結核病是由結核分支桿菌感染所造成的，並且結核分支桿菌是最古老的人類病原體之一且演化出複雜的策略來生存。肺在做氣體交換時是接觸到懸浮氣體中結核分支桿菌的第一個器官。因此，在肺部免疫系統的守衛像是巨噬細胞和樹突狀細胞是很重要的防護來反抗結核分支桿菌的感染。到目前為止，雖然已經有很多文章探討了巨噬細胞和樹突狀細胞在感染結核分支桿菌的功能，但是全基因組的路徑和網路還是不完整的。此外，巨噬細胞和樹突狀細胞的免疫反應還是不同的。所以，我們分析巨噬細胞和樹突狀細胞感染結核分支桿菌前期的全基因組基因和表觀遺傳網路(GWGEINs)來找出宿主和病原體在巨噬細胞和樹突狀細胞間不同的機制。
我們首先使用databases mining來建立巨噬細胞和樹突狀細胞candidate GWGEINs。接著我們使用人類巨噬細胞和樹突狀細胞感染結核分支桿菌H37Rv的microarray data 來刪減掉candidate GWGEINs中的假陽性連線。我們透過constrained least square方法使用基因調控網路和蛋白質交互作用模型來決定基因和蛋白質連線的能力，並且我們使用Akaike information criterion (AIC)來刪減掉candidate GWGEINs中不顯著的連線，最後建立出巨噬細胞和樹突狀細胞感染結核分支桿菌的GWGEINs。
建完GWGEINs後，使用principle network projection (PNP)方法來建立巨噬細胞和樹突狀細胞的宿主病原體間核心網路(HPCNs)。因此，我們可以研究宿主和病原體間潛在的機制還有找出病源體如何對抗巨噬細胞和樹突狀細胞在結核分支桿菌H37Rv早期感染時的攻擊機制。最後，因為Rv1675c在巨噬細胞的結核分支桿菌中扮演重要防禦的角色我們預測Rv1675c可以成為一個有潛力的藥物標靶。此外，結核分支桿菌膜上的蛋白Rv1438、Rv1098c、Rv0967、Rv0969、Rv0970、Rv0667、Rv1696和Rv2404c也可能成為有潛力的藥物標靶因為他們在兩個細胞型態的結核分支桿菌中的存活上有重要的角色，而且這些膜蛋白也很容易的可以被藥物標記。更進一步，多分子藥物包括Lopinavir、TMC207、ATSM和GTSM也被提出來作為治療結核分支桿菌的感染透過標記之前所敘述的有潛力的藥物標靶。

Tuberculosis is caused by the infection of Mycobacterium tuberculosis (Mtb), and Mtb is one of the oldest human pathogens and evolves complex strategies for survival. Lung is the first organ exposed to aerosol-transmitted Mtb during gaseous exchange. Therefore, the guards of the immune system in lung such as macrophages (Mϕs) and dendritic cells (DCs) are the most important defense against Mtb infection. To date, although there are several studies discussing the functions of Mϕs and DCs during Mtb infection, the genome-wide pathways and networks are still incomplete. Besides, the immune responses in Mϕs and DCs are also different. Thus, we analyzed the genome-wide genetic-and-epigenetic interspecies networks (GWGEINs) of Mϕs and DCs both infected with Mtb to find out the different mechanisms of both host and pathogen between Mϕs and DCs during early Mtb infection.
We first used databases mining to construct candidate GWGEINs of Mϕs and DCs. Then we used the two-sided microarray data from human Mϕs and DCs both infected with Mtb H37Rv to prune the false-positive edges in candidate GWGEINs due to big database mining. We used gene regulation models and protein-protein interaction (PPI) models of both host and pathogen to determine the association abilities of connective edges of genes and proteins by constrained least square method, and we deleted the false-positives by pruning the insignificant edges in the candidate GWGEINs out of system order (number of edges) determined by Akaike information criterion (AIC), constructing the GWGEINs in Mϕs and DCs both infected with Mtb.
After constructing GWGEINs, the principal network projection (PNP) method is employed to construct the host-pathogen core networks (HPCNs) in both Mϕs and DCs. Thus, we can investigate the underlying cross-talk mechanisms between host and pathogen and find out how the pathogen counteracts the offensive mechanisms by host in Mϕs and in DCs during Mtb H37Rv early infection. Finally, we predicted Rv1675c as a potential drug target because of its important defensive role of Mtb in Mϕs. Besides, the membrane proteins Rv1438, Rv1098c, Rv0967, Rv0969, Rv0970, Rv0667, Rv1696 and Rv2404c in Mtb might be also potential drug targets because of their important roles on the survival of Mtb in both cell types and their being easily targeted by drugs. Further, a dealing of multiple molecules drug including Lopinavir, TMC207, ATSM and GTSM is also suggested for potential therapeutic treatment of Mtb infection by targeting above potential drug targets.

摘要 i
Abstract iii
Contents iv
List of Tables v
List of Figures v
List of Supplemental materials v
Introduction 1
Materials and Methods 5
3.1 Overview the construction processes of GWGEINs in Mϕs and DCs both infected with Mtb 5
3.2 Big data mining and data preprocessing 5
3.3 Construction processes of candidate genome-wide genetic-and-epigenetic interspecies networks (GWGEINs) in Mϕs and DCs infected with Mtb 6
3.4 Dynamic model of GRNs and PPINs in candidate GWGEINs for Mϕs and DCs 7
3.5 Identification of the regulative and interactive parameters in candidate GWGEINs in Mϕs and DCs 9
3.6 Prune the false-positives in GRNs and PPINs of candidate GWGEINs 15
3.7 Core network construction by using principal network projection (PNP) method 17
Results and Discussion 20
3.1 The GWGEINs in Mϕs and DCs both infected with Mtb 20
3.2 The HPCNs in Mϕs and DCs both infected with Mtb 20
3.2.1 The biological processes of host core networks in both cell types 20
3.2.2 Host-pathogen cross-talk interactions in both cell types 21
3.2.3 Host responses in Mϕs and in DCs during Mtb infection 22
3.2.4 The protective mechanisms of Mtb in Mϕs and DCs 25
Overview the offensive and defensive mechanisms between host and pathogen and the dysfunctions of host in Mϕs and DCs 29
3.3 Drug targets, Drug mining and multiple molecules drug design 30
Conclusion 31
Reference 44

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