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Zhang et al. J Transl Genet Genom 2018;2:18. I https://doi.org/10.20517/jtgg.2018.22 Page 3 of 11

hyper-responsiveness from methacholine challenge producing more than 20% decrease in FEV1 of less than
[10]
8 mg/mL. Asthma control was assessed using the asthma control test (ACT) . There are some asthmatics
who do not respond to albuterol. We also included patients who had a history and physical exam consistent
with asthma (wheezing, shortness of breath with absence of history of chronic obstructive pulmonary
disease or other lung disease) in conjunction with reduced FEV1/forced vital capacity (FVC) ratio < 0.7,
or demonstrated evidence of airway hyper-reactivity as follows: greater than a 10% increase in FEV1 after
maximal anti-inflammatory treatment with 40 mg of prednisone for ≥ 1 week, or more than a 12% variability
in FEV1 on serial spirometry obtained at clinic visits over the span of 12 months.

RNA isolation
Venous blood was collected peripherally, then centrifuged at 300 rpm in a clinical centrifuge to isolate
plasma. 2 µL of 50 nmol/L synthetic cel-miR-39 was added as a “spike in” normalization to 500 µL of plasma
for RNA isolation. Then, 1.5 mL of TRIzol reagent was added and total RNA was extracted according to
the manufacturer’s protocol. Finally, RNA concentration was measured based on A260/280 with NanoDrop
Lite Spectrophotometer.

cDNA preparation
[7]
Expression of 39 miRNAs in plasma was screened with using our previously published protocols . Briefly,
up to 500 ng of total RNA was reverse transcribed to cDNA with the qScript miRNA cDNA synthesis kit.

MiRNA expression by qPCR
MiRNA quantification with qPCR was performed on the CFX384 real-time system. cDNA was diluted
1:10. Primers to each miRNA were obtained from integrated DNA technologies. Each sample was run in
quadruplet. A 2-step program was used as follows: 40 cycles of 95 ºC for 10 s and 60 ºC for 30 s. Sample cycle
threshold (Ct) values were normalized to cel-miR-39 to control for variability.

Statistics
Normally distributed data were analyzed by one-way analysis of variance with Tukey post-test for multiple
comparisons or Student’s t-test where appropriate. Fisher’s exact tests were used for categorical binary
variables, and Chi-squared test for categorical variables across more than two groups. Hierarchical cluster
[11]
analysis was performed in Cluster 3.0 using the average-linkage method .

RESULTS
Our previous work indicated that plasma miRNAs have the potential to identify asthma phenotypes, and
[7]
we identified a panel of 39 miRNAs that had potential to serve as non-invasive biomarkers in the blood . In
this study, we built on these findings and used qPCR to analyze their expression in n = 62 asthmatics that
spanned the range of severity, including difficult to control asthmatic subjects (demographics in Table 1).

We first asked whether miRNA expression profiles were associated with different clinical features of asthma.
A cluster analysis of the miRNA expression data identified four main clusters of subjects, which we labeled
Cluster 1-4 [Figure 1, X-axis]. The patterns of miRNA expression that led to definition of these clusters could
be generally separated into different 5 groups [Figure 1, Y-axis]. We then analyzed the miRNA patterns in
each of the clusters and determined whether they were associated with differences in demographics and
clinical features of asthma.

The miRNAs assigned to group 1 (Let7 family and miR-98) showed higher expression in Cluster 3 and 4
relative to Clusters 1 and 2 [Figure 2, Table 2]. However, we also observed differences in miRNA expression
between Cluster 1 and 2 as well, with expression generally being lower in the latter [Figure 2A, miR-98 as a
representative example].