臺灣博碩士論文加值系統

本研究目的主要為藉由建立生物資訊資料庫的藥物註解檔案，來發展一套能系統性地篩選有效藥物及治療目標的模式。藉由微陣列檢測系統，獲得在藥物處理之下細胞特定的基因表現變化圖譜，將其與數種國外大規模藥物篩選資料庫進行比對，能讓我們分析其結果並將其應用於藥物開發及相關的研究計畫。因此，透過整合性的平台來結合高通量篩選科技及電腦資訊模式，可以幫助我們預測藥物可能的作用機轉，並以老藥新用的模式加速藥物開發的進行。此篇研究涵蓋三個研究主題，分別為（1）找到了一個抗生素為癌症幹細胞抑制劑，並藉由在非小細胞肺癌細胞當中觀察到E-cadherin表現上升及Slug表現下降有抑制EMT的情形，（2）植物藥justicidin A可抑制核內NF-ĸB的表現，並抑制NF-ĸB與DNA的結合能力，為一NF-ĸB 抑制劑，（3）發現法布瑞氏症的酵素替代治療可能藉由抑制JAK、toll-like receptor (TLR)、interferon (IFN) 和interleukin (IL) 相關途徑而對免疫及發炎產生影響。總結，此篇的研究結果針對藥物開發提供了不同的見解，也發展了新的藥物開發途徑。

In this study, a novel algorithm has been developed by creating the comprehensive drug annotations for bioinformatics database to identify drug-target relationships in silico. By utilizing the microarray to obtain specific gene expression signatures, we can make inference on the connection between input signatures and internal reference profiles from the database. Thus, the integrated platform of high-throughput technologies and computational models enables us to predict molecular actions of small molecules and apply the concept of drug repurposing to accelerate drug discovery. Further, this thesis covers 3 different studies, including (1) identifying an antibiotic as a cancer stem cell (CSC) inhibitor with the ability to inhibit epithelial-to-mesenchymal transition (EMT) processes by enhancing E-cadherin expression and reducing Slug expression in NSCLC cells, (2) investigating the nature lignan justicidin A as a NF-ĸB suppressor which can suppress both the nuclear NF-ĸB expression and NF-ĸB DNA-binding activity, and (3) evaluating the effect of enzyme replacement therapy for Fabry disease in the inflammatory and immune responses possibly through JAK, toll-like receptor (TLR), interferon (IFN), and interleukin (IL)-related signaling pathways. In conclusion, the integrated platform of this study yields numerous insights and paves the way for new drug investigations.

Content

致謝 i
Abstract ii
中文摘要 iii
Content iv
Lists of Abbreviations vii
Introduction 1
Precision medicine 1
Drug screening database 2
Connectivity Map (Cmap) 3
Library of Integrated Network-based Cellular Signatures (LINCS) 5
Cancer Therapeutics Response Portal (CTRP) 5
Genomics of Drug Sensitivity in Cancer (GDSC) 6
Drug repurposing 6
Clinical examples for drug repurposing 8
Innovative strategies for drug repurposing 9
NIH NCATS program 10

Objectives 11
Aim 1: To employ microarray technology to identify specific gene signatures. 12
Aim 2: To establish new LINCS drug annotations and make the connection among different datasets. 12
Aim 3: To integrate and identify different groups of compound signatures into research projects. 13

Materials and Methods 13
Microarray-based gene profiling analysis 13
Drug screening via Connectivity map and LINCS 14
Drug annotations from different data repositories 15
Elucidate potential pathways through CPDB analysis 15

Results 16
Comprehensive drug annotations for LINCS database 16
Utilizing bioinformatics approach for predicting innovative autophagy inducers 16
To investigate the specific gene signatures to research projects 17

Part 1. Identifying thiostrepton as an inhibitor of cancer stem cell growth. 17
To identify potential CSC inhibitor via bioinformatics analysis 17
The CSC and anti-CSC signatures 18
GSE18150 as anti-CSC signature 18
GSE18931 as CSC signature 19
GSE17215 as anti-CSC and EMT signature 20

Part 2. Rapid identification of justicidin A as a NF- κB suppressor. 20
Nature lignin Justicidin A 20
Microarray analysis for JA in HT-29 and Hep 3B 21
15-delta prostaglandin J2 was predicted to act as JA 21

Part 3. Evaluation the effects of standard treatment of Fabry disease in inflammatory and immune responses. 22
Fabry disease and enzyme replacement therapy 22
Samples from Fabry disease patients and mice 23
Analysis for microarray and Gb3 24
Predicted pathways and compounds from PBMC of Fabry disease patients 25
Predicted pathways and compounds from mice models 26

Discussion 27

References 35

List of Figures 42
Figure 1. To facilitate novel drug discovery by using microarray technologies via integrating different big data resources. 42
Figure 2. Overview for drug repurposing. 44
Figure 3. Schematic illustration of new drug development via bioinformatics analysis. 45
Figure 4. The comprehensive drug annotations for LINCS database. 46
Figure 5. Cancer stem cell gene signatures. 47

List of Tables 48
Table 1. Current target therapy for cancer treatment based on the concept of precision medicine. 48
Table 2. The list of comprehensive drug annotations for LINCS database. 49
Table 3. The list of autophagy regulators from bioinformatics analysis. 97
Table 4. Top 30 compounds were selected for identifying the conceivable mechanism of JA from Cmap. 100
Table 5. The bioinformatics analysis for Fabry disease from patient’s and mice’s samples. 101

Supplementary 103
Supplementary Figure 1. GSEA plots of 81 gene expression profiles of thiostrepton among the 11,641 L1000 gene expression profiles. 103
Supplementary Figure 2. JA decreased the binding ability of NF-kB to DNA in a time- and dose-dependent manner. 104

Publication 105

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