以疾病進展趨勢挑選基因法識別正常腦老化與阿爾茨海默氏症在特定腦區引發的關鍵功能路徑與調節路徑之變化

簡易檢索 / 詳目顯示

回結果列表

研究生：	湯佳薇 Chia-Wei Tang
論文名稱：	以疾病進展趨勢挑選基因法識別正常腦老化與阿爾茨海默氏症在特定腦區引發的關鍵功能路徑與調節路徑之變化 Identification of key changes in functional pathways and regulatory pathways in normal brain aging and in brain region-specific Alzheimer’s disease using the method of gene-selection by trend of disease progression
指導教授：	李弘謙 Hoong-Chien Lee
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：	68
中文關鍵詞：	阿茲海默症、疾病進展趨勢、挑選基因法、功能路徑、蛋白質體酶、神經營養路徑、Wnt 訊號路徑
外文關鍵詞：	ToP, brain region-specific, functional pathway, method of gene-selection, Wnt signaling pathway, neurotrophin signaling
相關次數：	點閱：20 下載：0
分享至:	分享至facebook 分享至twitter

查詢本校圖書館目錄查詢臺灣博碩士論文知識加值系統勘誤回報

阿茲海默症是一種最常見的漸進性神經退化性疾病。由於蛋白質在腦中堆積形成斑塊和糾結進而導致阿茲海默症。目前研究已知 APP、PSEN1、PSEN2 以及 APOE 這些常見危險因子的基因突變對阿茲海默症有高度致病風險。致病研究的探究雖然已行之多年，但是對於阿茲海默症的發病機制仍然尚未明瞭。因此，我們用一種新的方法-疾病進展趨勢挑選基因法去建構特定的基因-基因相互作用網路，並在AG和AD中的顯著途徑識別樞紐基因以及其重要性。
我們透過微陣列數據尋找造成正常腦老化與阿茲海默症的五個特定腦區的共表達基因對，五個腦區分別為：(entorhinal cortex, EC)、海馬 (hippocampus, HC)、內側顳中回 (medial temporal gyrus, MTG)、後扣帶 (posterior cingulate, PC)、額上回 (superior frontal gyrus, SFG)。並使用疾病進展趨勢挑選基因法去建構疾病和控制組的基因-基因相互作用網路(GGINs)，並將兩種狀態下的網路具有明顯變化的基因作為樞紐基因，最後將這些與樞紐基因有連接的基因進行KEGG路徑分析，所得的路徑在疾病與控制組有顯著的不同。
結果顯示在老化與阿茲海默症中前10的樞紐基因有很明顯的不同，而阿茲海默症五個腦區之間的前10樞紐基因也有所不同。老化的前10名樞紐基因中有些為已知的老化基因，包含：EP300、 MDM2、HSPA8 和 EGFR。同樣的，阿茲海默症五個腦區的前10名樞紐基因也有為已知的阿茲海默基因，包含 GAPDH (EC和PC) 、APP (PC) 和 TUBB (SFG)。
前10樞紐基因所形成的基因網路，其功能路徑結果顯示正常老化與阿茲海默症五個腦區的不同，在正常老化出現 Wnt signaling pathway (hsa04310) 和Ubiquitin mediated proteolysis (hsa04120)等路徑，在阿茲海默症出現Alzheimer’s disease(hsa05010)、pathogenic Escherichia coli infection(hsa0130)、Fc gamma R-mediated pathagocytosis (hsa04666)、proteasome(hsa03050)及 neurotrophin signaling pathway(hsa04722)等的腦區共同致病路徑。

