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研究生： 許博惟
Bo-Wei Hsu
論文名稱： 利用遺傳和表觀遺傳宿主-病毒網路和真實世界的雙邊RNA-Seq數據研究發病機制並鑑定人類呼吸道合胞病毒藥物再利用的生物標誌物:系統生物學和深度學習方法
Genetic and Epigenetic Host-Virus Network to Investigate Pathogenesis and Identify biomarkers for Drug Repurposing of Human Respiratory Syncytial Virus via Real-World Two-side RNA-Seq data: Systems Biology and Deep learning Approach
指導教授： 陳博現
Chen, Bor-Sen
口試委員: 莊永仁
Chuang, Yung-Jen
藍忠昱
Lan, Chung-Yu
林澤
Lin, Che
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 電機工程學系
Department of Electrical Engineering
論文出版年： 2023
畢業學年度： 111
語文別： 英文
論文頁數： 44
中文關鍵詞： 人類呼吸道合胞病毒 、宿主-病原體 RNA 序列數據 、深度神經網路 、藥物-靶點相互作用預測模型 、藥物設計規範 、系統生物學 、多分子藥物
外文關鍵詞： human respiratory syncytial virus (hRSV), host–pathogen RNA-Seq data, deep neural network, drug-target interaction prediction model, drug design specification, system biology, multi-molecule drug
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    • 人類呼吸道合胞病毒 (hRSV) 每年影響超過 3300 萬人，但目前尚無有效藥物或疫苗獲得批准。在這項研究中，我們首先通過大數據挖掘構建了一個候選宿主-病原體物種間全基因組遺傳和表觀遺傳網絡（HPI-GWGEN）。然後，我們通過雙邊宿主-病原體 RNA-seq 時間剖析數據並採用系統生物學方法來修剪候選 HPI-GWGEN 中的偽陽性，以獲得真正的 HPI-GWGEN。借助主網路投影和 KEGG 通路註釋，我們可以提取 hRSV 感染過程中的核心信號通路並研究 hRSV 感染的致 病機制，並選擇相應的重要生物標誌物作為藥物靶點，即 TRAF6、STAT3、IRF3、TYK2 和 MAVS。 最後，為了發現潛在的分子藥物，我們通過藥物-靶點相互作用 (DTI) 數據庫訓練了基於深度神經網路的 DTI 預測模型，以預測這些藥物靶點的候選分子藥物。同時通過三個藥物設計規範篩選這些候選分子藥物後，即調 控能力、敏感性和毒性。我們最終選擇了阿曲汀、RS-67333 和苯乙雙胍作為治療 hRSV 感染的潛在多分子藥物。

    Human respiratory syncytial virus (hRSV) affects more than 33 million people each year, but there are currently no effective drugs or vaccines approved. In this study, we first constructed a candidate host–pathogen interspecies genome-wide genetic and epigenetic network (HPI-GWGEN) via big-data mining. Then, we employed reversed dynamic methods via two-side host–pathogen RNA-seq time-profile data to prune false positives in candidate HPI-GWGEN to obtain the real HPI-GWGEN. With the aid of principal-network projection and the annotation of KEGG pathways, we can extract core signaling pathways during hRSV infection to investigate the pathogenic mechanism of hRSV infection and select the corresponding significant biomarkers as drug targets, i.e., TRAF6, STAT3, IRF3, TYK2, and MAVS. Finally, in order to discover potential molecular drugs, we trained a DNN-based DTI model by drug–target interaction databases to predict candidate molecular drugs for these drug targets. After screening these candidate molecular drugs by three drug design specifications simultaneously, i.e., regulation ability, sensitivity, and toxicity. We finally selected acitretin, RS-67333, and phenformin to combine as a potential multi-molecule drug for the therapeutic treatment of hRSV infection.

    摘要...I Abstract...II 誌謝...III Contents...IV 1.Introduction...1 2. Methods and Materials...5 2.1 Overview of Systematic Drug Discovery for hRSV Infection via Systems-Biology Method...4 2.2 Construction of Candidate HPI-GWGEN by Database Mining and Integration...6 2.3 HPI RNA-seq Time-Profile Data of Human A549 Cell and hRSV...7 2.4. Construction of Dynamic Model of HPI-GWGEN for hRSV Infection...7 2.5. Parameter Estimation of Dynamic Model for Candidate HPI-GWGEN by System Identification Method for hRSV Infection Progression...10 2.6. Extracting Core HPI-GWGEN via Principal-Network Projection...15 2.7. Systematic Drug Repurposing Design of hRSV Infection via DNN-based DTI Model and Drug Specifications...17 2.7.1. DNN-based DTI Model for Drug Repurposing of hRSV Infection...19 2.7.2. Drug Design Specifications...20 3.Results...21 3.1. Extracting Core Signaling Pathways via System Identification and PrincipalNetwork Projection Approach...21 3.2. Investigation of Core HPI Signaling Pathways for Pathogenic Mechanism of hRSV Infection Progression...25 3.2.1. The Significant Signaling Pathways Involved with Biomarkers TRAF6 and RELA...27 3.2.2. The Significant Signaling Pathways Involved with Biomarkers MAVS and IRF3...28 3.2.3. The Significant Signaling Pathways Involved with Biomarker TYK2...29 3.2.4. TNF Signaling Pathway...30 3.2.5. Conclusion of HPI Signaling Pathways during hRSV Infection...30 3.3. Multi-molecule Drug Repurposing by DNN-based DTI Model and Drug Design Specifications...31 3.3.1. DNN-based DTI Model...32 3.3.2. Multi-molecule Drug Repurposing for hRSV Infection Treatment...35 4. Discussion...36 5. Conclusions...38 Reference ...38

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