變異都跟複雜疾病有關，但與這些疾病有關的遺傳病因仍然是很大的未知。在此，我們收集19組基因表現量數據集包含了6種複雜疾病(第二型糖尿病、慢性腎臟病、阿茲海默症、甲狀腺癌、高雪氏症以及多發性硬化症)進行功能基因組分析，探索疾病功能的相似性與差異性，並鑑定跟功能相關的基因型獨特重要基因。我們的研究結果提出了許多實驗驗證文獻，並為六種複雜疾病的預防、診斷和治療提供了有用的信息。

摘要(英)

Complex diseases such as cancer, neurodegenerative disorders, diabetes mellitus, and cardiovascular disease are associated with altered interactions between thousands of genes. Although variants in many genetic and environmental factors have been associated with complex diseases, inter-disease relations remains largely unexplored. Here, based on nineteen gene expression datasets on six complex diseases – type 2 diabetes, chronic kidney disorder, Alzheimer’s disease, thyroid cancer, Gaucher disease, and multiple sclerosis – we construct functional genomic analysis of the diseases, explore functional similarity/dissimilarity among the diseases, and identify function-associated genotype-unique significant genes for the diseases. Our results suggest many validating experiments and provide useful information for the prevention, diagnostic, and treatment of the six complex diseases.

關鍵字(中)

★ 複雜疾病
★ 基因表達微陣列分析
★ 功能基因組學
★ 疾病-疾病比較
★ 基因型獨特顯著基因

關鍵字(英)

★ Complex diseases
★ gene expression microarray analysis
★ functional genomics
★ disease-disease comparison
★ genotype-unique significant genes

論文目次

中文摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 vii
第一章、緒論 1
1.1 複雜疾病 1
1.2 基因表達與調控 4
1.3 微陣列分析 6
1.4 功能基因組學與複雜疾病 8
1.5 研究目標 9
第二章、材料與方法 13
2.1 複雜疾病數據組與前處理 13
2.2 分子標籤資料庫 15
2.3 基因集富集分析 16
2.3.1 ES值 16
2.3.2 NES值 17
2.3.3 名義p值 18
2.3.4 前緣子集 18
2.4 KEGG生物路徑的富集分析 19
2.5 挑選分子標籤的門檻與篩選顯著分子標籤 19
2.6 疾病與顯著分子標籤的雙向集群分析 19
2.7 疾病特異性集群基因集的建立 20
2.8 相似係數 20
第三章、研究結果 21
3.1 19組數據集與191個顯著分子標籤的雙向集群 21
3.2 篩選四大集群前緣子集基因 22
3.3 各集群前緣子集基因的KEGG術語富集強度趨勢 23
3.4 篩選基因型獨特的分子標籤 25
3.5 八種基因型獨特分子標籤集群與191個顯著分子標籤集群的結果相似 27
3.6 八種基因型前緣子集基因的交集 28
3.7 篩選基因型特異性高頻前緣子集基因進行KEGG分析 30
3.8 基因型特異性高頻前緣子集基因在KEGG術語中的富集強度 31
3.9 篩選基因型獨特顯著基因 34
3.10 七大基因型獨特顯著基因與KEGG術語網路圖譜 37
第四章、討論 40
第五章、總結 46
參考文獻 47
附錄 53

