A pathway-based approach to find novel markers of local glucocorticoid treatment in intermittent allergic rhinitis

Abstract

To cite this article: Wang H, Chavali S, Mobini R, Muraro A, Barbon F, Boldrin D, Åberg N, Benson M. A pathway-based approach to find novel markers of local glucocorticoid treatment in intermittent allergic rhinitis. Allergy 2011; 66: 132–140.

Abstract

Background:  Glucocorticoids (GCs) may affect the expression of hundreds of genes in different cells and tissues from patients with intermittent allergic rhinitis (IAR). It is a formidable challenge to understand these complex changes by studying individual genes. In this study, we aimed to identify (i) pathways affected by local GC treatment and (ii) examine if those pathways could be used to find novel markers of local GC treatment in nasal fluids from patients with IAR.

Methods:  Gene expression microarray- and iTRAQ-based proteomic analyses of nasal fluids, nasal fluid cells and nasal mucosa from patients with IAR were performed to find pathways enriched for differentially expressed genes and proteins. Proteins representing those pathways were analyzed with ELISA in an independent material of nasal fluids from 23 patients with IAR before and after treatment with a local GC.

Results:  Transcriptomal and proteomic high-throughput analyses of nasal fluids, nasal fluid cells and nasal mucosal showed that local GC treatment affected a wide variety of pathways in IAR such as the glucocorticoid receptor pathway and the acute phase response pathway. Extracellular proteins encoded by genes in those pathways were analyzed in an independent material of nasal fluids from patients. Proteins that changed significantly in expression included known biomarkers such as eosinophil cationic protein but also proteins that had not been previously described in IAR, namely CCL2, M-CSF, CXCL6 and apoH.

Conclusion:  Pathway-based analyses of genomic and proteomic high-throughput data can be used as a complementary approach to identify novel potential markers of GC treatment in IAR.

Abbreviations

ALB

Albumin

ApoH

Apoliprotein H

CC16

Secretoglobin, family 1A, member 1

CCL2

Chemokine (C-C motif) ligand 2

CXCL6

Chemokine (C-X-C motif) ligand 6

GCs

Glucocorticoids

IAR

Intermittent allergic rhinitis

IPA

Ingenuity pathway analysis

iTRAQ

Isobaric tags for relative and absolute quantification

M-CSF

macrophage colony-stimulating factor 1

MIF

Macrophage migration inhibitory factor

TNFSF10

Tumor necrosis factor ligand superfamily member 10

VEGFB

Vascular endothelial growth factor B

The beneficial effects of treatment with glucocorticoids (GCs) in intermittent allergic rhinitis (IAR) are well documented (1–5). Decades long research efforts have shown that GCs have wide-spread effects on the inflammatory response (6, 7). Although perceived as generally down-regulatory, GCs have variable effects on inflammatory cells and their products. For example, while GCs inhibit apoptosis in eosinophils, the opposite is true for neutrophils (8). In previous studies of nasal fluids from patients with IAR, we and others have found that treatment with a topical GC results in decrease of the T helper 2 (Th2) cytokines, eosinophils, IgE, eosinophil cationic protein (ECP), but not T helper 1 (Th1) cytokines, nor IL-1β, TNF-α or neutrophils (9, 10). Changes in nasal fluid proteins may reflect the effects of GCs on both nasal fluid cells and the nasal mucosa. In the latter compartment, GCs affect not only inflammatory cells but also vascular permeability, and thereby plasma transudation which has an important role in IAR (4, 11). These changes are regulated by a large number of inflammatory proteins. Gene expression microarray studies of allergen-challenged CD4⁺ cells and nasal mucosa indicate that hundreds of genes change in expression following GC treatment (10, 12). It is a formidable challenge to understand such changes by studying individual genes one by one. An alternative strategy may be to change the scale to pathways enriched for genes whose expression changes in response to treatment with GC. Such pathways may be dissected to find potential protein markers encoded by the genes that change most in expression or are easiest to measure. This strategy was recently employed in nasal polyps before and after treatment with GC (13) but has to our knowledge not been applied to IAR. However, we have previously identified protein markers of untreated allergic inflammation by pathway-based analysis of gene expression microarray data from allergen-challenged skin and CD4⁺ cells from patients with IAR (14, 15). The aim of this study was to test if pathway-based analysis could be used to find potential markers of response to local GC treatment in IAR. To achieve this, proteomic high-throughput data from nasal fluids from patients with IAR were generated by the isobaric tags for relative and absolute quantification (iTRAQ) technique, which is at the forefront of proteomics techniques enabling multiplexed relative quantitation of proteins by mass spectrometry (MS) (16). Gene expression microarray data from nasal fluid cells and nasal mucosa from patients with IAR before and after treatment with GC were also analyzed. We then identified pathways in the resulting transcriptomal and proteomic high-throughput data. Finally, we examined if proteins that represented those pathways were differentially expressed in nasal fluids from an independent material consisting of patients with IAR before and after treatment with topical GC. This led to the identification of several novel GC-responsive nasal fluid proteins. The same analytical principles are likely to be generally applicable to find biomarkers in allergic disease.

