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.
Asthma impairs quality of life and leads to emergency room visits, hospitalizations, and sometimes death in people of all ages and walks of life. To identify noninvasive, predictive biomarkers for the diagnosis and management of asthma, we will comprehensively profile and examine nasal gene expression and the nasal microbial community in subjects with mild/moderate and severe asthma. We will use computational biology to construct networks that reflect the interactions and interconnectivity of host and microbes in asthma, and identify nasal biomarker candidates that we expect will lead to a nasal test for asthma.
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