Nasal biomarkers of asthma

 描述(申请人提供)：哮喘是一种慢性呼吸道疾病，在美国9.3%的儿童和8.0%的成年人受到影响。轻度到中度哮喘可能很难诊断和治疗，因为有消退和消退的症状。导致哮喘的气流阻塞、支气管高反应性和呼吸道炎症是常规和容易评估的挑战。考虑到鼻部评估和监测的可及性，确定轻度/中度哮喘的鼻部生物标志物在临床上和科学上都具有迫切性。到目前为止，几种下呼吸道病原体与哮喘有关。在单独的研究中，呼吸道中宿主基因的表达与哮喘有关。毫无疑问，宿主和微生物在哮喘中相互作用。鼻腔生物标记物可以准确地识别轻/中度哮喘，并提供宿主与微生物的贡献以及它们与疾病的相对因果关系的信息，将对临床护理和研究非常有用。我们假设轻度/中度哮喘的病因生物标志物可以通过基于网络的鼻部基因表达和微生物区系的检查来识别。我们将招募患有轻度/中度哮喘、重度哮喘和对照的受试者，我们将从他们那里产生哮喘患者的第一对系统范围的宿主和微生物组图谱。在目标1中，我们将重点研究宿主特征，并通过鼻刷RNA序列分析、差异基因表达分析和机器学习来识别轻/中度哮喘的鼻转录生物标志物。在目标2中，我们将对哮喘的鼻部微生物组进行首次研究，以确定轻/中度哮喘的鼻部微生物生物标志物。我们将从鼻拭子中产生16S rRNA数据来识别与轻/中度哮喘相关的细菌分类群，应用元基因组学推断来确定它们的功能，执行元基因组测序来识别非细菌分类群，并应用机器学习来区分轻/中度哮喘的微生物分类器。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应用于特征良好的哮喘患者的人。在目标3中，我们将通过整合遗传、转录组、微生物组和临床数据的数据驱动的网络方法，识别轻度/中度哮喘的鼻部生物标记物。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，并通过构建贝叶斯网络来推断轻/中度哮喘的病因。我们将把哮喘特有的子网络投射到我们的网络上，并将我们的网络与其他呼吸系统疾病的网络进行比较，以识别哮喘中调节失调的基因和微生物的连贯模块。在所有目标中，我们将通过在重症哮喘患者的鼻腔和支气管样本中检测已识别的生物标记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，可用于轻/中度哮喘的临床管理和调查，这是一种目前诊断和治疗不太理想的流行疾病。

 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.