Methods

Subjects

Intermittent allergic rhinitis was defined by a positive seasonal history and a positive skin prick test or by a positive ImmunoCap Rapid (Phadia, Uppsala, Sweden) to birch and/or grass pollen. Patients with perennial symptoms or asthma were not included. Nasal lavage samples from the patients were obtained after the start of symptoms during the pollen season, and after 2 weeks of treatment with two doses of 50 μg per dose fluticasone nasal spray in each nostril once daily. All patients before and after treatment with fluticasone were asked to mark their symptoms (sneezing, rhinorrhea, congestion and itching) on a visual analogue scale of 10. These values were added to the total symptom score, as previously described (9). Different materials were analyzed: One consisted of seven adult Swedish patients that were used for nasal fluid cell gene expression microarray and nasal fluid proteomic studies. The mean ± SEM age of these patients was 24 ± 5.8, and five were women. The mean ± SEM symptom score of these patients was 16.1 ± 2.9 vs 9.9 ± 2.2 (P = 0.189). We also re-analyzed previously described gene expression microarray data from nasal biopsies from three patients with IAR and three healthy controls during the pollen season, as well as nasal polyps from patients with IAR outside of the pollen season (the autumn–winter) before and after treatment with GC (12, 17). The nasal fluids from 23 Italian patients with IAR during the pollen season before and after treatment with fluticasone were used for ELISA measurement (henceforth referred to as the validation material). The mean ± SEM age of these patients was 32 ± 1.6, and 10 were women. The mean ± SEM symptom score of these patients was 20.9 ± 1.5 vs 7.2 ± 1.4 (P < 0.00001).

Nasal lavage fluid collection

Nasal lavage fluids were obtained as previously described (9). Sterile normal saline solution at room temperature was aerosolized into each nostril, while alternatingly clearing the other. The nasal fluids were allowed to return passively and collected in a graded test tube that was submerged in iced water, until 6 ml was recovered. The fluids were then filtered through a 30-μm Pre-Separation Filter (Miltenyi Biotec Inc., Bergisch-Gladbach, Germany) and centrifuged for 10 min at 1334 g in 4°C. The supernatant was separated from the pellet and stored in aliquots in −70°C until use. The pellet was lysed in 700 μl QIAzol (QIAGEN, Inc., Valencia, CA, USA) and stored in −70°C. The study was approved by the Ethics Committees of the Medical Faculties of the universities of Gothenburg and Padua.

iTRAQ-based proteomic analysis of nasal fluids

Nasal fluids from seven Swedish pollen allergic patients during the pollen season, before and after treatment with GC, were analyzed with iTRAQ-based proteomic analysis. Briefly, all nasal fluid samples were lyophilized, dissolved, alkylated and digested with trypsin according to the manufacture’s protocol (Applied Biosystems, Foster City, CA, USA). Each four-plex set consisting of one pooled standard sample and three nasal fluid samples was labeled with iTRAQ reagent 114, 115, 116 and 117, respectively, following manufacturer’s instructions (Applied Biosystems). Nano-LC-MS/MS analysis and iTRAQ data analysis were performed as described in the supplementary methods section (see Data S1 for a detailed description). Differentially expressed proteins were identified using R. Differentially expressed proteins with a fold change ≥1.5 or ≤−1.5 were selected for pathway analysis.

Microarray analysis of nasal fluid cells and nasal mucosa

Total RNA was isolated using the miRNeasy kit (QIAGEN, Inc). RNA concentrations were determined spectrophotometrically. The quantity of RNA was analyzed with NanoDrop ND-1000 UV Spectrophotometer (NanoDrop Technologies, Thermo Fisher Scientific Inc., Waltham, MA, USA). The RNA quality was examined in Agilent 2100 Bioanalyzer using RNA 6000 Pico kit and the RNA Integrity Number was calculated in Agilent 2100 Bioanalyzer expert software (Agilent Technologies, Inc., Palo Alto, CA, USA).

