Integrative-omics of the Disordered COPD Small Airway Epithelium

Weill Medical College of Cornell University42 enrolled

Overview

We aim to use an integrated network systems approach to analyze certain existing small airway epithelium (SAE) omic data sets at the genetic, epigenetic (methylation), gene expression, microRNA and metabolomic levels, to develop an initial model of network connectivities and key network pressure points relevant to SAE biology in health and disease.

Hypothesis: We hypothesize that the disordered differentiation of the SAE that characterizes COPD results from the complex interaction of cigarette smoke components with a hierarchy of genetic, epigenetic, gene expression and metabolomics network interactions as the BC differentiate into a mucociliary epithelium. Specific aim 1. Using an integrated network systems approach to analyze our extensive existing SAE omic data sets at the genetic, epigenetic (methylation), gene expression, microRNA and metabolomics levels, to develop an initial model of network connectivities and key network pressure points relevant to SAE biology in health and disease. Specific aim 2. To refine the model, a comprehensive omics data set will be collected at multiple time points as SAE BC of nonsmokers and COPD smokers differentiate to normal and disordered mucociliary epithelium (respectively) on air-liquid interface (ALI) culture, an in vitro model of SAE differentiation. The computational strategies from aim 1 will be used to improve the model with these data. Specific aim 3. To test and finalize the integrated network model, a parallel omics data set will be generated from BC from nonsmokers, and COPD smokers as they differentiate on ALI under conditions where key hubs will be up- or down-regulated and the differentiation process stressed under conditions mimicking the in vivo SAE environment. The end result will be an integrated systems model of SAE biology and how this is disordered in COPD.

Study Type

OBSERVATIONAL

Enrollment

Conditions

Smoking COPD

Eligibility

Sex: ALLMin age: 18 YearsMax age: 100 YearsHealthy volunteers:

Medical Language ↔ Plain English

Healthy nonsmokers (n=50) Inclusion criteria * Must be enrolled into IRB approved protocol #1204012331(all inclusion criteria from protocol #1204012331 thus applies to this protocol) * Self-reported never-smokers, with current smoking status validated by the absence of nicotine metabolites in urine (nicotine less than 2 ng/ml and cotinine less than 5 ng/ml) * Negative HIV serology Smokers with COPD (n=50) Inclusion criteria * Must be enrolled into IRB approved protocol #1204012331(all inclusion criteria from protocol #1204012331 thus applies to this protocol) * Self-reported current daily smokers with greater than or equal to 10 pack-yr, validated by any of the following: urine nicotine greater than 30 ng/ml or urine cotinine greater than 50 ng/ml * Meeting GOLD stages I-III criteria for chronic obstructive lung disease (COPD) based on postbronchodilator spirometry * Taking any or no pulmonary-related medication, including beta-agonists, anticholinergics, or inhaled corticosteroids * Negative HIV serology Healthy nonsmokers (n=50) Exclusion criteria * Unable to meet the inclusion criteria (all exclusion criteria from protocol #1204012331 applies to this protocol) * Evidence of malignancy within the past 5 years Smokers with COPD (n=50) Exclusion criteria * Unable to meet the inclusion criteria (all exclusion criteria from protocol #1204012331 applies to this protocol) * Individuals in whom participation in the study would compromise the normal care and expected progression of their disease * Evidence of malignancy within the past 5 years

Locations (1)

Weill Cornell Medical College

New York, New York, United States

Outcomes

Primary Outcomes

Identification of network pressure points relevant to SAE biology confirmed by analyze of SAE data sets.

Using an integrated network systems approach to analyze our extensive existing SAE omic data sets at the genetic, epigenetic (methylation), gene expression, microRNA and metabolomics levels, to develop an initial model of network connectivities and key network pressure points relevant to SAE biology in health and disease.

Time frame: One Year

Data from ClinicalTrials.gov

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