Systems-level transcriptomic analyses to Identify mouse models of asthma

系统级转录组分析识别哮喘小鼠模型

基本信息

批准号：
8876046
负责人：
Samir Kelada
金额：
$ 46.89万
依托单位：
UNIV OF NORTH CAROLINA CHAPEL HILL
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-08-15 至 2019-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8876046
关键词：
Acute Address Affect Alleles Allergens Allergic rhinitis Asthma Binding Sites Bioinformatics Breathing Chronic Coupled Data Data Set Development Disease Disease model Drug Targeting Epithelial Exhibits Feedback Gene Cluster Gene Expression Gene Expression Profile Gene Expression Regulation Gene Targeting Genes Genetic Genetic Variation Genomics Goals Grant Health Care Costs High Prevalence Human Knock-out Laboratories Lung Measures Meta-Analysis Methods MicroRNAs Modeling Molecular Mouse Strains Mucins Mus NIH Program Announcements Onset of illness Pathway interactions Pattern Phenotype Population Prevalence Prevention strategy Process Quality of life RNA Sequences Regulator Genes Research Personnel Site System Testing Time Transcriptional Regulation Validation Western World airway epithelium airway hyperresponsiveness airway inflammation allergic airway disease base comparative design differential expression disease phenotype disorder prevention disorder subtype drug discovery genetic resource human data human disease in vivo innovation knock-down knockout gene man mouse model new therapeutic target novel novel therapeutics predictive modeling prevent public health relevance response skills therapeutic target transcription factor transcriptome sequencing transcriptomics treatment strategy

项目摘要

DESCRIPTION (provided by applicant): The high prevalence of allergic airway diseases (AADs) including asthma and allergic rhinitis make identifying new disease prevention and treatment strategies a priority. Recent studies have shown that gene expression in airway epithelia is associated with AAD and may underlie different AAD sub-types. With few exceptions, the key transcriptional regulators of epithelial gene expression in AAD are unknown. Their identification represents an important step in the development of novel therapeutic strategies. The identification of novel therapeutic targets has also been hindered by the lack of validated mouse models for AAD (in particular, sub-types of asthma). We hypothesize that we can identify mouse models of AAD that more closely mimic human AAD sub- types than currently exists using conserved patterns of gene expression as decision criteria. Further, we hypothesize that these new models can be used to identify and test novel disease targets. In Aim 1, we will build predictive models of transcriptional regulation in AAD in which master regulators of gene expression, namely transcription factors (TFs) and microRNAs (miRNAs), are coupled to their target genes. We will first perform a meta-analysis of six transcriptomic studies of human AAD (N = 218 subjects: 118 asthma cases, 26 allergic rhinitis cases, and 74 controls) to identify genes consistently altered in AAD and which genes distinguish AAD sub-types. We will then identify putative master regulators of the differentially expressed genes (including subsets that are co-expressed) using gene set enrichment analysis for regulatory motifs (transcription factor binding sites and miRNA target sites). In Aim 2, we will identify new strains of mice from the Collaborative Cross (CC) population, which features enhanced genetic diversity compared to existing mouse genetics resources, that better mimic human AAD sub-types. We will phenotype 40 CC strains for hallmark AAD phenotypes in response to acute and chronic allergen exposure, and measure lung gene expression by RNA-sequencing. Using an innovative statistical approach of the RNA-seq data, we will identify which CC strains exhibit patterns of gene expression most akin to human AAD subtypes. At the same time, we will conduct gene set enrichment analysis of differentially expressed (and co-expressed) genes to identify key TFs and miRNAs that are shared between human and mouse. In Aim 3, we conduct the validation component of the grant. We will test whether novel candidate master regulators that are conserved between mouse and man (identified in Aims 1 and 2) represent new AAD therapeutic targets. We will perturb two candidate master regulators in vivo using gene knockdown and gene knockout approaches in mice. The effect of these perturbations will be evaluated in acute and chronic allergen models to test whether altering the master regulator can affect AAD onset and/or modify established AAD. Thus in total we will (1) identify conserved master regulators of AAD phenotypes, (2) establish new mouse models of AAD that better mimic human disease, and (3) evaluate whether master regulators of gene expression represent novel drug targets.

描述（由申请人提供）：包括哮喘和过敏性鼻炎在内的过敏性气道疾病（AAD）的高患病率使得确定新的疾病预防和治疗策略成为当务之急。最近的研究表明，气道上皮细胞中的基因表达与 AAD 相关，并可能是其基础。除了少数例外，AAD 中上皮基因表达的关键转录调节因子尚不清楚，这代表了开发新治疗策略的重要一步。由于缺乏经过验证的 AAD 小鼠模型（特别是哮喘亚型），我们努力寻找比目前使用保守模式更接近人类 AAD 亚型的 AAD 小鼠模型。此外，我们致力于将这些新模型用于识别和测试新的疾病靶点。在目标 1 中，我们将建立 AAD 转录调控的预测模型，其中掌握基因表达（即转录）的调控因子。因素 (TF) 和我们将首先对人类 AAD 的六项转录组学研究（N = 218 名受试者：118 名哮喘病例、26 名过敏性鼻炎病例和 74 名对照者）进行荟萃分析，以一致地识别基因。然后，我们将使用基因集富集分析来识别差异表达基因（包括共表达的子集）的假定主调节因子。在目标 2 中，我们将鉴定新菌株。来自协作杂交 (CC) 群体的小鼠，与现有的小鼠遗传资源相比，其具有增强的遗传多样性，可以更好地模拟人类 AAD 亚型。我们将对 40 个 CC 品系进行表型分析，以获得标志性 AAD 表型，以响应急性和慢性过敏原暴露。，并通过 RNA 测序测量肺部基因表达，我们将确定哪些 CC 菌株表现出最类似于人类 AAD 亚型的基因表达模式。对差异表达（和共表达）基因进行基因集富集分析，以确定人类和小鼠之间共享的关键 TF 和 miRNA。在目标 3 中，我们将测试是否有新的候选主调节因子。在小鼠和人类之间保守的（目标 1 和 2 中确定的）代表新的 AAD 治疗靶点，我们将使用基因敲低和基因敲除方法在小鼠体内扰乱两个候选主调节因子。将在急性和慢性过敏原模型中进行评估，以测试改变主调节因子是否会影响 AAD 发作和/或修改已建立的 AAD。因此，我们总共将 (1) 识别 AAD 表型的保守主调节因子，(2) 建立新的小鼠模型。更好地模拟人类疾病的 AAD，以及 (3) 评估基因表达的主调节因子是否代表新的药物靶点。