Sixty nanogram total RNA was used in the amplification and labeling reaction. The amplification and labeling protocol was performed using Illumina TotalPrep RNA Amplification kit (Ambion, Austin, TX, USA). Briefly, 1.5 μg of biotin labeled cRNA was used to hybridise onto Illumina Human-6 v3 Expression BeadChips (Illumina, Inc., San Diego, CA, USA). The hybridization, staining, washing and scanning were performed according to the manufacturer’s protocol (Illumina, Inc). The microarray analyses of nasal mucosal biopsies from patients with IAR and healthy controls, as well as from nasal polyps before and after treatment with GC, were performed as previously described (12, 17).

Pathway analysis of differentially expressed genes and proteins

Signal values and arrays were background corrected and normalized using the robust multiarray average. A two-tailed Student’s t-test and a fold-change criterium was used to identify genes that differed in expression, namely a fold change ≥1.5 or ≤−1.5, as previously described (15). The Ingenuity Pathways Analysis application (IPA) was used to map the differentially expressed transcripts and proteins on to known pathways. Next, immunological pathways that were statistically enriched for either differentially expressed transcripts or proteins were selected as previously described (18).

Analysis of nasal fluid proteins with ELISA

Extracellular proteins and genes in different pathways, which had high expression levels before or after treatment, were selected for ELISA analysis in nasal fluids from 23 Italian patients with IAR before and after treatment with GC. Albumin (ALB) was analyzed with an ELISA kit from Bethyl Laboratories (Montgomery, TX, USA). Apoliprotein H (apoH) was analyzed with an ELISA kit from United States Biological (Swampscott, MA, USA). Chemokine (C-C motif) ligand 2 (CCL2), chemokine (C-X-C motif) ligand 6 (CXCL6), macrophage colony-stimulating factor 1 (M-CSF) and tumor necrosis factor ligand superfamily member 10 (TNFSF10) were analyzed with ELISA kits from R&D Systems Inc (Minneapolis, MN, USA). Secretoglobin, family 1A, member 1 (CC16) was analyzed with an ELISA kit from Bio Vender Laboratory Medicine (Brno, Czech Republic). Eosinophil cationic protein was analyzed with an ELISA kit from IG Instrumenten-Gesellschaft AG (Zürich, Switzerland). Macrophage migration inhibitory factor (MIF) was analyzed with an ELISA kit from RayBiotech (Norcross, GA, USA). Vascular endothelial growth factor B (VEGFB) was analyzed with an ELISA kit from Gentaur (Brussels, Belgium). All experiments were performed according to the manufacturer’s protocol. Data were expressed as the mean ± SEM. Differences between two paired experimental groups were analyzed by paired Student’s t-test. A P-value <0.05 was considered significant.

Results

Gene expression microarray analysis of nasal fluid cells from patients with IAR

Nasal fluid cells from seven patients with IAR during the pollen season, before and after GC treatment, were analyzed with gene expression microarrays. This led to the identification of 25 up-regulated genes and 68 down-regulated genes (Table S1). Pathway analysis of the differentially expressed genes revealed that no known immune response pathway was significantly enriched for those genes. We therefore selected the extracellular protein that encoded by the most differentially expressed gene for ELISA analysis in the validation material, namely CXCL6.

Gene expression microarray analysis of nasal biopsies from patients with IAR

To identify putatively GC-affected pathways, gene expression microarray data from nasal polyps from patients with IAR outside of season before and after treatment with GC were analyzed (12). This resulted in the identification of pathways as well as a selection of novel protein markers for analysis in the independent material. Those markers are listed in parentheses following each pathway: Acute phase response signaling (albumin and apoH), chemokine signaling, glucocorticoid receptor signaling (CC16 and CCL2), IL8 signaling and T helper cell differentiation signaling (Table 1, see online supplement Table S2 for complete list). We also examined if novel markers for response to GC treatment could be identified in pathways that differed in gene expression microarray data from nasal biopsies patients with untreated IAR during the pollen season and healthy controls (17). The following pathways were enriched for differentially expressed genes, acute phase response signaling, complement system signaling, chemokine signaling, death receptor signaling (TNFSF10), glucocorticoid receptor signaling, IL4 signaling, IL8 signaling, role of macrophages, fibroblasts and endothelial cells in Rheumatoid Arthritis signaling (MCSF, MIF) and VEGF signaling (VEGFB) (Table 2, see online supplement Table S3 for complete list).

