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

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湯佳薇(Chia-Wei Tang) 查詢紙本館藏

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系統生物與生物資訊研究所

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)

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摘要(中)

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。

摘要(英)

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).

關鍵字(中)

★ 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

關鍵字(英)

★ ToP
★ Alzheimer
★ trend of disease progression
★ brain region-specific
★ functional pathway
★ method of gene-selection
★ Wnt signaling pathway
★ Proteasome
★ neurotrophin signaling

論文目次

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

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指導教授

李弘謙(Hoong-Chien Lee)

審核日期

2016-8-11

推文