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Gene expression data from 131 human subjects in Detroit, Michigan

ABSTRACT: Background Complex diseases are often difficult to diagnose, treat and study due to the multi-factorial nature of the underlying etiology. Large data sets are now widely available that can be used to define novel, mechanistically distinct disease subtypes (endotypes) in a completely data-driven manner. However, significant challenges exist with regard to how to segregate individuals into suitable subtypes of the disease and understand the distinct biological mechanisms of each when the goal is to maximize the discovery potential of these data sets. Results A multi-step decision tree-based method is described for defining endotypes based on gene expression, clinical covariates, and disease indicators using childhood asthma as a case study. We attempted to use alternative approaches such as the Student’s t-test, single data domain clustering and the Modk-prototypes algorithm, which incorporates multiple data domains into a single analysis and none performed as well as the novel multi-step decision tree method. This new method gave the best segregation of asthmatics and non-asthmatics, and it provides easy access to all genes and clinical covariates that distinguish the groups. Conclusions The multi-step decision tree method described here will lead to better understanding of complex disease in general by allowing purely data-driven disease endotypes to facilitate the discovery of new mechanisms underlying these diseases. This application should be considered a complement to ongoing efforts to better define and diagnose known endotypes. When coupled with existing methods developed to determine the genetics of gene expression, these methods provide a mechanism for linking genetics and exposomics data and thereby accounting for both major determinants of disease.

ORGANISM(S): Homo sapiens

PROVIDER: GSE35571 | GEO | 2013/11/12

SECONDARY ACCESSION(S): PRJNA152221

REPOSITORIES: GEO

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Project description:Background: The first step in SARS-CoV-2 infection is binding of the virus to angiotensin converting enzyme 2 (ACE2) on the airway epithelium. Asthma affects over 300 million people world-wide, many of whom may encounter SARS-CoV-2. Epidemiologic data suggests that asthmatics who get infected may be at increased risk of more severe disease. Our objective was to assess whether maintenance inhaled corticosteroids (ICS), a major treatment for asthma, is associated with airway ACE2 expression in asthmatics. Methods: Large airway epithelium (LAE) of asthmatics treated with maintenance ICS (ICS+), asthmatics not treated with ICS (ICS-), and healthy controls (controls) was analyzed for expression of ACE2 and other coronavirus infection-related genes using microarrays. Results: AsResults: As a group, there was no difference in LAE ACE2 expression in all asthmatics vs controls. In contrast, subgroup analysis demonstrated that LAE ACE2 expression was higher in asthmatics ICS+ compared to ICS‾ and ACE2 expression was higher in male ICS+ compared to female ICS+ and ICS‾ of either sex. ACE2 expression did not correlate with serum IgE, absolute eosinophil level, or change in FEV1 in response to bronchodilators in either ICS- or ICS+. Conclusion: Airway ACE2 expression is increased in asthmatics on long-term treatment with ICS, an observation that should be taken into consideration when assessing the use of inhaled corticosteroids during the pandemic. a group, there was no difference in LAE ACE2 expression in all asthmatics vs controls. In contrast, subgroup analysis demonstrated that LAE ACE2 expression was higher in asthmatics ICS+ compared to ICS‾ and ACE2 expression was higher in male ICS+ compared to female ICS+ and ICS‾ of either sex. ACE2 expression did not correlate with serum IgE, absolute eosinophil level, or change in FEV1 in response to bronchodilators in either ICS- or ICS+. Conclusion: Airway ACE2 expression is increased in asthmatics on long-term treatment with ICS, an observation that should be taken into consideration when assessing the use of inhaled corticosteroids during the pandemic.

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Gene expression data from 131 human subjects in Detroit, Michigan

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