System Biology Approach for Signaling Transduction Study of Complex Phenotypes

描述（由申请人提供）： 拟议研究的主要目标是开发一个软件包，信号转导网络分析仪（STkAnalyzer），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，通过整合高通量全基因组分析，使用单核苷酸多态性（SNP）阵列，基因表达阵列，microRNA阵列，以及可在网上获得的京都基因和基因组百科全书（KEGG）和蛋白质-蛋白质相互作用（PPI）数据库，来测试该软件包的性能。在美国人群中，MDS的频率和发病率正在增加，但在过去十年中，MDS患者的诊断并未显示出任何显着改善。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。Chang博士在卫理公会医院的研究小组正在研究一个由300多名特征良好的MDS患者组成的队列。MDS的特征在于非常复杂的表型，主要类别包括难治性贫血（RA）、RA伴环形铁粒幼细胞（RARS）、难治性血细胞减少伴多谱系发育不良（RCMD）、RA伴原始细胞过多（RAEB）。虽然该方案以MDS作为疾病的原型，但开发的软件包将适用于癌症、糖尿病等多种具有复杂表型的疾病，在系统生物学中的影响是巨大的。 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遗传改变。但它不能回答这些候选基因中哪些是MDS表型的真正致病基因以及这些基因如何引起MDS表型。类似地，疾病样品和正常样品之间的基因表达谱的比较可以鉴定哪些基因在疾病组织中是活跃的，哪些基因是不活跃的。然而，它无法区分哪些基因是原因，哪些基因是结果。这些问题是非常重要的，答案将塑造我们的MDS表型的分子机制的基本观点，并影响如何设计和开发诊断，治疗和预防MDS的新策略。近年来，蛋白质-蛋白质互作、蛋白质-DNA互作以及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新的途径。因此，我们建议确定受易感基因座干扰的信号通路，进而导致四种MDS表型。 这套软件的主要技术贡献（STkAnalyzer）的方法主要有四个方面：第一，发展了一种新的条件随机模式方法用于扩增SNParray拷贝数估计和洛缺失检测;第三，提出了microRNA-mRNA靶向的显著性分析（SAMiMT）以整合mRNA和microRNA阵列，最后提出了一种扩散作图或半组方法来推断信号转导网络和生物标志物基序（生物标志物模式或途径签名），以揭示eQTL如何导致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.