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

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：48

、訪客IP：18.226.187.210

姓名

彭伊筠(Yi-Yun Peng) 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

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
(Changes in functional pathways and hub-gene subnetworks derived from differential co-expression gene pairs in normal brain aging and region-specific Alzheimer’s disease)

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	★ 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

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)

阿茲海默症是由於一種蛋白質在腦堆積形成斑塊和糾結而導致一種漸進性的神經退行性疾病。目前研究已知APP、PSEN1、PSEN2以及APOE這些常見危險因子之基因突變與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，我們透過微陣列數據尋找正常腦老化以及因阿茲海默症病患特定五個腦區之差異共表達(DEC)基因對，五個腦區分別為:嗅皮層(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)。利用差異共表達基因對分析，辨別基因與基因之間的關聯-共表達增加 (GOC) 或共表達失去(LOC)。接著利用蛋白質資料庫與差異共表達基因對交集，建立出具有蛋白質交互作用的核心基因網路。
結果顯示在老化以及五個阿茲海默症不同腦區核心基因網路的基因有顯著的不同。老化的基因GOC數量大於LOC的數量，但是阿茲海默症因腦區而有所不同。在核心基因網路中阿茲海默症之不同腦區的樞紐基因也為已經知道與阿茲海默症有相關的基因，包含: TUBB、GAPDH、NEFL和AMPH。同樣的許多老化組的樞紐基因也為已經知道與老化有相關的基因，包含: YWHAZ, HSPAB, EEF2, 和HIF1A。在老化組研究當中也發現YWHAZ和XRCC6為老化組和阿茲海默症組都有的樞紐基因。
在功能路徑的結果顯示正常老化和阿茲海默症五個腦區的不同，例如:阿茲海默症病人的海馬回 (HC) 中並未發現任何異常癌症相關的路徑，但是在老化和後扣帶區(PC)顯著表現。大多數的功能路徑不是屬於GOC就是屬於LOC，但是也有少數例外:蛋白酶體以及大腸桿菌相關路經，它們同時在GOC和LOC中都有出現。

摘要(英)

Alzheimer′s disease (AD) is one of most prevalent progressive neurodegenerative disease. It is believed that main pathology of AD in brain is due to accumulated neuronal plaque and tangle formations. Previous studies indicated that abnormally in the genes APP, PSEN1, PSEN2, and APOE are high risk factors to AD. Recent reports have also suggested that infec-tions from foreign pathogens may cause AD. In spite of much research efforts the underlying pathophysiology of AD remains unclear. Here, our goal is to gain insight into possible new genetic causes of normal brain aging (AG) and brain region-specific AD, Also, to identify changes in functional pathways associated with AG and region-specific AD, and to elaborate on the heterogeneity of AD in five brain regions.
The data we use 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 differential co-expression (DCE) analysis for our study. We identified pairs of genes whose correlation of expression levels in control versus test data in-creased significantly (gain of co-expression, or GOC), and those whose correlation decreased significantly (loss of co-expression, or LOC), integrated all the genes involved in GOC and LOC pairs with protein-protein interaction data to construct six core protein-protein interac-tion networks (cPPINs), one for each set of data, and used the networks for hub-gene identifi-cation (high-degree genes in cPPIN) and for pathway studies.
There was significant difference between the AG cPPIN and the AD cPPINs, and among regional AD cPPINs. The number of GOC genes greater than LOC genes in the AG cPPIN, but the GOC to LOC ratio is highly region-dependent in the AD networks.
Many of hub-genes in the AD cPPINs were also known AD target genes, including TUBB, GAPDH, NEFL, and AMPH. Similarly, many of hub-genes in the AG cPPIN were known AG target genes, including YWHAZ, HSPAB, EEF2, and HIF1A. YWHAZ and XRCC6 were among gene that were AG as well as AD hub-genes and were both AD and AG known target genes.
Classes of enriched pathways differed between AG and AD, and intra-regionally in AD. For instance, cancer pathways were seen to be highly enriched in AG, and PC/AD, but absent in HC/AD. Most pathways were either GOC or LOC enriched, but some, including pro-teasome and E. coli related pathways, were both GOC and LOC enriched.