Alzheimer's disease (AD) is one of most prevalent progressive neurodegenerative disease. It is believed that the main pathology of AD is given rise by accumulated neuronal plaques and tangle formations in the brain. Previous studies indicated that abnormally in the genes APP, PSEN1, PSEN2, and APOE are high risk factors to AD. In spite of much research efforts the underlying pathophysiology of AD remains unclear. Here, our goal is to gain new insight into possible genetic causes of AG and brain region-specific AD. We used a novel approach, Gene Selection by Trend of disease Progression (GSToP), to construct state-specific gene-gene interaction networks, and to identify hub genes and significant AG and AD related changes in pathways in which specific hub genes play major roles.
We used for our study six sets of publically available microarray dataset: one set from brains tissues of people of a wide range of ages not known to have AD or similar brain disorders, and five sets from tissues of five brain regions – entorhinal cortex, hippocampus, medial temporal gyrus, posterior cingulate, and superior frontal gyrus – of AD patients and controls. We use the method of trend of disease progression for our study. We used GSToP to construct two – experiment(exp) and control – gene-gene interaction networks (GGINs) for each dataset, identified as hub genes those whose connectivities underwent significant changes in the two networks, and then used the sub-GGINs connected to these hub genes to identify KEGG pathways whose enrichments in experiment and control were significantly different.
There was significant difference between the top-10 hub genes for AG and for AD, as well among the top-10 hub genes for specific AD regions. Known AG target genes found in the top-10 AG hub genes include EP300, MDM2, HSPA8, and EGFR, and known AD target genes separately found in top-10 AD hub genes include GAPDH (in EC and PC), APP (PC), and TUBB (SFG). Many significantly enriched pathways were identified for AG and AD. The relationship between the set of enriched pathways found for AG and those for region-specific AD was heterogenic. Pathways found for AG include Wnt signaling pathway (hsa04310) and ubiquitin mediated proteolysis (hsa04120). Pathways found for AD in at least two brain regions include Alzheimer’s disease (hsa05010), pathogenic Escherichia coli infection (hsa0130), Fc gamma R-mediated pathagocytosis (hsa04666), proteasome (hsa03050), and neurotrophin signaling pathway (hsa04722).

Table of Contents
摘要    i
Abstract    ii
Table of Contents    iv
List of Tables    vi
List of Figures    vii
List of Supplementary Tables    viii
List of Supplementary Figures    ix
Chapter 1. Introduction    1
Chapter 2. Materials and Methods    3
2-1 Materials    3
2-1-1 Gene expression microarray    3
2-1-2 Selection of known AG and AD targets    3
2-2 Methods    3
2-2-1 Microarray data preprocessing    3
2-2-2 Construction of co-expressed gene pairs    4
2-2-3 Construction of gene-gene interaction networks    4
2-2-4 Gene Selection of Trend-of-Disease-Progression (GSToP)    4
2-2-5 Construction of GGIN using GSToP    5
2-2-6 Functional genomics analysis    5
Chapter 3. Results    6
3-1 Co-expressed gene pairs (cox-gene pairs)    6
3-1-1 Cox-gene pairs were selected for all datasets    6
3-1-2 Relative sizes of cox-gene pair do not have set pattern    6
3-2 Gene-gene interaction networks (GGIN)    6
3-2-1 PPIs were screening for all datasets    6
3-2-2 Selection of union gene sets over all datasets    7
3-2-3 Relative sizes of hub genes with all datasets    7
3-2-4 KEGG terms enriched in both GOA and LOA    7
3-2-5 Selected categories of enriched KEEG terms    8
3-3 Trend-of-Disease-Progression for AG and AD    9
3-3-1 Content of known AG target genes in the six hub sets of GGINs    9
3-3-2 Content of known AD target genes in the five hub sets of GGINs    9
3-3-3 Selections of top 10 hub genes do not have set similarity    9
3-3-4 EP300 genes gains activity with age    10
3-3-5 MAPT gains activity with AD progression    11
3-3-6 Unknown AD genes lose activity with AD progression    11
Chapter 4. Discussion    12
Chapter 5. Summary    14
Reference    28
Supplementary Data    34


List of Tables
Table 1. Microarray datasets used in study    15
Table 2. Top hub genes in six GGINs    15
Table 3. Numerical properties of two hub-GGINs and two KEGG pathways enriched in it    16


List of Figures
Figure 1. Schema of a hub-GGIN constructed around a hub gene    17
Figure 2. Selected categories of enriched KEEG terms queried by six sets of GOA genes and six sets of LOA genes, respectively    18
Figure 3. Two-way hierarchical clustering of 59 known AG target genes and the AG and five AD region    19
Figure 4. Two-way hierarchical clustering of 53 known AD target genes and the five AD region    20
Figure 5. Enriched KEEG terms in top-10 hub-GGINs    21
Figure 6. Hub-GGINs of AG cohorts built around the GOA hub gene EP300    22
Figure 7. Sub-networks in the GGINs of AD-HC cohorts built around the GOA hub gene MAPT    23
Figure 8. Wn signaling pathway enriched in the EP300-based hub-GGIN    24
Figure 9. Alzheimer’s disease pathway enriched in the MAPT-based hub-GGIN    25
Figure 10. Proteasome pathway enriched in the PSMA7-based hub-GGIN    26
Figure 11. Neurotrophin signaling pathway enriched in the GRB2-based hub-GGIN    27


