【作者】 朴冠英；

【导师】 吴家睿；

【作者基本信息】 中国科学技术大学 ， 生物信息学， 2013， 博士

【摘要】 2型糖尿病是一种复杂系统疾病,具有明显的代谢紊乱。而肝脏在糖尿病的发展过程中扮演着极其重要的角色。只从单个基因的角度来看,很难说某个基因的表达量的改变对糖尿病的产生具有多大的作用。系统生物学可以整合大量的生物数据,对生物系统各个组成部分之间相互作用进行数学上的建模。为此本文基于2型糖尿病Goto-Kakizaki (GK)大鼠和对照组Wistar-Kyoto(WKY)大鼠在糖尿病发展过程中不同时间点上测得的芯片数据,从网络系统生物学的角度,寻找在GK大鼠糖尿病发展中的活跃调控网络。第一个工作是关于GK大鼠糖尿病发展中面向表型差异的分子功能识别。本课题中,我们设计了一种从表型水平到分子水平做演绎证明的方法,并用其来寻找疾病的分子功能。使用我们的方法得到的结果,对之前的研究所找到的分子功能具有很好的覆盖性。而且,我们还获得了可能帮助解释疾病的分子机制的一些信息。我们的面向表型差异的分子功能识别的方法提供了糖尿病的分子标签与表型数据之间的联系的信息。第二个工作是利用网络过滤的方法寻找GK大鼠糖尿病发展中的活跃调控网络。首先,通过将已知的转录因子与他们所调控的基因之间二元调控关系和已有的生物分子分类体系相结合,得到调控网络的集合。然后,计算每个调控网络与GK大鼠中测得的芯片数据的一致性,以找出相应条件下的活跃调控网络。最后得到的结果表明在2型糖尿病中：(1)在糖尿病发展的中间阶段,活跃的调控网络个数最多。(2)炎症,组织缺氧,细胞凋亡增多,细胞增生减少以及代谢紊乱GK大鼠糖尿病的特征,并且早在4周时就显现出来。(3)糖尿病发展中同时伴有损伤和补偿(4)核受体共同工作以维持正常的血糖鲁棒系统。这是第一次基于肝脏的高通量数据对GK大鼠2型糖尿病的活跃调控网络的进行的全面研究。一些重要的通路在糖尿病发展中具有重要作用。我们的发现也说明网络过滤能帮助我们理解糖尿病这样的复杂疾病,证明了网络系统生物学阐明核心机制的能力。而这些是传统的基于单个基因的分析所无法做到的。第三个工作是在第二个工作的基础上,结合路径一致算法寻找这些活跃调控网络中的主调控因子。首先,我们利用网络过滤的方法,挑选出GK大鼠糖尿病发展过程中三个时期中活跃的“转录因子一基因”对的集合,再用网络推断的方法找出另外一个“转录因子—基因”对的集合。在这一过程中,还考虑到了GK大鼠和WKY大鼠在三个时期中的基因表达特征。然后；再将转录因子在GK大鼠中出现的特异性和覆盖考虑进去,将上面得出的基因对集合进行了进一步的筛选。最终,在这些选出的“转录因子—基因”对的集合中找出了5个转录调控因子作为主调控因子,包括Etv4, Fus, Nr2f1, Sp2和Tcfap2b,以及它们所调控的54个基因。我们的方法成功的识别出了可能的主调控因子,其中包括了一些在之前与糖尿病有关的文献中曾经报道的转录调控因子。这证明我们的计算方法是识别生物现象中关键分子的有效方法。更多还原

【Abstract】 Type2diabetes mellitus is a complex systemic disease, with significant disorders of metabolism. The liver plays a key role in the development of diabetes. It is difficult to evaluate the contributions of one altered gene expression level to diabetes. However, systems biology can combine huge amount of data, mathematically model the interactions among every component with biological system. So, from the view of network biology, we have performed comprehensive active regulatory network survey in Goto-Kakizaki (GK) rat and Wistar-Kyoto (WKY) rat liver microarray data.The first work is about phenotype-difference oriented identification of molecular functions for diabetes progression in GK rat. Here, we have proposed a method for detecting molecular functions of the disease by a deductive justification from phenotype level to molecular level, and used it for testing molecular functions of disease. The functions identified by the previous studies were well covered by the functions identified by our method. The result also provided some implications for molecular mechanisms. Our phenotype-difference oriented method provides some clues to bridge directly a gap between molecular signatures and phenotype data in diabetes.The second work is about network screening of GK rat liver microarray data during diabetic progression. First, we combine the known binary relationships between the transcriptional factors and their regulated genes and the biological classification scheme to get reference regulatory networks. Then, the consistency of each regulatory network with the microarray data measured in GK rat is estimated to detect the active networks under certain comditions. The results in the case of type2diabetes in the GK rat reveals:1. More pathways are active during inter-middle stage diabetes;2. Inflammation, hypoxia, increased apoptosis, decreased proliferation, and altered metabolism are characteristics and display as early as4weeks in GK rat;3. Diabetes progression accompanies insults and compensations;4.Nuclear receptors work in concert to maintain normal glycemic robustness system. This is the first comprehensive network screening study of non-insulin dependent diabetes in the GK rat based on high throughput data of the liver. We have found several important pathways playing critical roles in the diabetes progression. Our finding s also show that network screening is able to help us understand complex diastase such as diabetes, and justify the power of network systems biology approach to elucidate the essential mechanisms which could not be solved by conventional single gene-based analysis.The third work is based on the second work, which is combined by path consistency algorithm for finding master regulators in theses active regulatory networks. First, active TF-gene pairs for three periods in GK rat were extracted from the networks by the network screening. And another set of active TF-gene pairs were selected by the network inference, in consideration of the gene expression signatures for three periods between GK and WKY rats. Then, the TF-gene pairs extracted by the two methods were further curated, from viewpoints of the emergence specificity of TF in GK rat and the regulated-gene coverage of TF in the expression signature. zuinally, in the set of TF-gene pairs we identify only5TFs, including Etv4, Fus, Nr2f1, Sp2, and Tcfap2b with54regulated genes as the candidates of MRs.更多还原