參考文獻

1. Sachidanandam, R., et al., A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms. Nature, 2001. 409(6822): p. 928-933.
2. Sherry, S.T., et al., dbSNP: the NCBI database of genetic variation. Nucleic acids research, 2001. 29(1): p. 308-311.
3. Klein, R.J., et al., Complement factor H polymorphism in age-related macular degeneration. Science, 2005. 308(5720): p. 385-389.
4. van der Sijde, M.R., A. Ng, and J. Fu, Systems genetics: From GWAS to disease pathways. Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease, 2014. 1842(10): p. 1903-1909.
5. Huang, Q., Genetic study of complex diseases in the post-GWAS era. Journal of Genetics and Genomics, 2015. 42(3): p. 87-98.
6. Pranavchand, R. and B. Reddy, Genomics era and complex disorders: Implications of GWAS with special reference to coronary artery disease, type 2 diabetes mellitus, and cancers. Journal of postgraduate medicine, 2016. 62(3): p. 188.
7. Goh, K.-I., et al., The human disease network. Proceedings of the National Academy of Sciences, 2007. 104(21): p. 8685-8690.
8. Cookson, W., et al., Mapping complex disease traits with global gene expression. Nature Reviews Genetics, 2009. 10(3): p. 184-194.
9. Blair, D.R., et al., A nondegenerate code of deleterious variants in Mendelian loci contributes to complex disease risk. Cell, 2013. 155(1): p. 70-80.
10. Khan, J., et al., Expression profiling in cancer using cDNA microarrays. Electrophoresis, 1999. 20(2): p. 223-229.
11. Nam, D. and S.-Y. Kim, Gene-set approach for expression pattern analysis. Briefings in bioinformatics, 2008. 9(3): p. 189-197.
12. Subramanian, A., et al., Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 2005. 102(43): p. 15545-15550.
13. Winkelmann, B.R., et al., Rationale and design of the LURIC study-a resource for functional genomics, pharmacogenomics and long-term prognosis of cardiovascular disease. Pharmacogenomics, 2001. 2(1): p. S1-S73.
14. Liu, E.T., Functional genomics of cancer. Current opinion in genetics & development, 2008. 18(3): p. 251-256.
15. Sabatini, M., Functional genomics reveals serine synthesis is essential in PHGDH-amplified breast cancer. Nature. 476(7360): p. 346-350.
16. Cacabelos, R., et al., Phenotypic profiles and functional genomics in Alzheimer′s disease and in dementia with a vascular component. Neurological research, 2004. 26(5): p. 459-480.
17. Wang, X., M. L Michaelis, and E. K Michaelis, Functional genomics of brain aging and Alzheimer′s disease: focus on selective neuronal vulnerability. Current genomics, 2010. 11(8): p. 618-633.
18. Wang, X., et al., Identification of a molecular signature in human type 1 diabetes mellitus using serum and functional genomics. The Journal of Immunology, 2008. 180(3): p. 1929-1937.
19. Pattaro, C., et al., Genome-wide association and functional follow-up reveals new loci for kidney function. PLoS genetics, 2012. 8(3): p. e1002584.
20. Afkarian, M., et al., Clinical manifestations of kidney disease among US adults with diabetes, 1988-2014. Jama, 2016. 316(6): p. 602-610.
21. Akter, K., et al., Diabetes mellitus and Alzheimer′s disease: shared pathology and treatment? British journal of clinical pharmacology, 2011. 71(3): p. 365-376.
22. Giovannucci, E., et al., Diabetes and cancer: a consensus report. CA: a cancer journal for clinicians, 2010. 60(4): p. 207-221.
23. Organization, W.H., Global Health Estimates 2016: Deaths by cause, age, sex, by country and by region, 2000–2016. 2018.
24. Beck-Nielsen, H. and L.C. Groop, Metabolic and genetic characterization of prediabetic states. Sequence of events leading to non-insulin-dependent diabetes mellitus. The Journal of clinical investigation, 1994. 94(5): p. 1714-1721.
25. Kahn, C.R., Insulin action, diabetogenes, and the cause of type II diabetes. Diabetes, 1994. 43(8): p. 1066-1085.
26. Li, Y., et al., Induction of long-term glycemic control in newly diagnosed type 2 diabetic patients is associated with improvement of β-cell function. Diabetes care, 2004. 27(11): p. 2597-2602.
27. Ripsin, C.M., H. Kang, and R.J. Urban, Management of blood glucose in type 2 diabetes mellitus. Am Fam Physician, 2009. 79(1): p. 29-36.
28. Pasquier, F., Diabetes and cognitive impairment: how to evaluate the cognitive status? Diabetes & metabolism, 2010. 36: p. S100-S105.