List of Supplementary Tables
Table S1. The size of co-expressed genes pairs (cox-gene pairs)    34
Table S2 The size of GGIN    34
Table S3. The size of union gene sets in GGIN    35
Table S4. The size of hub genes in GGIN    35
Table S5. KEGG enrichment in GGIN    36
Table S6. The size of Top 10 hub-GGIN    36
Table S7. KEGG enrichment in hub-GGIN    40
Table S8. Hub genes ranking in the AG-GGIN overlapping with 305 known AG targets genes    45
Table S9. Hub genes ranking in the AD-GGIN overlapping with 210 known AD targets genes    48



List of Supplementary Figures
Figure S1. Two-way hierarchical clustering of microarrays data    53
Figure S2. Hub genes with high changing AG target between AG and regions-specific AD    54








                                

[1] Kerchner GA, Wyss-Coray T (2016) The Role of Aging in Alzheimer’s Disease In Advances in Geroscience Springer, pp. 197-227.
[2] Bukar Maina M, Al-Hilaly YK, Serpell LC (2016) Nuclear Tau and Its Potential Role in Alzheimer’s Disease. Biomolecules 6, 9.
[3] La Rosa LR, Matrone C, Ferraina C, Panico MB, Piccirilli S, Di Certo MG, Strimpakos G, Mercuri NB, Calissano P, D'Amelio M (2013) Age-related changes of hippocampal synaptic plasticity in AβPP-null mice are restored by NGF through p75NTR. J. Alzheimers Dis 33, 265-272.
[4] 台灣失智症協會 (2004) 機構照顧需求之調查 -長期照護機構失智症患者之盛行率調查」研究報告.
[5] (NIA) NIoA (2015) Bypass Budget Proposal for Fiscal Year 2017—Reaching for a Cure: Alzheimers Disease and Related Dementias Research.
[6] Bredesen DE (2015) Metabolic profiling distinguishes three subtypes of Alzheimer's disease. Aging (Albany NY) 7, 595.
[7] Oskarsson ME, Paulsson JF, Schultz SW, Ingelsson M, Westermark P, Westermark GT (2015) In vivo seeding and cross-seeding of localized amyloidosis: a molecular link between type 2 diabetes and Alzheimer disease. The American journal of pathology 185, 834-846.
[8] Suzanne M, Wands JR (2008) Alzheimer's disease is type 3 diabetes—evidence reviewed. Journal of diabetes science and technology 2, 1101-1113.
[9] Pisa D, Alonso R, Rábano A, Rodal I, Carrasco L (2015) Different brain regions are infected with fungi in Alzheimer’s disease. Scientific reports 5.
[10] Cacabelos R, Martinez-Bouza R, Carlos Carril J, Fernandez-Novoa L, Lombardi V, Carrera I, Corzo L, McKay A (2012) Genomics and pharmacogenomics of brain disorders. Current pharmaceutical biotechnology 13, 674-725.
[11] Honea RA, Cruchaga C, Perea RD, Saykin AJ, Burns JM, Weinberger DR, Goate AM, Initiative AsDN (2013) Characterizing the role of brain derived neurotrophic factor genetic variation in Alzheimer’s disease neurodegeneration. PloS one 8, e76001.
[12] Phillips NR, Simpkins JW, Roby RK (2014) Mitochondrial DNA deletions in Alzheimer's brains: A review. Alzheimer's & Dementia 10, 393-400.
[13] Morán M, Moreno-Lastres D, Marín-Buera L, Arenas J, Martín MA, Ugalde C (2012) Mitochondrial respiratory chain dysfunction: implications in neurodegeneration. Free Radical Biology and Medicine 53, 595-609.
[14] Wang X, Wang W, Li L, Perry G, Lee H-g, Zhu X (2014) Oxidative stress and mitochondrial dysfunction in Alzheimer's disease. Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease 1842, 1240-1247.
[15] Manczak M, Park BS, Jung Y, Reddy PH (2004) Differential expression of oxidative phosphorylation genes in patients with Alzheimer’s disease. Neuromolecular medicine 5, 147-162.
[16] Rhein V, Song X, Wiesner A, Ittner LM, Baysang G, Meier F, Ozmen L, Bluethmann H, Dröse S, Brandt U (2009) Amyloid-β and tau synergistically impair the oxidative phosphorylation system in triple transgenic Alzheimer's disease mice. Proceedings of the National Academy of Sciences 106, 20057-20062.
[17] Crimins JL, Pooler A, Polydoro M, Luebke JI, Spires-Jones TL (2013) The intersection of amyloid beta and tau in glutamatergic synaptic dysfunction and collapse in Alzheimer's disease. Ageing research reviews 12, 757-763.
[18] Revett TJ, Baker GB, Jhamandas J, Kar S (2013) Glutamate system, amyloid β peptides and tau protein: functional interrelationships and relevance to Alzheimer disease pathology. Journal of psychiatry & neuroscience: JPN 38, 6.