關鍵字(中)

★ 老化
★ 阿茲海默症
★ 共表達差異基因對

關鍵字(英)

★ aging
★ Alzheimer’s disease
★ co-expression gene pairs

論文目次

摘要 i
Abstract ii
Acknowledgements iii
Table of Contents iv
Tables vi
Figures vii
Supplementary Tables viii
Supplementary Figures ix
Chapter 1. Introduction 1
Chapter 2. Materials and Methods 3
2-1 Materials 3
2-1-1 Gene expression microarray 3
2-2 Data preprocessing 4
2-2-1 Microarray data preprocessing 4
2-3 Methods 4
2-3-1 Selection of differentially co-expressed gene pairs 4
2-3-2 Mapping and construction of protein-protein interaction networks 6
2-3-3 Functional Profiling of cPPINs 6
2-3-4 Construction of known AG and AD genes related subnetworks 6
Chapter 3. Results 7
3-1 Differentially co-expressed (DC) gene pairs 7
3-1-1 DC gene pairs were selected for all datasets 7
3-1-2 Relative sizes of GOC and LOC do not have set pattern 7
3-1-3 Degrees of top hub genes vary greatly among datasets 7
3-1-4 Relative sizes of GOC and LOC with five major culprits 7
3-2 Protein-protein interaction (PPI) networks 8
3-2-1 PPIs were screening for all datasets 9
3-3 Functional genomics profiling 11
3-3-1 AG, but not AD, has significant elevated risks in cancers and brain inflammation 14
3-3-2 Proteasome (PSM) related functions are both positive and negative enriched 17
3-4 AG and AD regulatory subnetworks 18
3-4-1 β-tubulin class as a candidate roles in AD 19
3-4-2 Known AG targets enrichment in regions-specific AD 22
Chapter 4 Discussion 24
Chapter 5 Summary 26
Reference 27
Supplementary data 32

