Immunological profiling in chronic rhinosinusitis with nasal polyps reveals distinct VEGF and GM-CSF signatures during symptomatic exacerbations

Abstract

Background: The mechanisms and immune pathways associated with chronic rhinosinusitis (CRS) are not fully understood. Immunological changes during acute exacerbation of CRS may provide valuable clues to the pathogenesis and perpetuation of the disease.

Objective: To characterize local and systemic immune responses associated with acute worsening of sinonasal symptoms during exacerbation in CRS with nasal polyps (CRSwNP) compared to controls.

Methods: This was a non-interventional prospective study of individuals with CRSwNP and normal controls. Subjects underwent a baseline visit with collection of nasal secretions, nasal washes, and serum specimens. Within 3 days of acute worsening of sinonasal symptoms, subjects underwent a study visit, followed by a post-visit 2 weeks later. The sinonasal outcome test-22 (SNOT-22) scores and immunological parameters in the specimens were analysed using a novel, unsupervised learning method and by conventional univariate analysis.

Results: Both CRSwNP patients and control subjects showed a significant increase in SNOT-22 scores during acute exacerbation. Increased nasal levels of IL-6, IL-5, and eosinophil major basic protein were observed in CRSwNP patients. A network analysis of serum specimens revealed changes in a set of immunological parameters, which are distinctly associated with CRSwNP but not with controls. In particular, systemic increases in VEGF and GM-CSF levels were notable and were validated by a conventional analysis.

Conclusions: CRSwNP patients demonstrate distinct immunological changes locally and systemically during acute exacerbation. Growth factors VEGF and GM-CSF may be involved in the immunopathogenesis of subjects with CRS and nasal polyps experiencing exacerbation.

Keywords: Chronic rhinosinusitis; GM-CSF; VEGF; exacerbation; human rhinovirus; nasal polyposis.

Figure 1

Study design and measurement scheme. See the Methods section for details.

Figure 2

Changes in SNOT-22 scores before (Visit 1), during (Visit 2), and after (Visit 3) acute exacerbation. Data are presented as median (horizontal bars), quartiles (boxes), and range (vertical lines); n=10 for controls, n=9 for CRSwNP.

Figure 3

Nasal levels of IL-6, IL-5 and MBP before (Visit 1) and during (Visit 2) acute exacerbation. Nasal secretions were collected before and during exacerbation and processed as described in the Methods section. Concentrations of inflammatory markers in the supernatants were analyzed by multiplex assay and radioimmunoassay. Data are presented as median (horizontal bars) and quartiles (boxes); n=10 for controls, n=9 for CRSwNP.

Figure 4

Unsupervised network analysis of patients and serum biomarkers during exacerbation. An initial bipartite network of 16 subjects (white nodes) and their 39 biomarkers (gray nodes) showing a random layout with subjects in center and biomarkers in periphery (Panel A). Once force-directed and weighted degree centrality algorithm was applied, the network revealed presence of structure and visual association of nodes (Panel B). Closer nodes represent strongly associated nodes. Node size was set between 15 to 60.

Figure 5

Unsupervised clustering of subject and biomarker nodes in the network during acute exacerbation. (Panel A) Hierarchical agglomerative clustering with heat map and dendrogram based on normalized biomarker values is shown. Breaks in dendrograms (red lines) were assigned by calculating significant breaks in joining distance of clusters (p<0.05). Four clusters of biomarkers (A through D) and two clusters of subjects (1 and 2) were revealed. (Panel B) Subject clusters and membership of biomarkers are overlaid on the network as shown Figure 4B. The nodes for CRS cases (black), controls (white) and biomarker clusters (gray, pink, orange and blue) are color-coded. Subject cluster 1 (blue line) and cluster 2 (green line) define boundaries of these subject clusters. (Panel C) Membership of individual biomarker clusters is presented.

Figure 6

Association between patient clusters and biomarker clusters during acute exacerbation is presented (Panel A). Horizontal bars, boxes and vertical lines are median, quartiles and range, respectively, of aggregate edge-weights of the subject clusters (1 and 2) to biomarker clusters (A to D). Comparison is made between control-dominant (cluster 1) and CRSwNP-dominant (cluster 2) clusters and four biomarkers. *; p<0.05 for inter cluster comparison per patient cluster while #; p<0.05 for biomarker cluster to biomarker cluster comparison between patient clusters. In Panel B, comparison of weighted degree centrality (node size) of biomarkers. Node size of each biomarker during exacerbation is presented; dotted line represents mean of all the biomarkers.

Figure 7

Change in association between selected serum biomarkers and subjects before (Visit 1) and during exacerbation (Visit 2). Edge weight of serum biomarkers (FGF, GM-CSF, Fractalkine, and VEGF) in relation to control and CRSwNP subjects was calculated for Visit 1 and Visit 2. Data are presented as median (horizontal bars), quartiles (boxes) and range (vertical lines) of edge weight values. Significant differences are depicted by horizontal lines.

Figure 8

Serum concentrations of FGF, GM-CSF, Fractalkine, and VEGF before (Visit 1) and during (Visit 2) acute exacerbation. Median (horizontal bars), quartiles (boxes) and range (vertical lines) are presented for each visit and each subject group. Significant differences between patient groups or between visits are depicted by horizontal lines.