[19] Kikuchi M, Ogishima S, Mizuno S, Miyashita A, Kuwano R, Nakaya J, Tanaka H (2016) Network-Based Analysis for Uncovering Mechanisms Underlying Alzheimer’s Disease. Systems Biology of Alzheimer's Disease, 479-491.
[20] Campion D, Pottier C, Nicolas G, Le Guennec K, Rovelet-Lecrux A (2016) Alzheimer disease: modeling an Aβ-centered biological network. Molecular psychiatry.
[21] Ferrari R, Forabosco P, Vandrovcova J, Botía JA, Guelfi S, Warren JD, Momeni P, Weale ME, Ryten M, Hardy J (2016) Frontotemporal dementia: insights into the biological underpinnings of disease through gene co-expression network analysis. Molecular neurodegeneration 11, 1.
[22] Bettencourt C, Forabosco P, Wiethoff S, Heidari M, Johnstone DM, Botía JA, Collingwood JF, Hardy J, Milward EA, Ryten M (2016) Gene co-expression networks shed light into diseases of brain iron accumulation. Neurobiology of disease 87, 59-68.
[23] Harel I, Benayoun BA, Machado B, Singh PP, Hu C-K, Pech MF, Valenzano DR, Zhang E, Sharp SC, Artandi SE (2015) A platform for rapid exploration of aging and diseases in a naturally short-lived vertebrate. Cell 160, 1013-1026.
[24] Yue H, Yang B, Yang F, Hu XL, Kong FB (2016) Co‑expression network‑based analysis of hippocampal expression data associated with Alzheimer's disease using a novel algorithm. Experimental and therapeutic medicine 11, 1707-1715.
[25] Reiman EM, Langbaum JB, Tariot PN, Lopera F, Bateman RJ, Morris JC, Sperling RA, Aisen PS, Roses AD, Welsh-Bohmer KA (2015) CAP_advancing the evaluation of preclinical Alzheimer disease treatments. Nature Reviews Neurology.
[26] Hampel H, Schneider LS, Giacobini E, Kivipelto M, Sindi S, Dubois B, Broich K, Nistico R, Aisen PS, Lista S (2015) Advances in the therapy of Alzheimer’s disease: targeting amyloid beta and tau and perspectives for the future. Expert review of neurotherapeutics 15, 83-105.
[27] Giacobini E, Gold G (2013) Alzheimer disease therapy—moving from amyloid-β to tau. Nature Reviews Neurology 9, 677-686.
[28] 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.
[29] 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.
[30] 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.
[31] 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.
[32] Tacutu R, Craig T, Budovsky A, Wuttke D, Lehmann G, Taranukha D, Costa J, Fraifeld VE, De Magalhães JoP (2012) Human ageing genomic resources: integrated databases and tools for the biology and genetics of ageing. Nucleic acids research, gks1155.
[33] Smyth GK (2005) Limma: linear models for microarray data In Bioinformatics and computational biology solutions using R and Bioconductor Springer, pp. 397-420.
[34] 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.
[35] 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.
[36] Wood F (2009) Principal component analysis.
[37] Yeung KY, Ruzzo WL (2001) Principal component analysis for clustering gene expression data. Bioinformatics 17, 763-774.
[38] Prasad TK, Goel R, Kandasamy K, Keerthikumar S, Kumar S, Mathivanan S, Telikicherla D, Raju R, Shafreen B, Venugopal A (2009) Human protein reference database—2009 update. Nucleic acids research 37, D767-D772.
[39] Consortium U (2014) Activities at the universal protein resource (UniProt). Nucleic acids research 42, 7486.
[40] Chung F-H, Lee HH-C, Lee H-C (2013) ToP: a trend-of-disease-progression procedure works well for identifying cancer genes from multi-state cohort gene expression data for human colorectal cancer. PloS one 8, e65683.
[41] Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T, Kawashima S, Okuda S, Tokimatsu T (2008) KEGG for linking genomes to life and the environment. Nucleic acids research 36, D480-D484.
[42] Butterfield DA, Hardas SS, Lange MLB (2010) Oxidatively modified glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and Alzheimer's disease: many pathways to neurodegeneration. Journal of Alzheimer's Disease 20, 369-393.
