Allergy Asthma Immunol Res.  2019 Sep;11(5):691-708. 10.4168/aair.2019.11.5.691.

In-Depth, Proteomic Analysis of Nasal Secretions from Patients With Chronic Rhinosinusitis and Nasal Polyps

• Kim YS
• Han D
• Kim J
• Kim DW
• Kim YM
• Mo JH
• Choi HG
• Park JW
• Shin HW

• KMID: 2452759
• DOI: http://doi.org/10.4168/aair.2019.11.5.691

Abstract

PURPOSE
Chronic rhinosinusitis (CRS) is a complex immunological condition, and novel experimental modalities are required to explore various clinical and pathophysiological endotypes; mere evaluation of nasal polyp (NP) status is inadequate. Therefore, we collected patient nasal secretions on filter paper and characterized the proteomes.
METHODS
We performed liquid chromatography-mass spectrometry (MS)/MS in the data-dependent acquisition (DDA) and data-independent acquisition (DIA) modes. Nasal secretions were collected from 10 controls, 10 CRS without NPs (CRSsNP) and 10 CRS with NPs (CRSwNP). We performed Orbitrap MS-based proteomic analysis in the DDA (5 controls, 5 CRSsNP and 5 CRSwNP) and the DIA (5 controls, 5 CRSsNP and 5 CRSwNP) modes, followed by a statistical analysis and a hierarchical clustering to identify differentially expressed proteins in the 3 groups.
RESULTS
We identified 2,020 proteins in nasal secretions. Canonical pathway analysis and gene ontology (GO) evaluation revealed that interleukin (IL)-7, IL-9, IL-17A and IL-22 signaling and neutrophil-mediated immune responses like neutrophil degranulation and activation were significantly increased in CRSwNP compared to control. The GO terms related to the iron ion metabolism that may be associated with CRS and NP development.
CONCLUSIONS
Collection of nasal secretions on the filter paper is a practical and non-invasive method for in-depth study of nasal proteomics. Our proteomic signatures also support that Asian NPs could be characterized as non-eosinophilic inflammation features. Therefore, the proteomic profiling of nasal secretions from CRS patients may enhance our understanding of CRS endotypes.

Keyword

Sinusitis; nasal polyps; proteomics

MeSH Terms

Asian Continental Ancestry Group
Gene Ontology
Humans
Inflammation
Interleukin-17
Interleukin-9
Interleukins
Iron
Metabolism
Methods
Nasal Polyps*
Neutrophils
Proteome
Proteomics
Sinusitis
Spectrum Analysis
Interleukin-17
Interleukin-9
Interleukins
Iron
Proteome

Figure

• 

  Fig. 1 Overall workflow. Workflow of the nasal secretions proteomic analysis. DDA, data-dependent acquisition; DIA, data-independent acquisition; CRSsNP, chronic rhinosinusitis without nasal polyp; CRSwNP, chronic rhinosinusitis with nasal polyp; DEP, differentially expressed protein; MED-FASP, multi-enzyme digestion filter aided sample preparation.

• 

  Fig. 2 Protein profiles in the DDA set. (A) Bar plot of total number of the identified proteins from the 3 technical replicates in the DDA set. Error bars were means ± standard deviations of the triplicates in control, CRSsNP and CRSwNP, respectively. (B) Comparison of the identified proteins in our study and other nasal sample proteomic studies. (C) Canonical pathway analysis representing significantly the up- or down-regulated canonical pathways in CRSsNP and CRSwNP compared to control. The values are activation Z-scores of the canonical pathways in order of the highest scoring with the values color indexed orange for the positive scores and blue for the negative scores. F, filter paper; SW, swab; SU, suction; L, nasal lavage; B, brushing; AR, allergic rhinitis; DDA, data-dependent acquisition; CRSsNP, chronic rhinosinusitis without nasal polyp; CRSwNP, chronic rhinosinusitis with nasal polyp; IL, interleukin; Cont, control; CCR3, C-C chemokine receptor type 3.

• 

  Fig. 3 Hierarchical clustering of differentially expressed proteins among 3 groups in the DDA set. A total of 1,666 proteins were identified between control, CRSsNP and CRSwNP in the DDA set (ANOVA, FDR < 0.05). The protein expression profiles were distinct from each other, and the technical triplicates were closest to each other. DDA, data-dependent acquisition; CRSsNP, chronic rhinosinusitis without nasal polyp; CRSwNP, chronic rhinosinusitis with nasal polyp.

• 

  Fig. 4 Clustergrams of the up- or down-regulated proteins in CRSwNP in the data-dependent acquisition set. Clustergrams of the proteins in the 3 clusters analyzed using Enrichr. The cluster of 20 proteins to the top 10 biological process terms were illustrated in cluster 1 (A), cluster 2 (B) and cluster 3 (C). CRSsNP, chronic rhinosinusitis without nasal polyp; CRSwNP, chronic rhinosinusitis with nasal polyp.

• 

  Fig. 5 Verification of the up- or down-regulated proteins in CRSwNP. (A) A total of 125 proteins were identified between control, CRSsNP and CRSwNP in the DIA set (ANOVA, P < 0.05). We clustered the up- or down-regulated proteins in CRSwNP compared to control and CRSsNP. Clustergrams of the upregulated proteins (B) and the downregulated proteins (C) both the DDA and DIA set. The clusters of proteins to the top 10 biological process terms were illustrated. DDA, data-dependent acquisition; DIA, data-independent acquisition; CRSsNP, chronic rhinosinusitis without nasal polyp; CRSwNP, chronic rhinosinusitis with nasal polyp.

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