29. Organization, W.H., Global status report on noncommunicable diseases 2010. 2011: Geneva: World Health Organization.
30. Ballard C, G.S., Corbett A, Brayne C, Aarsland D, Jones E., Alzheimer′s disease. Lancet, 2011.
31. Hashimoto, M., et al., Role of protein aggregation in mitochondrial dysfunction and neurodegeneration in Alzheimer’s and Parkinson’s diseases. Neuromolecular medicine, 2003. 4(1-2): p. 21-35.
32. Davies, F., et al., Polycystic kidney disease re-evaluated: a population-based study. QJM: An International Journal of Medicine, 1991. 79(3): p. 477-485.
33. Gabow, P.A., Autosomal dominant polycystic kidney disease. New England Journal of Medicine, 1993. 329(5): p. 332-342.
34. Levy, M. and J. Feingold, Estimating prevalence in single-gene kidney diseases progressing to renal failure. Kidney international, 2000. 58(3): p. 925-943.
35. Mochizuki, T., et al., PKD2, a gene for polycystic kidney disease that encodes an integral membrane protein. Science, 1996. 272(5266): p. 1339-1342.
36. Zhou, J., Polycystins and primary cilia: primers for cell cycle progression. Annual review of physiology, 2009. 71: p. 83-113.
37. Hateboer, N., et al., Comparison of phenotypes of polycystic kidney disease types 1 and 2. The Lancet, 1999. 353(9147): p. 103-107.
38. Vos, T., et al., Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. The Lancet, 2016. 388(10053): p. 1545-1602.
39. Ron, E., et al., Multiple primary breast and thyroid cancer. British journal of cancer, 1984. 49(1): p. 87.
40. Cormand, B., et al., Genetic fine localization of the β-glucocerebrosidase (GBA) and prosaposin (PSAP) genes: implications for Gaucher disease. Human genetics, 1997. 100(1): p. 75-79.
41. Nagral, A., Gaucher disease. Journal of clinical and experimental hepatology, 2014. 4(1): p. 37-50.
42. Langeveld, M., et al., Overweight, insulin resistance and type II diabetes in type I Gaucher disease patients in relation to enzyme replacement therapy. Blood Cells, Molecules, and Diseases, 2008. 40(3): p. 428-432.
43. Langeveld, M., et al., Type I Gaucher disease, a glycosphingolipid storage disorder, is associated with insulin resistance. The Journal of Clinical Endocrinology & Metabolism, 2008. 93(3): p. 845-851.
44. Calabresi, P.A., Diagnosis and management of multiple sclerosis. American family physician, 2004. 70(10): p. 1935-1944.
45. Edgar, R., M. Domrachev, and A.E. Lash, Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic acids research, 2002. 30(1): p. 207-210.
46. Irizarry, R.A., et al., Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 2003. 4(2): p. 249-264.
47. Abdi, H. and L.J. Williams, Principal component analysis. Wiley interdisciplinary reviews: computational statistics, 2010. 2(4): p. 433-459.
48. Team, R.C., R language definition. Vienna, Austria: R foundation for statistical computing, 2000.
49. Dominguez, V., et al., Class II phosphoinositide 3-kinase regulates exocytosis of insulin granules in pancreatic β cells. Journal of Biological Chemistry, 2011. 286(6): p. 4216-4225.
50. Woroniecka, K.I., et al., Transcriptome analysis of human diabetic kidney disease. Diabetes, 2011: p. DB_101181.
51. Song, X., et al., Systems biology of autosomal dominant polycystic kidney disease (ADPKD): computational identification of gene expression pathways and integrated regulatory networks. Human molecular genetics, 2009. 18(13): p. 2328-2343.
52. Liang, W.S., et al., Alzheimer′s disease is associated with reduced expression of energy metabolism genes in posterior cingulate neurons. Proceedings of the National Academy of Sciences, 2008. 105(11): p. 4441-4446.
53. Giordano, T.J., et al., Molecular classification of papillary thyroid carcinoma: distinct BRAF, RAS, and RET/PTC mutation-specific gene expression profiles discovered by DNA microarray analysis. Oncogene, 2005. 24(44): p. 6646.
54. Gilli, F., et al., Learning from nature: pregnancy changes the expression of inflammation-related genes in patients with multiple sclerosis. PLoS One, 2010. 5(1): p. e8962.
55. Liberzon, A., et al., Molecular signatures database (MSigDB) 3.0. Bioinformatics, 2011. 27(12): p. 1739-1740.
56. Liberzon, A., et al., The molecular signatures database hallmark gene set collection. Cell systems, 2015. 1(6): p. 417-425.
57. Kanehisa, M. and S. Goto, KEGG: kyoto encyclopedia of genes and genomes. Nucleic acids research, 2000. 28(1): p. 27-30.