[43] Sunaga K, Takahashi H, Chuang D-M, Ishitani R (1995) Glyceraldehyde-3-phosphate dehydrogenase is over-expressed during apoptotic death of neuronal cultures and is recognized by a monoclonal antibody against amyloid plaques from Alzheimer's brain. Neuroscience letters 200, 133-136.
[44] Silva PN, Furuya TK, Braga IL, Rasmussen LT, Labio RW, Bertolucci PH, Chen ES, Turecki G, Mechawar N, Payao SL (2014) Analysis of HSPA8 and HSPA9 mRNA expression and promoter methylation in the brain and blood of Alzheimer's disease patients. Journal of Alzheimer's disease 38, 165-170.
[45] Cacabelos R, Torrellas C, López-Muñoz F (2014) Epigenomics of Alzheimer's disease. Journal of Experimental & Clinical Medicine 6, 75-82.
[46] Chiasserini D, van Weering JR, Piersma SR, Pham TV, Malekzadeh A, Teunissen CE, de Wit H, Jiménez CR (2014) Proteomic analysis of cerebrospinal fluid extracellular vesicles: a comprehensive dataset. Journal of proteomics 106, 191-204.
[47] Zhang M, Huang K, Zhang Z, Ji B, Zhu H, Zhou K, Li Y, Yang J, Sun L, Wei Z (2011) Proteome alterations of cortex and hippocampus tissues in mice subjected to vitamin A depletion. The Journal of nutritional biochemistry 22, 1003-1008.
[48] Ding Q, Markesbery WR, Chen Q, Li F, Keller JN (2005) Ribosome dysfunction is an early event in Alzheimer's disease. The Journal of neuroscience 25, 9171-9175.
[49] Hernández‐Ortega K, Garcia‐Esparcia P, Gil L, Lucas JJ, Ferrer I (2015) Altered machinery of protein synthesis in Alzheimer's: from the nucleolus to the ribosome. Brain Pathology.
[50] Roy K, Chakrabarti O, Mukhopadhyay D (2014) Interaction of Grb2 SH3 domain with UVRAG in an Alzheimer’s disease–like scenario. Biochemistry and Cell Biology 92, 219-225.
[51] Lecker SH, Goldberg AL, Mitch WE (2006) Protein degradation by the ubiquitin–proteasome pathway in normal and disease states. Journal of the American Society of Nephrology 17, 1807-1819.
[52] Lebed YV, Dosenko VE, Skibo GG (2011) Expression of Proteasome Subunits PSMB5 and PSMB9 mRNA in Hippocampal Neurons: Link with Apoptosis and Necrosis. International Journal of Physiology and Pathophysiology 2.
[53] Hofmann JW, McBryan T, Adams PD, Sedivy JM (2014) The effects of aging on the expression of Wnt pathway genes in mouse tissues. Age 36, 1033-1040.
[54] Boyken J, Grønborg M, Riedel D, Urlaub H, Jahn R, Chua JJE (2013) Molecular profiling of synaptic vesicle docking sites reveals novel proteins but few differences between glutamatergic and GABAergic synapses. Neuron 78, 285-297.
[55] Ginsberg SD, Che S, Counts SE, Mufson EJ (2006) Shift in the ratio of three‐repeat tau and four‐repeat tau mRNAs in individual cholinergic basal forebrain neurons in mild cognitive impairment and Alzheimer's disease. Journal of neurochemistry 96, 1401-1408.
[56] Mori H, Kondo J, Ihara Y (1987) Ubiquitin is a component of paired helical filaments in Alzheimer's disease. Science 235, 1641-1644.
[57] Ehlers MD (2003) Activity level controls postsynaptic composition and signaling via the ubiquitin-proteasome system. Nature neuroscience 6, 231-242.
[58] Riederer BM, Leuba G, Vernay A, Riederer IM (2011) The role of the ubiquitin proteasome system in Alzheimer's disease. Experimental Biology and Medicine 236, 268-276.
[59] Nguyen MD, Julien J-P, Rivest S (2002) Innate immunity: the missing link in neuroprotection and neurodegeneration? Nature Reviews Neuroscience 3, 216-227.
[60] Kumar DKV, Choi SH, Washicosky KJ, Eimer WA, Tucker S, Ghofrani J, Lefkowitz A, McColl G, Goldstein LE, Tanzi RE (2016) Amyloid-β peptide protects against microbial infection in mouse and worm models of Alzheimer’s disease. Science translational medicine 8, 340ra372-340ra372.
[61] Amor S, Puentes F, Baker D, Van Der Valk P (2010) Inflammation in neurodegenerative diseases. Immunology 129, 154-169.
[62] Nicolson GL (2008) Chronic bacterial and viral infections in neurodegenerative and neurobehavioral diseases. Laboratory Medicine 39, 291-299.
[63] 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.
[64] 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.
[65] 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.

簡易檢索 / 詳目顯示

相關論文