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

• 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
• 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

 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（由适用提供）：过敏性气道疾病（AAD）的高流行率，包括哮喘和过敏性鼻炎，使鉴定新疾病预防和治疗策略的优先级是优先事项。最近的研究表明，气道上皮中的基因表达与AAD有关，可能是不同AAD亚型的基础。除少数例外，AAD中上皮基因表达的关键转录调节剂尚不清楚。他们的识别代表了新理论策略发展的重要一步。由于缺乏AAD验证的小鼠模型（尤其是哮喘的亚型），因此也阻碍了新型治疗靶标的鉴定。我们假设我们可以识别出比使用基因表达的构成模式作为决策标准的当前存在的AAD小鼠模型，该模型比当前存在的更紧密地模拟人AAD亚型。此外，我们假设这些新模型可用于识别和测试新型疾病靶标。在AIM 1中，我们将在AAD中构建转录调控的预测模型，在该模型中，基因表达的主要调节剂，即转录因子（TFS）和microRNA（miRNA），与其靶基因耦合。我们将首先对人类AAD的六项转录组研究进行荟萃分析（n = 218受试者：118例哮喘病例，26例过敏性鼻炎病例和74例对照），以鉴定AAD中始终改变的基因，哪些基因区分AAD亚型。然后，我们将使用对调节基序的基因集富集分析（转录因子结合位点和miRNA靶位点）来确定不同表达基因（包括共表达的亚集）的推定主要调节剂。在AIM 2中，我们将确定新的压力 与现有的小鼠遗传学资源相比，合作杂交（CC）人群的小鼠（CC）人群具有增强的遗传多样性，这些资源可以更好地模拟人类AAD子类型。我们将对急性和慢性过敏原暴露的标志性AAD表型进行表型40 cc菌株，并通过RNA测序测量肺基因表达。使用RNA-Seq数据的创新统计方法，我们将确定哪些CC菌株暴露的基因表达模式最类似于人AAD亚型。同时，我们将对不同表达（和共表达）基因的基因集富集分析，以识别人与小鼠之间共享的关键TF和miRNA。在AIM 3中，我们进行了赠款的验证部分。我们将测试小鼠和人之间保守的新型候选主调节器（在目标1和2中识别）是否代表新的AAD治疗靶标。我们将使用小鼠的基因敲低和基因敲除方法在体内扰动两个候选主调节剂​​。这些扰动的效果将在急性和慢性过敏原模型中评估，以测试更改主调节器是否会影响AAD发作和/或修改已建立的AAD。总共（1）确定构成AAD表型的主要调节剂，（2）建立新的AAD小鼠模型，以更好地模仿人类疾病，并且（3）评估基因表达的主调节剂是否代表新的药物靶标。