System Biology Approach for Signaling Transduction Study of Complex Phenotypes

描述(由申请人提供)： 这项拟议研究的主要目标是开发一个名为信号转导网络分析器(STkAnalyzer)的软件包，以识别与复杂表型疾病相关的信号通路和通路特征。我们将使用四种骨髓增生异常综合征(MDS)表型作为疾病的原型，通过整合使用单核苷酸多态(SNP)阵列、基因表达阵列、microRNA阵列以及公开可用的京都基因和基因组百科全书(KEGG)和蛋白质相互作用(PPI)数据库的高通量全基因组图谱来测试该软件包的性能。在美国人口中，MDSS的频率和发病率正在增加，但在过去十年中，MDSS患者的诊断并没有显示出任何显着的改善。后一种现象的一个主要原因是缺乏在早期阶段准确找到MDS的途径和生物标志物的方法。常博士在卫理公会医院的团队正在研究300多名具有良好特征的MDS患者的队列。MDS具有非常复杂的表型，主要类型包括难治性贫血(RA)、环状铁粒母细胞(RARS)、难治性细胞减少伴多系异型增生(RCMD)、RA伴原始细胞增多(RAEB)。尽管MDS被用作这一提议的疾病原型，但开发的套餐将适用于癌症、糖尿病等具有复杂表型的多种疾病。这一方案对系统生物学的影响是巨大的。 检测染色体异常可以识别可能导致造血干细胞转化的候选基因改变。但它不能回答这些候选基因中哪些是MDS表型的真正原因基因，以及这些基因是如何导致MDS表型的。同样，比较疾病样本和正常样本之间的基因表达谱可以识别哪些基因在疾病组织中活跃，哪些基因不活跃。然而，它不能区分哪些基因是原因，哪些基因是结果。这些问题非常重要，这些答案将形成我们对MDS表型分子机制的基本看法，并影响如何设计和开发新的诊断、治疗和预防策略。最近大量蛋白质-蛋白质相互作用、蛋白质-DNA相互作用数据和表达数量性状基因座(EQTL)定位技术的出现为解决这些问题提供了手段。因此，我们建议识别受易感基因干扰的信号通路，进而导致四种MDS表型。 该软件包(STkAnalyzer)的主要技术贡献包括四个方面：第一，提出了一种新的条件随机模式方法，用于扩增SNParray拷贝数估计和杂合性缺失检测；第二，提出了eQTL作图，将基因分型数据和mRNA关联起来；第三，提出了MicroRNA-mRNA靶向显著性分析(SAMiMT)，整合了mRNA和microRNA阵列；最后，提出了一种扩散映射或半群方法，用于推断信号转导网络和生物标记物基序(生物标志物模式或路径签名)，以揭示eQTL导致MDS发病的潜在机制。

DESCRIPTION (provided by applicant): The primary goal of the proposed study is to develop a software package, Signal Transduction Network Analyzer (STkAnalyzer), to identify signal pathways and pathway signatures that are related to diseases with complex phenotype. We will use the four myelodysplastic syndromes (MDS) phenotypes as the prototype of disease to test the performance of this package by integrating high-throughput genome-wide profiling using single-nucleotide polymorphism (SNP) array, gene expression arrays, microRNA array, and publically available Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interaction (PPI) databases. The frequency and incidence of MDSs is increasing in the U.S. population but the diagnosis of MDSs patients has not shown any significant improvement over the last decade. One major cause of the latter phenomenon is the lack of methodologies to accurately finding the pathways and biomarkers for MDSs at an early stage. Dr. Chang's group in The Methodist Hospital is studying a cohort of more than 300 well-characterized MDS patients. The MDS is characterized by very complex phenotypes with main categories include refractory anemia (RA), RA with ringed sideroblasts (RARS), refractory cytopenia with multi-lineage dysplasia (RCMD), RA with excess blasts (RAEB). Although MDS was used as the prototype of disease for this proposal, the package developed will be applicable to multiple diseases with complex phenotypes such as cancers, diabetes and so on. The impact of the package is tremendous in system biology. Detecting chromosomal abnormality can identify the candidate genetic alterations which may cause the transformation of the hematopoietic stem cells. But it cannot answer which of these candidate genes are the true causal genes of MDS phenotypes and how these genes cause MDS phenotypes. Similarly, comparison of gene expression profiles between disease samples and normal samples could identify which genes are active in disease tissue and which genes are inactive. However, it cannot discriminate which genes are the causes and which genes are the results. These questions are extremely important and the answers will shape our basic view of the molecular mechanism of MDS phenotypes and influences how to design and develop new strategies for diagnosis, treat and prevent MDS. The recent availability of large protein-protein interaction, protein-DNA interaction data, and the expression quantitative trait loci (eQTL) mapping techniques provides a means to address these issues. Hence we propose to identify signal pathways that are perturbed by susceptibility loci and that in turn lead to the four MDS phenotypes. The major technological contributions in this package (STkAnalyzer) are in four: first, a novel Conditional Random Pattern approach is developed for amplified SNParray copy number estimation and LOH detection; second, eQTL mapping is proposed to associate the genotyping data and mRNA; third a significance analysis of microRNA-mRNA targeting (SAMiMT) is proposed to integrate mRNA and microRNA arrays, and finally a Diffusion Mapping or Semi-Group approach is proposed for inferring signal transduction network and biomarker motif (biomarker pattern or pathway signature) to unravel the underlying mechanism how the eQTLs lead to the MDS pathogenesis.