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Page 2 of 11 Zhang et al. J Transl Genet Genom 2018;2:18. I https://doi.org/10.20517/jtgg.2018.22

INTRODUCTION
[1]
Asthma affects approximately 300 million people worldwide and is associated with significant morbidity . It is
characterized by high burden of healthcare utilizations due to the cost of medications and hospitalizations/clinic
[2,3]
visits, and poor quality of life that includes chronic symptoms and frequent days of missed school/work .

Our understanding of asthma pathogenesis is still incomplete. However, there has been a recent paradigm
shift from thinking of asthma as a single disease, to now considering it a syndrome comprised of distinct
phenotypes. Several asthma phenotypes have been identified (based on clinical characteristics, biomarkers,
and response to treatment), including early onset allergic, late onset eosinophilic, exercise induced, obesity
[4]
related, and neutrophilic . However, assigning patients to these phenotypes is not always straightforward,
and these phenotypes may have overlapping clinical features and disease pathogeneses. As asthma arising
from different disease pathogeneses may produce different clinical courses with variable response to specific
asthma medications, it is important to develop biomarkers that have the ability to identify these subtypes at
the time of asthma diagnosis.

A number of recent studies have identified unique subtypes of asthmatics that were defined based on a
[5]
cluster analysis of clinical variables . These groups exhibited differences in eosinophil levels, severity, and
[5]
need for medications to control asthma . In particular, distinguishing between eosinophilic and non-
eosinophilic subtypes may be crucial for treatment, as it has been shown that non-eosinophilic asthmatics
[6]
respond poorly to corticosteroids . However, there may also be sub-phenotypes within a particular group
(i.e., subgroups of eosinophilic asthmatics) that are difficult to distinguish with current tools such as blood
eosinophil levels, allergen testing, and fractional exhaled nitrous oxide (FeNO). Blood eosinophil levels do
not always correlate with tissue eosinophil levels. Similarly, FeNO levels are not an accurate tool to measure
airway eosinophilia. Levels can be altered by medications and the presence of allergic rhinitis, and there
is an intermediate range of values that is difficult to interpret. Better tools are needed to more accurately
identify subgroups of asthmatics and provide more precise information. Ideal biomarkers would better
classify subtypes, help guide therapy, predict outcomes, and provide information about what immunological
pathways are de-regulated in the specific subtypes.

MicroRNAs (miRNAs) may be an important biomarker to address many of these issues. MiRNAs are short,
single stranded, non-coding RNAs that can regulate gene expression by interacting with mRNAs post
transcription. MiRNAs are involved in numerous disease processes across many cells and systems. MiRNAs
are readily detectable in blood, and expression profiles may be useful noninvasive biomarkers in various
[7-9]
diseases, including asthma . Our previous work has demonstrated that miRNAs can distinguish asthmatic
from non-asthmatics subjects, that specific miRNAs may de-regulated in eosinophilic asthmatics, and that
[7]
these molecules regulate inflammatory pathways in airway epithelial cells and T-cells . Thus, miRNAs may
represent an important bridge between molecular pathways and clinical entities.

Previous research showed that miR-125b, miR-16, miR-299-5p, miR-126, miR-206, and miR-133b levels
[7]
were predictive of allergic and asthmatic status . These, along with a host of other miRNAs, were found to
be differentially expressed amongst healthy, allergic, and asthmatic patients. In this study, we determined
whether these previously identified miRNAs could identify subgroups with differing response to treatments
and asthma control.

METHODS
Study population (sample size, characteristics of population)
This study was approved by Penn State College of Medicine Institutional Review Board. Participants signed
informed consent forms. Patients were classified as asthmatics based on history, and forced expiratory
volume in 1 s (FEV1) reversible by more than 12% and more than 200 mL after bronchodilator or airway