58. Huang, D.W., B.T. Sherman, and R.A. Lempicki, Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature protocols, 2008. 4(1): p. 44.
59. Huang, D.W., B.T. Sherman, and R.A. Lempicki, Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic acids research, 2008. 37(1): p. 1-13.
60. Wagner, E.F. and A.R. Nebreda, Signal integration by JNK and p38 MAPK pathways in cancer development. Nature Reviews Cancer, 2009. 9(8): p. 537.
61. Sriram, G. and R.B. Birge, Emerging roles for crk in human cancer. Genes & cancer, 2010. 1(11): p. 1132-1139.
62. Pivonello, R., et al., Central diabetes insipidus and autoimmunity: relationship between the occurrence of antibodies to arginine vasopressin-secreting cells and clinical, immunological, and radiological features in a large cohort of patients with central diabetes insipidus of known and unknown etiology. The Journal of Clinical Endocrinology & Metabolism, 2003. 88(4): p. 1629-1636.
63. Acosta, J.C., et al., Chemokine signaling via the CXCR2 receptor reinforces senescence. Cell, 2008. 133(6): p. 1006-1018.
64. Tutunea-Fatan, E., et al., The role of CCL21/CCR7 chemokine axis in breast cancer-induced lymphangiogenesis. Molecular cancer, 2015. 14(1): p. 35.
65. Federico, A., et al., Mitochondria, oxidative stress and neurodegeneration. Journal of the neurological sciences, 2012. 322(1): p. 254-262.
66. Celik, S., et al., A probabilistic approach to using big data reveals Complex I as a potential Alzheimer′s disease therapeutic target. bioRxiv, 2018: p. 302737.
67. Kundra, R., et al., Protein homeostasis of a metastable subproteome associated with Alzheimer’s disease. Proceedings of the National Academy of Sciences, 2017: p. 201618417.
68. Hu, H., et al., Genome-wide association study identified ATP6V1H locus influencing cerebrospinal fluid BACE activity. BMC medical genetics, 2018. 19(1): p. 75.
69. Buchheit, C.L., K.J. Weigel, and Z.T. Schafer, Cancer cell survival during detachment from the ECM: multiple barriers to tumour progression. Nature Reviews Cancer, 2014. 14(9): p. 632.
70. Pelsers, M.M., Fatty acid?binding protein as marker for renal injury. Scandinavian Journal of Clinical and Laboratory Investigation, 2008. 68(sup241): p. 73-77.
71. Xu, Y., et al., L-FABP: A novel biomarker of kidney disease. Clinica Chimica Acta, 2015. 445: p. 85-90.
72. Xia, C., X. Rao, and J. Zhong, Role of T lymphocytes in type 2 diabetes and diabetes-associated inflammation. Journal of diabetes research, 2017. 2017.
73. Ueda, S., et al., Crucial role of the small GTPase Rac1 in insulin-stimulated translocation of glucose transporter 4 to the mouse skeletal muscle sarcolemma. The FASEB Journal, 2010. 24(7): p. 2254-2261.
74. Hedman, A.C., J.M. Smith, and D.B. Sacks, The biology of IQGAP proteins: beyond the cytoskeleton. EMBO reports, 2015. 16(4): p. 427-446.
75. Wolf, G., Cell cycle regulation in diabetic nephropathy. Kidney International, 2000. 58: p. S59-S66.
76. Campeau, P.M., et al., Characterization of Gaucher disease bone marrow mesenchymal stromal cells reveals an altered inflammatory secretome. Blood, 2009. 114(15): p. 3181-3190.
77. Moran, M.T., et al., Pathologic gene expression in Gaucher disease: up-regulation of cysteine proteinases including osteoclastic cathepsin K. Blood, 2000. 96(5): p. 1969-1978.
78. Batta, G., et al., Alterations in the properties of the cell membrane due to glycosphingolipid accumulation in a model of Gaucher disease. Scientific reports, 2018. 8(1): p. 157.
79. Fodil, N., D. Langlais, and P. Gros, Primary immunodeficiencies and inflammatory disease: a growing genetic intersection. Trends in immunology, 2016. 37(2): p. 126-140.
80. Kawamata, H. and G. Manfredi, Proteinopathies and OXPHOS dysfunction in neurodegenerative diseases. J Cell Biol, 2017: p. jcb. 201709172.
81. Wang, H. and A.J. Saunders, The role of ubiquitin-proteasome in the metabolism of amyloid precursor protein (APP): implications for novel therapeutic strategies for Alzheimer’s disease. Discov Med, 2014. 18(97): p. 41-50.
82. Salloway, S., et al., Two phase 3 trials of bapineuzumab in mild-to-moderate Alzheimer′s disease. New England Journal of Medicine, 2014. 370(4): p. 322-333.
83. Mullard, A., Alzheimer amyloid hypothesis lives on. 2016, Nature Publishing Group.

指導教授

李弘謙(Hoong-Chien Lee)

審核日期

2018-7-27

推文