Table 1.   Differentially expressed extracellular genes in nasal polyps from patients with intermittent allergic rhinitis before and after treatment with glucocorticoids (GCs) ordered by pathways

Canonical pathway	Entrez Gene ID	Gene symbol	Fold change
The molecular functions of the genes are provided in online supplement (Table S5).
Acute phase response signaling	183	AGT	−1.91
	197	AHSG	−1.74
	213	ALB	−1.69
	259	AMBP	2.032
	335	APOA1	−2.931
	336	APOA2	−2.284
	350	APOH	−5.184
	735	C9	−2.047
	1401	CRP	−3
	2243	FGA	−4.511
	2244	FGB	−2.901
	57 817	HAMP	−1.825
	3240	HP	−2.171
	3263	HPX	−1.57
	3273	HRG	−1.805
	3606	IL18	3
	3552	IL1A	−2.065
	3553	IL1B	−3.286
	26 525	IL1F5	−4.443
	27 179	IL1F6	−1.58
	27 178	IL1F7	−5.382
	56 300	IL1F9	−2.128
	3557	IL1RN	−1.52
	3699	ITIH3	−1.98
	3700	ITIH4	−1.815
	3818	KLKB1	−1.744
	4153	MBL2	−4.309
	5004	ORM1	−1.616
	5950	RBP4	−2.615
	6288	SAA1	2.541
	5265	SERPINA1	2.996
	5054	SERPINE1	2.947
	7018	TF	1.702
	7124	TNF	−2.558
	7276	TTR	−2.067
Chemokine signaling	6347	CCL2	2.218
	6351	CCL4	−1.517
	6352	CCL5	−1.515
	6354	CCL7	−1.63
	6369	CCL24	1.643
	6387	CXCL12	−1.545
GC receptor signaling	183	AGT	−1.91
	6347	CCL2	2.218
	6348	CCL3	−2.806
	6352	CCL5	−1.515
	1437	CSF2	−1.964
	1447	CSN2	−2.068
	3458	IFNG	−2.576
	3558	IL2	−2.586
	3562	IL3	−5.806
	3565	IL4	−3.618
	3567	IL5	−1.618
	3576	IL8	−2.079
	3553	IL1B	−3.286
	3557	IL1RN	−1.52
	4312	MMP1	−2.091
	4878	NPPA	−2.079
	5443	POMC	−2.273
	5617	PRL	−2.919
	7356	CC16	4.116
	5054	SERPINE1	2.947
	7124	TNF	−2.558
IL8 signaling	285	ANGPT2	2.24
	2919	CXCL1	3.109
	80 864	EGF	−3.027
	2277	FIGF	−2.082
	1839	HBEGF	2.724
	3576	IL8	−2.079
	4318	MMP9	−1.714
	5228	PGF	1.86
T helper cell differentiation signaling	959	CD40LG	−2.667
	3458	IFNG	−2.576
	3558	IL2	−2.586
	3565	IL4	−3.618
	3567	IL5	−1.618
	3606	IL18	3
	59 067	IL21	−2.438
	3593	IL12B	−1.754
	3605	IL17A	−7.594
	7124	TNF	−2.558

Table 2.   Differentially expressed extracellular genes in nasal biopsies from patients with intermittent allergic rhinitis versus healthy controls ordered by pathways

