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（由适用提供）：哮喘是一种慢性呼吸道疾病，影响9.3％的儿童和8.0％的成年人。给定上蜡和减弱的症状，轻度至中度哮喘可能很难诊断和管理。哮喘构成的气流异常，支气管高反应性和气道注入受到挑战，可以定期，容易评估。鉴于鼻子进行评估和监测的可及性，在临床和科学上识别轻度/中度哮喘的鼻生物标志物是临床和科学上令人信服的。迄今为止，几种较低的气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。可以准确识别温和/中度哮喘并提供有关宿主与微生物贡献的信息及其对疾病的相对休闲性的鼻生物标志物对临床护理和研究非常有用。我们假设可以通过基于网络的鼻基因表达和微生物群的检查来鉴定轻度/中度哮喘的因果生物标志物。我们将招募患有轻度/中度哮喘，严重哮喘的受试者，并从中招募对照组，我们将在哮喘中生成第一个配对的宿主和微生物组的范围。在AIM 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/中度哮喘的鼻动物生物标志物。在AIM 2中，我们将对哮喘中的鼻微生物组进行首次研究，以鉴定轻度/中度哮喘的鼻微生物生物标志物。我们将从鼻拭子中生成16S rRNA数据，以识别与轻度/中度哮喘相关的细菌分类群，应用元基因组推断以确定其功能，执行元基因组测序，以鉴定非细菌分类群，并应用机器学习以区分轻度/中度心理的微生物分类器。我们将是第一个通过元基因组推断重建与哮喘相关的细菌功能的人，也是第一个将元基因组测序施加到良好的哮喘患者中。在AIM 3中，我们将通过数据驱动的网络方法来确定与遗传学，转录组，微生物组和临床数据的催化鼻生物标志物。我们将通过构建相互作用网络，通过EQTL检测来表征遗传变异和基因表达之间的关联，并通过贝叶斯网络构建来推断温和/中度哮喘的因果驱动因素来将宿主与微生物组联系起来。我们将把哮喘特异性子网投射到我们的网络上，并将我们的网络与其他呼吸道疾病的网络进行比较，以识别哮喘中基因和微生物的相干模块。总体而言，我们将通过测试严重哮喘患者的鼻和支气管样品中确定的生物标志物来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腔测试的发展，该测试可用于临床管理和调查轻度/中度哮喘，这种哮喘是一种目前次优诊断和管理的普遍疾病。