System Biology Approach for Signaling Transduction Study of Complex Phenotypes

用于复杂表型信号转导研究的系统生物学方法

• 批准号: 7941855
• 负责人: Chung-Che Chang
• 金额: $ 32.73万
• 依托单位: METHODIST HOSPITAL RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2013-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7941855
• 关键词: System; Biology; Approach; Signaling; Transduction

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.

描述（由申请人提供）： 该研究的主要目标是开发一个软件包——信号转导网络分析器（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) 表型作为疾病原型，通过使用单核苷酸多态性 (SNP) 阵列、基因表达阵列、microRNA 阵列以及公开的京都基因和基因组百科全书 (KEGG) 和蛋白质-蛋白质相互作用进行高通量全基因组分析，来测试该软件包的性能 (PPI) 数据库。在美国人口中，MDS 的频率和发病率正在增加，但 MDS 患者的诊断在过去十年中没有显示出任何显着改善。后一种现象的一个主要原因是缺乏早期准确找到MDS的途径和生物标志物的方法。卫理公会医院的 Chang 博士团队正在对 300 多名特征明确的 MDS 患者进行队列研究。 MDS 的特点是非常复杂的表型，主要类别包括难治性贫血 (RA)、伴环状铁粒幼细胞的 RA (RARS)、难治性血细胞减少伴多谱系不典型增生 (RCMD)、伴原始细胞过多的 RA (RAEB)。虽然该提案以MDS作为疾病原型，但开发的软件包将适用于具有复杂表型的多种疾病，例如癌症、糖尿病等。该软件包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。但它无法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导致MDS表型。类似地，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，它无法区分哪些基因是原因，哪些基因是结果。这些问题极其重要，答案将塑造我们对MDS表型分子机制的基本看法，并影响如何设计和开发诊断、治疗和预防MDS的新策略。最近出现的大型蛋白质-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) 作图技术提供了解决这些问题的方法。因此，我们建议鉴定受易感性位点干扰并进而导致四种 MDS 表型的信号通路。 该软件包 (STkAnalyzer) 的主要技术贡献有四个：首先，开发了一种新颖的条件随机模式方法，用于扩增 SNParray 拷贝数估计和 LOH 检测；其次，提出eQTL作图以将基因分型数据和mRNA关联起来；第三，提出了microRNA-mRNA靶向的显着性分析（SAMiMT）来整合mRNA和microRNA阵列，最后提出了扩散图谱或半组方法来推断信号转导网络和生物标志物基序（生物标志物模式或通路特征），以揭示eQTLs导致MDS发病的潜在机制。