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Human Molecular Genetics Advance Access originally published online on August 22, 2006
Human Molecular Genetics 2006 15(19):2923-2935; doi:10.1093/hmg/ddl234
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Downstream target genes of the neuropeptide S–NPSR1 pathway

Johanna Vendelin1, Sara Bruce4, Päivi Holopainen1,5, Ville Pulkkinen1, Paula Rytilä6, Asta Pirskanen8, Marko Rehn8, Tarja Laitinen8, Lauri A. Laitinen7, Tari Haahtela6, Ulpu Saarialho-Kere2,9, Annika Laitinen3 and Juha Kere1,4,*

1 Department of Medical Genetics, Biomedicum Helsinki, 2 Department of Dermatology and 3 Department of Anatomy, Biomedicum Helsinki, University of Helsinki, Helsinki, Finland, 4 Department of Biosciences and Nutrition, Clinical Research Centre, Karolinska Institutet, Karolinska University Hospital, Huddinge, Sweden, 5 SIAF, Davos, Switzerland, 6 Department of Allergy and 7 Department of Medicine, Helsinki University Central Hospital, Helsinki, Finland and 8 GeneOS Ltd, Helsinki, Finland and 9 Department of Dermatology, Karolinska Institutet at Stockholm Söder Hospital, Stockholm, Sweden

* To whom correspondence should be addressed. Tel: +358 46 86089158; Fax: +358 46 87745538; Email: juha.kere{at}biosci.ki.se

Received July 5, 2006; Accepted August 11, 2006


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 REFERENCES
 
The neuropeptide S (NPS)–NPS receptor 1 (NPSR1) pathway has recently been implicated in the pathogenesis of asthma. The purpose of this study was to identify downstream gene targets regulated by NPSR1 upon NPS stimulation. A total of 104 genes were found significantly up-regulated and 42 down-regulated by microarray analysis 6 h after NPS administration. By Gene Ontology enrichment analysis, the categories ‘cell proliferation’, ‘morphogenesis’ and ‘immune response’ were among the most altered. A TMM microarray database comparison suggested a common co-regulated pathway, which includes JUN/FOS oncogene homologs, early growth response genes, nuclear receptor subfamily 4 members and dual specificity phosphatases. The expression of four up-regulated genes, matrix metallopeptidase 10 (MMP10), INHBA (activin A), interleukin 8 (IL8) and EPH receptor A2 (EPHA2), exhibited a significant NPS dose–response relationship as confirmed by quantitative reverse-transcriptase–PCR and for MMP10 by immunoassay. Immunohistochemical analyses revealed that MMP10 and TIMP metallopeptidase inhibitor 3 (TIMP3) were both strongly expressed in bronchial epithelium, and macrophages and eosinophils expressed MMP10 in asthmatic sputum samples. Because remodeling of airway epithelium is a feature of chronic asthma, the up-regulation of MMP10 and TIMP3 by NPS–NPSR1 signaling may be of relevance in the pathogenesis of asthma.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 REFERENCES
 
Asthma is a chronic disorder of the airways, characterized by bronchial hyperreactivity, local inflammation and morphological changes. Asthma is familial, and already hundreds of studies have addressed possible candidate susceptibility genes, about 20 genome-wide scans have attempted to localize asthma genes and at least five genes have been implicated by positional cloning (1,2).

Among these positionally cloned asthma susceptibility genes, the association of NPSR1 (previously called also as GPR154, GPRA, VRR1 or PGR14) with either asthma, high serum IgE, sensitization or bronchial hyperreactivity has been confirmed in six European, the French Canadian and the Chinese population (3–6). NPSR1 belongs to the G protein-coupled receptor (GPCR) family and has a topology of seven transmembrane domains. NPSR1 has two full-length transcripts with alternative 3' exons (3), first called GPRA-A and GPRA-B, referred here as NPSR1-A and NPSR1-B, respectively. Positive NPSR1-B staining in the smooth muscle cells of each of eight bronchial biopsies from asthmatic samples and the lack of expression in 10 control samples suggested a universal role for NPSR1 in the pathogenesis of asthma and potentially, in bronchoconstriction. Furthermore, Npsr1 expression levels were up-regulated in a modified model of ovalbumin-sensitized mice, suggesting an immunomodulatory function for NPSR1 (3,7).

A 20-mer endogenous peptide agonist for NPSR1-A was first found in a proteomic screening (8). The ligand is a C-terminal proteolytic fragment of a precursor polypeptide shown to stimulate NPSR1-A by inducing both Gs and Gq pathways, thus eliciting intracellular cAMP and Ca2+ levels, respectively (7,9). The agonist was named neuropeptide S (NPS), and it has been suggested to regulate anxiety and arousal in rats (10).