參考文獻

[1] López-Otín C, Blasco MA, Partridge L, Serrano M, Kroemer G (2013) The hallmarks of aging. Cell 153, 1194-1217.
[2] Wang J, Zhang S, Wang Y, Chen L, Zhang X-S (2009) Disease-aging network reveals significant roles of aging genes in connecting genetic diseases. PLoS Comput Biol 5, e1000521-e1000521.
[3] Huang H-C, Jiang Z-F (2008) Accumulated amyloid-beta peptide and hyperphosphorylated tau protein: relationship and links in Alzheimer′s disease. Journal of Alzheimer′s disease: JAD 16, 15-27.
[4] Hardy JA, Higgins GA (1992) Alzheimer′s disease: the amyloid cascade hypothesis. Science 256, 184.
[5] (NIA) NIoA (2015) Bypass Budget Proposal for Fiscal Year 2017—Reaching for a Cure: Alzheimers Disease and Related Dementias Research.
[6] Association TAsD (2012) Annual Report.
[7] Bruni A (1997) Cloning of a gene bearing missense mutations in early onset familial Alzheimer′s disease: a Calabrian study. Functional neurology 13, 257-261.
[8] Levy-Lahad E, Wasco W, Poorkaj P, Romano DM (1995) Candidate gene for the chromosome 1 familial Alzheimer′s disease locus. Science 269, 973.
[9] Golde TE, Schneider LS, Koo EH (2011) Anti-aβ therapeutics in Alzheimer′s disease: the need for a paradigm shift. Neuron 69, 203-213.
[10] Berchtold NC, Coleman PD, Cribbs DH, Rogers J, Gillen DL, Cotman CW (2013) Synaptic genes are extensively downregulated across multiple brain regions in normal human aging and Alzheimer′s disease. Neurobiology of aging 34, 1653-1661.
[11] Wang X, Michaelis ML, Michaelis EK (2010) Functional genomics of brain aging and Alzheimer’s disease: focus on selective neuronal vulnerability. Current genomics 11, 618.
[12] Saetre P, Jazin E, Emilsson L (2011) Age‐related changes in gene expression are accelerated in Alzheimer′s disease. Synapse 65, 971-974.
[13] Chu G, Narasimhan B, Tibshirani R, Tusher V (2002) SAM,“Significance Analysis of Microarrays”: Users Guide and Technical Document. Stanford University.
[14] Avramopoulos D, Szymanski M, Wang R, Bassett S (2011) Gene expression reveals overlap between normal aging and Alzheimer′s disease genes. Neurobiology of aging 32, 2319. e2327-2319. e2334.
[15] 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.
[16] Swerdlow RH (2011) Brain aging, Alzheimer′s disease, and mitochondria. Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease 1812, 1630-1639.
[17] Sekar S, McDonald J, Cuyugan L, Aldrich J, Kurdoglu A, Adkins J, Serrano G, Beach TG, Craig DW, Valla J (2015) Alzheimer′s disease is associated with altered expression of genes involved in immune response and mitochondrial processes in astrocytes. Neurobiology of aging 36, 583-591.
[18] Hong L, Huang H-C, Jiang Z-F (2014) Relationship between amyloid-beta and the ubiquitin-proteasome system in Alzheimer′s disease. Neurological research 36, 276-282.
[19] Steinlein OK (2012) Ion channel mutations in neuronal diseases: a genetics perspective. Chemical reviews 112, 6334-6352.
[20] 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.
[21] Mondragón-Rodríguez S, Perry G, Zhu X, Boehm J (2012) Amyloid beta and tau proteins as therapeutic targets for Alzheimer’s disease treatment: rethinking the current strategy. International Journal of Alzheimer’s Disease 2012.
[22] 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.
[23] Smyth GK (2005) Limma: linear models for microarray data In Bioinformatics and computational biology solutions using R and Bioconductor Springer, pp. 397-420.
[24] 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.
[25] 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.
[26] Wood F (2009) Principal component analysis.
[27] Yeung KY, Ruzzo WL (2001) Principal component analysis for clustering gene expression data. Bioinformatics 17, 763-774.
[28] 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.
[29] 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.
[30] Consortium U (2014) Activities at the universal protein resource (UniProt). Nucleic acids research 42, 7486.
[31] 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.
[32] 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.
[33] 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.
[34] 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.
[35] 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.
[36] 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.
[37] Speese SD, Trotta N, Rodesch CK, Aravamudan B, Broadie K (2003) The ubiquitin proteasome system acutely regulates presynaptic protein turnover and synaptic efficacy. Current Biology 13, 899-910.
[38] Keller J, Huang F, Markesbery W (2000) Decreased levels of proteasome activity and proteasome expression in aging spinal cord. Neuroscience 98, 149-156.
[39] Hyun DH, Lee M, Halliwell B, Jenner P (2003) Proteasomal inhibition causes the formation of protein aggregates containing a wide range of proteins, including nitrated proteins. Journal of neurochemistry 86, 363-373.
[40] Almeida CG, Takahashi RH, Gouras GK (2006) β-Amyloid accumulation impairs multivesicular body sorting by inhibiting the ubiquitin-proteasome system. The Journal of neuroscience 26, 4277-4288.
[41] Jarome TJ, Helmstetter FJ (2013) The ubiquitin–proteasome system as a critical regulator of synaptic plasticity and long-term memory formation. Neurobiology of learning and memory 105, 107-116.
[42] Lopez‐Salon M, Alonso M, Vianna MR, Viola H, Souza E, Mello T, Izquierdo I, Pasquini JM, Medina JH (2001) The ubiquitin–proteasome cascade is required for mammalian long‐term memory formation. European Journal of Neuroscience 14, 1820-1826.
[43] Xanthos DN, Sandkühler J (2013) Neurogenic neuroinflammation: inflammatory CNS reactions in response to neuronal activity. Nature Reviews Neuroscience.
[44] Mancuso R, Baglio F, Cabinio M, Calabrese E, Hernis A, Nemni R, Clerici M (2014) Titers of herpes simplex virus type 1 antibodies positively correlate with grey matter volumes in Alzheimer′s disease. Journal of Alzheimer′s Disease 38, 741-745.
[45] Michael E. Benros BLW, Merete Nordentoft (2013) Autoimmune Diseases and Severe Infections as Risk Factors for Mood Disorders: A Nationwide Study. JAMA Psychiatry 70, 812-820.
[46] Esposito P, Gheorghe D, Kandere K, Pang X, Connolly R, Jacobson S, Theoharides TC (2001) Acute stress increases permeability of the blood–brain-barrier through activation of brain mast cells. Brain research 888, 117-127.
[47] Hill JM, Clement C, Pogue AI, Bhattacharjee S, Zhao Y, Lukiw WJ (2014) Pathogenic microbes, the microbiome, and Alzheimer’s disease (AD). Frontiers in aging neuroscience 6, 127.
[48] 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.
[49] Spangenberg EE, Lee RJ, Najafi AR, Rice RA, Elmore MR, Blurton-Jones M, West BL, Green KN (2016) Eliminating microglia in Alzheimer’s mice prevents neuronal loss without modulating amyloid-β pathology. Brain 139, 1265-1281.
[50] Rivera-Chávez F, Zhang LF, Faber F, Lopez CA, Byndloss MX, Olsan EE, Xu G, Velazquez EM, Lebrilla CB, Winter SE (2016) Depletion of butyrate-producing clostridia from the gut microbiota drives an aerobic luminal expansion of Salmonella. Cell host & microbe 19, 443-454.
[51] Roe C, Behrens M, Xiong C, Miller J, Morris J (2005) Alzheimer disease and cancer. Neurology 64, 895-898.
[52] Basu S, Haase G, Ben-Ze′ev A (2016) Wnt signaling in cancer stem cells and colon cancer metastasis. F1000Research 5.
[53] Inestrosa NC, Toledo EM (2008) The role of Wnt signaling in neuronal dysfunction in Alzheimer′s Disease. Molecular neurodegeneration 3, 1.
[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] Law BM, Spain VA, Leinster VH, Chia R, Beilina A, Cho HJ, Taymans J-M, Urban MK, Sancho RM, Ramírez MB (2014) A direct interaction between leucine-rich repeat kinase 2 and specific β-tubulin isoforms regulates tubulin acetylation. Journal of Biological Chemistry 289, 895-908.
[56] 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.
[57] 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.

指導教授

李弘謙(Hoong-Chien Lee)

審核日期

2016-7-27

推文