Impact of azithromycin treatment on macrophage gene expression in subjects with cystic fibrosis

Abstract

Background: Azithromycin treatment improves clinical parameters in patients with CF, and alters macrophage activation from a pro-inflammatory (M1) phenotype to a pro-fibrotic, alternatively activated (M2) phenotype. The transcriptional profile of cells from patients receiving azithromycin is unknown.

Methods: Gene expression in association with macrophage polarization, inflammation, and tissue remodeling was assessed from sputum samples collected from patients with CF. Transcriptional profiles and clinical characteristics, including azithromycin therapy, were compared.

Results: Expression of NOS2 and TNFα was decreased in subjects receiving azithromycin, whereas expression of M2-associated genes was unaffected. Principal component analysis revealed gene expression profiles consistent with M1- (MMP9, NOS2, and TLR4) or M2-polarization (CCL18, fibronectin, and MR1) in select subject groups. These expression signatures did not significantly correlate with clinical characteristics.

Conclusions: Pro-inflammatory gene expression was low in subjects receiving AZM. Genes were stratified into groupings characteristic of M1- or M2-polarization, suggesting that overall polarization status is distinct among patient groups.

Keywords: Azithromycin; Cystic fibrosis; Macrophage phenotype.

Figure 1

Expression of M1 and M2 genes in subjects receiving AZM. Gene expression was assessed via qRT-PCR, and normalized to the housekeeping gene GAPDH (seen on the Y-axis). Mean expression values ± SD for samples obtained both from initial and follow-up visit are depicted, stratified between groups of subjects receiving and not receiving AZM. Statistical analysis was performed using an unpaired t-test. Differences were deemed statistically significant with a p value <0.05.

Figure 2

Multifactorial analysis of restricted gene set. Factor analysis generated through evaluation of expression levels of the 8 genes analyzed from the initial sputum samples obtained from subjects with CF as identified from the first round of principal component analysis (PCA). (A) Weighted factors for each gene are listed, along with the Eigen values for each component (components 1 and 2) and the amount of variance encompassed by each component. (B) Classification of individuals into molecular phenotype subsets. Scores for components 1 and 2 were calculated for each individual based on the sum of weighted expression of all 8 genes. Set 1 includes subjects with low scores for both PC1 and PC2, Set 2 includes subjects with high PC1 scores and low PC2 scores, and Set 3 is comprised of subjects scoring low for PC1 and high for PC2. Each point on the scatter plot represents one subject. (C) Correlation analysis of gene expression. Non-parametric evaluation was performed using Spearman correlation analysis among genes of the restricted set generated by PCA. Correlation coefficients (r) and p-values are listed for each gene pair comparison. Statistically significant differences (p < 0.05) are shaded.

Figure 3

Comparison of individual biomarkers groups of subjects stratified by molecular phenotype generated by multifactorial analysis of gene expression of the restricted set as described in Figure 2. mRNA gene expression levels were measured in the sputum samples of each subject by qPCR and normalized to expression of GAPDH. Mean ± SEM are shown for each gene and compared by the non-parametric Kruskal-Wallis test. Asterisks indicate significant differences in gene expression (P < 0.05) in Sets 2 and 3 as compared to that of Set 1.