Canonical pathway	Entrez Gene ID	Gene symbol	Fold change
The molecular functions of the genes are provided in online supplement (Table S5).
Acute phase response signaling	717	C2	10.949
	715	C1R	1.74
	716	C1S	−2.618
	720	C4A	2.945
	722	C4BPA	−3.849
	1356	CP	5.459
	2243	FGA	−2.416
	2266	FGG	−2.102
	3240	HP	66.644
	3557	IL1RN	1.686
	3699	ITIH3	−1.618
	3700	ITIH4	1.785
	4153	MBL2	−1.764
	5947	RBP1	1.548
	5949	RBP3	−1.643
	6288	SAA1	3.285
	12	SERPINA3	25.201
	5054	SERPINE1	7.329
	5176	SERPINF1	34.187
	710	SERPING1	196.342
	7124	TNF	3.449
	7450	VWF	46.486
Chemokine signaling	6347	CCL2	3.086
	6352	CCL5	1.98
	6354	CCL7	2.008
	6387	CXCL12	10.161
Complement signaling	717	C2	10.949
	715	C1R	1.74
	716	C1S	−2.618
	720	C4A	2.945
	722	C4BPA	−3.849
	731	C8A	−1.764
	733	C8G	1.506
	1675	CFD	1.877
	3075	CFH	17.297
	10 747	MASP2	2.249
	4153	MBL2	−1.764
Dendritic cell maturation signaling	1302	COL11A2	3.58
	1277	COL1A1	6.134
	1278	COL1A2	13.516
	1280	COL2A1	19.486
	1281	COL3A1	1.844
	3456	IFNB1	−3.032
	3500	IGHG1	310.029
	3586	IL10	−1.832
	3592	IL12A	−2.446
	3593	IL12B	−1.793
	3557	IL1RN	1.686
	51 561	IL23A	−1.589
	4049	LTA	4.814
	4050	LTB	2.258
	7124	TNF	3.449
Death receptor signaling	356	FASLG	−1.5
	7124	TNF	3.449
	8743	TNFSF10	17.424
	8742	TNFSF12	2.865
GC receptor signaling	6347	CCL2	3.086
	6352	CCL5	1.98
	1447	CSN2	−1.611
	2266	FGG	−2.102
	3458	IFNG	1.954
	3565	IL4	12.32
	3576	IL8	−1.726
	3586	IL10	−1.832
	3596	IL13	2.149
	3557	IL1RN	1.686
	5443	POMC	1.958
	7356	SCGB1A1	1.659
	5054	SERPINE1	7.329
	7040	TGFB1	3.614
	7124	TNF	3.449
IL4 signaling	3565	IL4	12.32
IL8 signaling	284	ANGPT1	−1.507
	285	ANGPT2	3.933
	2919	CXCL1	2.706
	1667	DEFA1	−1.707
	1950	EGF	2.879
	2277	FIGF	−1.811
	1839	HBEGF	1.727
	3576	IL8	−1.726
	4318	MMP9	2.317
	7423	VEGFB	7.89
	7424	VEGFC	−1.966
Role of macrophages, fibroblasts and endothelial cells in Rheumatoid Arthritis	715	C1R	1.74
	716	C1S	−2.618
	6347	CCL2	3.086
	6352	CCL5	1.98
	1435	MCSF	9.076
	6387	CXCL12	10.161
	2247	FGF2	1.781
	2277	FIGF	−1.811
	3240	HP	66.644
	3500	IGHG1	310.029
	3574	IL7	−1.717
	3576	IL8	−1.726
	3586	IL10	−1.832
	3557	IL1RN	1.686
	5653	KLK6	4.589
	11 012	KLK11	15.03
	4049	LTA	4.814
	4050	LTB	2.258
	4282	MIF	4.155
	4314	MMP3	−2.018
	4322	MMP13	−1.53
	25 891	PAMR1	−1.603
	5155	PDGFB	4.841
	5644	PRSS1	−1.599
	5646	PRSS3	2.114
	5657	PRTN3	−1.722
	7040	TGFB1	3.614
	7124	TNF	3.449
	7423	VEGFB	7.89
	7424	VEGFC	−1.966
	11 197	WIF1	−1.623
	7471	WNT1	−1.523
	54 361	WNT4	5.22
	7475	WNT6	3.53
	7480	WNT10B	2.883
	7474	WNT5A	4.618
	7476	WNT7A	2.19
VEGF signaling	2277	FIGF	−1.811
	7423	VEGFB	7.89
	7424	VEGFC	−1.966

iTRAQ-based protein profiling of nasal fluids from patients with IAR

Protein profiling of nasal fluids from patients with IAR during the season, before and after treatment with GC, was performed. Among 451 identified proteins, 62 proteins increased and 71 proteins decreased. The pathway analysis using the IPA software showed that two immunological pathways were significantly enriched for differentially expressed proteins, namely acute phase response signaling and complement system signaling (Table 3). These two pathways had also been identified by the gene expression microarray analysis of nasal mucosa and protein markers selected based on that. We also noted that the most differentially expressed proteins included proteins that were not identified by the pathway analysis, for example three cystatins, cystatin-SN (CST1), cystatin-S (CST4) and cystatin-D (CST5), all of which increased (Table S4).