The expression pattern of the Nps precursor and Npsr1 in rat tissues was examined by quantitative reverse-transcriptase (RT)–PCR, demonstrating that both are expressed in various tissues with the highest levels in different sections of the brain, in the thyroid and in the salivary and mammary glands (10). According to our results, in human, NPSR1 is also expressed in the skin, lung and gastrointestinal tract as shown by immunohistochemistry, whereas NPS is expressed in the bronchus and colon as shown by in situ hybridization (11). Thus, it is possible that NPS, acting via NPSR1, regulates diverse functions in different tissues. When studying cellular effects of NPS stimulation, we found that activation of NPSR1-A overexpressing cell lines (NPSR1-A cells) with NPS resulted in significant growth inhibition (11). Our recent studies with a mouse model and a macrophage cell line show that NPSR1 is up-regulated after antigen challenge and that NPS is capable of inducing phagocytosis of unopsonized bacteria, suggesting a role for the NPS–NPSR1 pathway in innate immunity (7). More recent studies have indicated that both NPSR1-A and NPSR1-B are equally functional as receptors for NPS and that the Asn107Ile polymorphism of NPSR1 increases signaling sensitivity upon NPS stimulation up to 10-fold (12–14).

Little is known yet about the downstream effects of NPSR1 activation by NPS. As a first attempt to characterize downstream targets of this pathway, we used stable NPSR1-A overexpressing human epithelial kidney 293H (HEK-293H) cells to monitor NPS stimulation effects using microarrays. Gene ontology (GO) Enrichment Analysis was done to look for the enrichment of certain gene groups, and the TMM microarray database was used to search for the groups of co-regulated genes within our list of differentially expressed genes. Dose–response experiments were used to verify the specificity of NPS stimulation effect on a selected set of genes. Immunohistochemistry was performed to compare the in vivo expression pattern of NPSR1 and selected NPSR1-regulated genes in bronchus sections and in sputum samples from asthmatics and healthy controls.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 REFERENCES
 
NPSR1-A mediates signals for inhibition of cell proliferation, but not activation of apoptosis
Cell growth was affected by NPS stimulation of the NPSR1-A cell lines as shown previously (11). Here we wanted to study whether cell proliferation or apoptosis was affected. According to our results, proliferation rate of the NPSR1-A cells was significantly (P<0.05) lower (2.6±0.7-fold change) when compared with the NPSR1-A negative cells (3.2±0.4-fold change) as measured with BrdU proliferation assay after 3 days of culture. The addition of NPS agonist did not increase the effect. The results represent averages of two different NPSR1-A positive clones and one NPSR1-A negative clone (transfection negative cells). Each cell line was cultured in triplicate wells and the experiment was replicated three times. Cyclohexamide (100 µg/ml) treated cells were used as negative control (1-fold change). The degree of apoptosis was also studied in NPSR1-A positive clones and HEK-293H parental cells with or without NPS treatment by a death end tunel detection system. There was no increase in the level of apoptosis detected between different cell samples (data not shown).

Microarray results
To study the downstream signaling of NPSR1-A upon NPS stimulation, we compared the NPS stimulated NPSR1-A cells with non-stimulated cell lines and NPS treated HEK-293H cells. Duplicate samples were assayed with the HGU133plus2 array, containing over 47 000 transcripts. The Pearson correlation coefficients of total log2 expression between replicates ranged between 0.9967 and 0.9971 and were consistently higher than the correlation between samples from different groups (data not shown; the complete microarray data are available through the ArrayExpress database, submission number E-MEXP-829). Furthermore, the overall correlations between the NPS stimulated HEK-293H samples and unstimulated NPSR1-A samples were consistently higher than their correlation to NPS stimulated NPSR1 samples (Supplementary Material, Fig. S1), indicating that the NPS stimulation had specific effects on the stimulated NPSR1-A cell expression profiles.

