Core 3: Translational Genomics and Data Integration Core

摘要/项目摘要 高通量基因表达技术在系统性硬化症中的应用 皮肤活检，分离的SSC细胞系和外周血细胞(PBC)样本显示 这将是了解风湿性疾病多样性的重要工具，如 以及潜在基因表达途径的变化。《翻译》 基因组学和数据集成(TGDI)核心将使用新的生物信息学和基因组学 已经开发并已经在核心中成功实现的方法 Pi的实验室分析SSC样本和健康对照。高质量的RNA将是 使用PI中建立的方案通过RNA测序进行准备和分析 实验室。所有数据都使用标准和新颖的方法进行处理，这些方法使用 结合测试差异基因表达的算法，丰富了途径 分析这些变化，并将其放在所有公开可用的SSC High- 吞吐量数据。TGDI核心使用硬皮病提供网络分析 特定网络(SSN)，用于分析来自细胞系、小鼠模型、临床试验、 和单细胞RNA-seq(scRNA-seq)，从而提供了治疗效果的衡量标准 消除我们在SSC中观察到的异常基因表达。这个核心的目标是 为每个项目提供高质量的rna-seq分析，并处理 在严格控制的分析管道中产生数据，以提供差异基因 表达和患者亚组分配，2)提供系统生物学和网络 使用我们的新SSN分析SSC中的基因表达数据，以及3)执行Meta- 使用现有数据和生成的新数据对SSC临床试验进行分析 CORT研究项目的一部分。 相关性： 高通量基因表达分析使得定义SSC的亚群成为可能 并确定了可以作为治疗靶点的解除管制的途径。近期 研究表明，患者在基线时的子集或激活的通路可以是 临床反应的预测。此核心提供高吞吐量的研究、数据 CORT调查人员分析、SSC生物信息学和系统生物学分析 样本。

Abstract / Project Summary Application of high-throughput gene expression technology to systemic sclerosis (SSc) skin biopsies, isolated SSc cell lines and peripheral blood cell (PBC) samples has shown that it will be an important tool for understanding the diversity in rheumatic diseases, as well as changes to the underlying gene expression pathways. The Translational Genomics and Data Integration (TGDI) core will use novel bioinformatic and genomic methods that have been developed and already successfully implemented in the core PI's laboratory to analyze SSc samples and healthy controls. High quality RNA will be prepared and analyzed by RNA-sequencing using protocols established in the PIs laboratory. All data are processed using standard and novel methods that use a combination of algorithms that test for differential gene expression, enriched pathways analysis and put the changes into the context of the all publicly available SSc high- throughput data. The TGDI core provides network analyses using a Scleroderma Specific Network (SSN) to analyze data from cells lines, mouse models, clinical trials, and single cell RNA-seq (scRNA-seq), thus providing a measure of how well a therapy eliminates the aberrant gene expression we observe in SSc. The goals of this core are to 1) Provide high quality RNA-seq analyses for individual projects and process the resulting data in a rigorously controlled analysis pipeline to provide differential gene expression and patient subset assignments, 2) provide a systems biology and network analysis of gene expression data in SSc using our novel SSN, and 3) perform meta- analyses of SSc clinical trials using both existing data as well as new data generated as part of the CORT research projects. Relevance: High-throughput gene expression analysis has allowed the definition of subsets of SSc and identified deregulated pathways that can be targeted therapeutically. Recent studies have shown that a patient's subset or activated pathways at baseline can be predictive of clinical response. This core, provides high-throughput studies, data analyses for CORT investigators, bioinformatics and systems biology analyses of SSc samples.