Table 3.   Differentially expressed extracellular proteins in nasal fluids from patients with intermittent allergic rhinitis during season before and after treatment with glucocorticoids ordered by pathways

Canonical pathway	Entrez Gene ID	Gene symbol	Fold change
The molecular functions of the genes are provided in online supplement (Table S5).
Acute phase response signaling	259	AMBP	−1.506
	325	APCS	−1.979
	727	C5	−1.695
	716	C1S	−1.669
	3700	ITIH4	−1.609
	3818	KLKB1	−1.581
	3053	SERPIND1	−1.505
Complement system signaling	727	C5	−1.695
	716	C1S	−1.669
	732	C8B	−1.809

Analysis of nasal fluid proteins with ELISA in 23 patients with IAR

We analyzed if the proteins representing the different pathways in nasal fluids and biopsies would also change in an independent material consisting of nasal fluids from 23 patients with active IAR before and after treatment with GC. The following proteins were analyzed with ELISA, ALB and apoH (acute phase response signaling in nasal polyps), CCL2 and CC16 (glucocorticoid receptor signaling in nasal polyps), M-CSF and MIF (role of macrophages, fibroblasts and endothelial cells in rheumatoid arthritis pathway in nasal biopsies), TNFSF10 (death receptor signaling in nasal biopsies) and VEGFB (VEGF signaling in nasal biopsies). We also analyzed the extracellular protein that encoded by the most differentially expressed gene in nasal fluid cells with ELISA, namely CXCL6 (Fig. 1). As a control we measured ECP which is known to decrease in patients with IAR following treatment with GC. Indeed, ECP decreased from 25.5 ± 1.6 before treatment to 20.1 ± 2.0 after treatment (P < 0.003). By contrast, CCL2 and M-CSF increased after treatment (P < 0.023 and P < 0.004, respectively) while CXCL6 and apoH decreased (P < 0.048 and P < 0.007, respectively) (Fig. 1). However, the other proteins did not change significantly, namely ALB (14.6 ± 1.2 vs 15.7 ± 1.1, P = 0.211), MIF (2727.5 ± 310.6 vs 2281.1 ± 289.7, P = 0.292), VEGFB (110.4 ± 8.7 vs 113.8 ± 9.3, P = 0.747), CC16 (22.2 ± 1.7 vs 23.3 ± 2.3, P = 0.478) and TNFSF10 (412.4 ± 75.9 vs 331.4 ± 60.6, P = 0.187).

Figure 1.  Analysis of proteins representing different pathways using ELISA. Nasal fluids from 23 Italian patients with intermittent allergic rhinitis before and after treatment were analyzed. Before_T, patients before treatment with glucocorticoids (GC); After_T, patients after treatment with GC.

Discussion

The identification of nasal fluid protein markers for response to GC treatment in IAR is complicated by the involvement of multiple cells and mediators in different compartments. In this study, we aimed to examine if novel protein markers could be identified by pathway-based analyses of genomic and proteomic high-throughput data from patients with IAR. Because nasal fluid proteins may be derived from nasal fluid cells as well as the nasal mucosa and plasma exudation, we analyzed not only nasal fluids but also nasal fluid cells and the nasal mucosa. To increase the feasibility of the identification of potential protein markers, we focused on differentially expressed extracellular genes that had high expression levels before or after GC treatment. Using this approach, we found that GC affected a wide variety of pathways, reflecting the complexity of allergic inflammation. In an independent analysis, we found that the observed changes in pathways were reflected by nasal fluid proteins that represented those pathways. There were considerable individual variations indicating that combinations of proteins that reflect those pathways may have potential as biomarkers for response to GC treatment in IAR.

To identify pathways and proteins that might be affected by GC in the nasal mucosa, we analyzed gene expression microarray data from nasal polyps from patients with IAR outside of the pollen season before and after treatment with GC. Nasal polyps may be used as a model for IAR because of their similarities in pathophysiological mechanisms and that polyps are more readily accessible for biopsy studies (19). However, we also analyzed gene expression microarray data from nasal biopsies from patients with untreated IAR during the season and healthy controls. This led to the identification of a wide variety of pathways, several of which have previously been implicated in IAR, such as IL4 signaling, IL8 signaling and VEGF signaling (20). Moreover, the effects of GCs on proteins representing those pathways have been studied in nasal fluids from patients with IAR (9). In this study, we focused on selecting proteins that had not previously been studied in nasal fluids from GC-treated patients.

