Nasal biomarkers of asthma

哮喘的鼻生物标志物

• 批准号: 9152652
• 负责人: Supinda Bunyavanich
• 金额: $ 2.08万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-05-01 至 2020-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9152652
• 关键词: Adult; Affect; Age; Asthma; Biological Markers; Biology; Bronchodilator Agents; Bronchoscopy; Cessation of life; Child; Chronic; Clinical Data; Clinical Management; Clinical Research; Complex; Computational Biology; Data; Decision Trees; Detection; Development; Diagnosis; Disease; Emergency department visit; Ensure; Environmental Exposure; Etiology; Gene Expression; Gene Expression Profiling; Genes; Genetic; Genetic Variation; Genomics; Goals; Hospitalization; Lasso; Lead; Learning; Life; Link; Lung; Lung diseases; Machine Learning; Metagenomics; Microbe; Microbial Genetics; Modeling; Molecular; Monitor; Nasal Epithelium; Network-based; Nose; Obstruction; Pharmaceutical Preparations; Quality of life; Quantitative Trait Loci; RNA Sequences; Recruitment Activity; Research Personnel; Respiratory Tract Infections; Ribosomal RNA; Role; Sampling; Swab; Symptoms; System; Systems Biology; Taxon; Testing; Tissue-Specific Gene Expression; Transcript; Validation; Walking; Waxes; airway inflammation; asthmatic; base; bronchial epithelium; candidate marker; case control; clinical care; clinical investigation; clinical practice; computer based statistical methods; epidemiologic data; forest; fungus; genetic analysis; metagenomic sequencing; microbial; microbial community; microbiome; microbiota; pathogen; predictive marker; public health relevance; reconstruction; success; tool; transcriptome; transcriptome sequencing; virus identification

 DESCRIPTION (provided by applicant): Asthma is a chronic respiratory disease that affects 9.3% of children and 8.0% of adults in the US. Mild to moderate asthma can be difficult to diagnose and manage given waxing and waning symptoms. The airflow obstruction, bronchial hyperresponsiveness and airway inflammation that underlie asthma are challenging to assess regularly and easily. Given the accessibility of the nose for assessment and monitoring, it is clinically and scientifically compelling to identify nasal biomarkers of mild/moderate asthma. To date, several lower airway pathogens have been associated with asthma. In separate studies, host gene expression in the airway has been associated with asthma. Host and microbes undoubtedly interact in asthma. A nasal biomarker that could accurately identify mild/moderate asthma and provide information on host vs. microbial contributions and their relative causality to disease would be highly useful for clinical care and research. We hypothesize that causal biomarkers of mild/moderate asthma can be identified through network-based examination of nasal gene expression and microbiota. We will recruit subjects with mild/moderate asthma, severe asthma, and controls from whom we will generate the first paired system-wide profiles of host and microbiome in asthma. In Aim 1, we will focus on host characterization and identify nasal transcript biomarkers of mild/moderate asthma by RNA-sequence profiling of nasal brushings, differential gene expression analysis, and machine learning. In Aim 2, we will perform the first study of the nasal microbiome in asthma to identify nasal microbial biomarkers of mild/moderate asthma. We will generate 16S rRNA data from nasal swabs to identify bacterial taxa associated with mild/moderate asthma, apply metagenomic inference to ascertain their functions, perform metagenomic sequencing for identification of non-bacterial taxa, and apply machine learning to distinguish microbial classifiers of mild/moderate asthma. We will be the first to reconstruct bacterial functions associated with asthma through metagenomic inference, and the first to apply metagenomic sequencing to well- characterized asthmatics. In Aim 3, we will identify causal nasal biomarkers of mild/moderate asthma through data-driven, network approaches that integrate genetic, transcriptome, microbiome, and clinical data. We will link host to microbiome by constructing interaction networks, characterize the association between genetic variation and gene expression by eQTL detection, and infer causal drivers of mild/moderate asthma through Bayesian network construction. We will project asthma-specific subnetworks onto our networks, and compare our networks to those for other respiratory diseases to identify coherent modules of genes and microbes dysregulated in asthma. In all aims, we will assess for relevance to asthma more broadly by testing for the identified biomarkers in nasal and bronchial samples from severe asthmatics. We expect that our results will lead to the development of a nasal test that can be used for the clinical management and investigation of mild/moderate asthma, a prevalent disease that is currently suboptimally diagnosed and managed.

 描述（由申请人提供）：哮喘是一种慢性呼吸道疾病，在美国影响9.3%的儿童和8.0%的成人。轻度至中度哮喘可能很难诊断和管理，因为症状时好时坏。作为哮喘基础的气流阻塞、支气管高反应性和气道炎症很难定期和容易地进行评估。鉴于鼻子可用于评估和监测，临床上和科学上都迫切需要识别轻度/中度哮喘的鼻生物标志物。迄今为止，几种下呼吸道病原体与哮喘有关。在单独的研究中，气道中的宿主基因表达与哮喘有关。宿主和微生物无疑在哮喘中相互作用。鼻生物标志物，可以准确地识别轻度/中度哮喘，并提供信息的主机与微生物的贡献和它们的相对因果关系的疾病将是非常有用的临床护理和研究。我们假设，轻度/中度哮喘的因果生物标志物可以通过基于网络的鼻腔基因表达和微生物群检查来识别。我们将招募轻度/中度哮喘、重度哮喘和对照受试者，我们将从他们中生成哮喘中宿主和微生物组的第一对全系统概况。在目标1中，我们将专注于宿主表征，并通过鼻刷的RNA序列分析、差异基因表达分析和机器学习来识别轻度/中度哮喘的鼻转录物生物标志物。在目标2中，我们将进行哮喘鼻微生物组的首次研究，以确定轻度/中度哮喘的鼻微生物生物标志物。我们将从鼻拭子中生成16 S rRNA数据，以识别与轻度/中度哮喘相关的细菌分类群，应用宏基因组推断来确定它们的功能，进行宏基因组测序以识别非细菌分类群，并应用机器学习来区分轻度/中度哮喘的微生物分类器。我们将是第一个通过宏基因组推断重建与哮喘相关的细菌功能的人，也是第一个将宏基因组测序应用于充分表征的哮喘患者的人。在目标3中，我们将通过整合遗传学、转录组、微生物组和临床数据的数据驱动的网络方法来识别轻度/中度哮喘的因果性鼻生物标志物。我们将通过构建相互作用网络将宿主与微生物组联系起来，通过eQTL检测来表征遗传变异与基因表达之间的关联，并通过贝叶斯网络构建来推断轻度/中度哮喘的因果驱动因素。我们将在我们的网络上投射哮喘特定的子网络，并将我们的网络与其他呼吸系统疾病的网络进行比较，以确定哮喘中基因和微生物失调的相关模块。在所有的目标中，我们将通过检测严重哮喘患者鼻和支气管样本中已鉴定的生物标志物，更广泛地评估与哮喘的相关性。我们预计我们的结果将导致鼻测试的开发，该测试可用于轻度/中度哮喘的临床管理和研究，轻度/中度哮喘是一种目前诊断和管理不佳的流行疾病。