Proteomic analysis of nasal fluids showed that the most significant pathways were acute phase response signaling and complement system signaling. These pathways contained serum proteins that enter the nasal fluids through extravasation from postcapillary venules. This process is thought to have a key role in IAR in that it allows acute local delivery of inflammatory cells and proteins as well as fluid that helps wash away pollen and cellular debris (21). Previous studies indicate that GC inhibits plasma extravasation in natural IAR but has more complex effects in allergen-challenge studies (22).

In contrast to the nasal mucosa and nasal fluids, gene expression microarray analysis of nasal fluid cells resulted in a small number of differentially expressed genes, which did not belong to any known immune-related pathway. This may indicate that, from an inflammatory perspective, nasal fluid cells play a smaller role than nasal mucosal cells and plasma exudation. We selected one of the most differentially expressed genes, CXCL6, a chemokine that has not been previously described in IAR.

The selected proteins were analyzed in an independent material of nasal fluids from patients with active IAR before and after GC treatment. We found that four novel proteins did change in concentrations following treatment with GC, namely CCL2, M-CSF, CXCL6 and apoH. As expected, ECP levels showed a significant decrease. CCL2 and CXCL6 are chemoattractants for T cells and eosinophils that increase in asthma (23–26). M-CSF induces proliferation of T cells (27) but has not been previously described in allergy. ApoH is a pleiotropic serum protein, which is novel in allergy, but has been implicated in Th2-like responses in Sjögren’s syndrome (28).

However, five other proteins that were selected based on the pathway analysis did not change in the independent material. One reason could be lack of compliance. The concurrent decrease of ECP makes this less likely. Other explanations include the limitations of high-throughput studies. Changes in mRNA expression detected by microarrays do not necessarily reflect corresponding protein changes. Further, the results obtained from iTRAQ-based high-throughput proteomic method could be affected by the methodological limitations like error prone reassembly process and difficulties in differentiating between isoforms. Additionally, the pathway-based analysis may be confounded by limited knowledge about pathways and how those differ between cells and tissues. For example, the proteomic analysis of nasal fluid showed that three cystatins, namely CST1, CST4 and CST5, were among the proteins whose levels increased most following GC treatment. These cystatins were not part of a pathway that changed significantly and therefore not selected for analysis in the validation material in this study (29). However, these proteins may have an important role in IAR as well as in explaining the beneficial effects of GC treatment: they may decrease the IgE-inducing immunogenicity of cysteine protease allergens (29). On the other hand, pathway-based analysis has the advantage of detecting groups of functionally related genes whose expression change together. This may imply greater biological significance that changes of individual genes. Moreover, it is likely that in the near future improved knowledge of pathways may increase the efficacy of pathway-based analysis to identify diagnostic markers. Another problem is the variability of protein expression. An example of this is CC16, an anti-inflammatory protein, whose expression levels changed in the high-throughput analyses, but not in the independent material. Previous studies have shown contradictory effects of GCs on CC16 expression, which may also be linked to genetic variants in this gene (30–33).

It is increasingly recognized that gene expression is at least in part genetically determined. Thus, individual variations in gene and protein expression may be one explanation for the difficulties in finding biomarkers in IAR (34). On the other hand, such variations may reflect individual variations in disease severity and response to treatment. Another important aspect is variable dilution of nasal fluids. However, because that dilution factor is the same for each protein in the same nasal fluid sample, this suggests that the relations between combinations of proteins may be used as diagnostic markers. The diagnostic potential of combinations of proteins has been demonstrated in cardiac diseases (35). Further studies of large materials are warranted to examine individual variations and if combinations of proteins can be used as diagnostic markers in IAR and other allergic diseases. Ideally, such studies should be based on parallel analyses of nasal fluid cells, nasal fluids and the nasal mucosa using combined genomic and proteomic high-throughput methods.

In summary, pathway analysis of genomic and proteomic high-throughput data may be used as a complementary approach to identify potential diagnostic markers in IAR. The same principle may be generally applicable to allergic diseases.

Acknowledgment

We thank The Proteomics Core Facility at Sahlgrenska Academy, University of Gothenburg. We thank Dr Nedergaard-Larsen of ALK-Abello for providing allergen extract. This work was supported by the European Commission and the Swedish Research Council.