A B-value larger than 7 was chosen to define the cut-off level for differential expression after visual inspection of the extent of changes in the different contrasts (Fig. 1A). At this cut-off, there was a maximal number of differentially expressed genes in contrasts 1 and 2 (NPS stimulated versus unstimulated NPSR1-A cells and NPS stimulated NPSR1-A, versus NPS stimulated HEK-293H, respectively), and at the same time, the differential expression in contrast 3 (unstimulated NPSR1-A versus NPS stimulated HEK-293H) was minimized (Fig. 1A, red dots in B). There were 329 probes with a B-value above 7 in contrast 1 (red dots in Fig. 1C) and 283 in contrast 2 (red dots in Fig. 1D). To exclude genes that potentially could be differentially expressed as a consequence of NPSR1-A overexpression alone or non-NPSR1-A mediated NPS stimulation, we considered only those probes found in both contrasts 1 and 2. A total of 195 probes were found in both contrasts, corresponding to 146 unique genes. All the genes selected by this criteria have a false discovery rate adjusted P<0.0001. Of these genes, 104 were found to be up-regulated and 42 down-regulated (Supplementary Material, Table S1). The orientation of these genes in the log2 intensity versus log2 fold-change dimensions is shown in Figure 1C and D (green triangles, where it is also evident that all of these genes had ~2.1-fold or larger changes). Fourty-eight genes with at least 4-fold changes in at least one contrast are presented in Table 1. Two probes could not be matched with any gene (see Supplementary Material, Table S1 for Affymetrix identifiers). Some of the markedly down-regulated genes in contrast 2 were not found in contrast 1 (red dots lacking green triangles in Fig. 1D), representing genes that were expressed at high levels in NPS stimulated HEK-293H cells only (Fig. 1B). The extremes of this group were NEF3 (NM005382), WWOX (NM_016373 [GenBank] ) and COL2A1 (NM_001844 [GenBank] ).


Figure 2341
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  		Figure 1. Differential expression of NPSR1-A-regulated genes upon NPS challenge. (A) Titration of the number of differentially expressed genes at different significance cut-offs. Log odds of differential expression (B-values, see Materials and Methods) cut-offs are presented on the x-axis. Each contrast is represented by a color; red: NPSR1-A, stimulated versus NPSR1-A; green: NPSR1-A, stimulated versus HEK-293H, stimulated; blue: NPSR1-A versus HEK-293H, stimulated. A dashed vertical line is drawn at a B-value of 7. At this level, the red and green lines indicate differential expression in the order of 300 features, whereas the blue line is close to zero. (B) Logarithm 2 (log2) ratio versus intensity plot for NPSR1-A versus HEK-293H, stimulated. Each black circle corresponds to a probe set on the HGU133plus2 array. Probe sets with a B-value above 7 are shown in red. (C) Log2 ratio versus intensity plot for NPSR1-A, stimulated versus NPSR1-A. Each black circle corresponds to a probeset on the HGU133plus2 array. Probe sets with B>7 are colored in red. Green triangles depict those that are differentially expressed (B>7) in both (C and D). Two horizontal dashed lines are drawn at log2 fold-change of 1 and –1 (corresponding to an absolute fold-change of 2). (D) Log2 ratio versus intensity plot for NPSR1-A, stimulated versus HEK-293H, stimulated. For annotations, see (C).

 


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  		Table 1. Differentially expressed genes with fold changes larger than 4a

 
GO pathway analysis shows significantly up-regulated pathways
To look for enrichment of GO terms in the class Biological Process, the up- and down-regulated groups of genes were analyzed separately in the EASE software, using the total gene list from the HGU133plus2 array as reference. For the up-regulated genes, the most enriched term was cell proliferation (E-score=0.0006). Another enriched group, transcription, DNA-dependent (E-score=0.009), reflects the abundance of up-regulated early response genes in this assay (Supplementary Material, Table S2). Other significantly enriched groups were morphogenesis (E-score=0.001), immune response (E-score=0.02), cell communication (E-score=0.02), response to pest/pathogen/parasite/ (E-score=0.03) and chemotaxis (E-score=0.04) (Fig. 2). The enriched term response to stimulus connects all the enriched terms related to immunity and inflammation and consists of 20 genes presented here: BTG2, CD24, CXCL2, CTGF, CYR61, DUSP1, FOSL1, GNAS, GEM, INHBA, IL6R, IL8, ERRFI1, NR4A2, PTGS2, STC1, TAC1, TIMP3, TCF8 and FOS. The complete list of enriched terms is shown in Supplementary Material, Table S2, where the gene identifiers constituting the enriched groups are also presented. In the group of down-regulated genes, no terms were enriched in the EASE analysis.


Figure 2342
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  		Figure 2. Relationship between enriched GO Biological Process groups. The relation between all enriched terms (P-value/E-score<0.05) in the EASE analysis (NPS-NPSR1-A up-regulated genes) is shown in a directed acyclic network graph (from roots towards node). The boxes colored in gray correspond to those terms with an E-score below 0.05. The number in the parenthesis corresponds to the E-score assigned to this group.

 
TMM microarray database analyses reveals a common NPSR1-A-regulated pathway
We queried the TMM microarray database with all differentially expressed genes to determine whether any had previously been correlated with each other at a stringency level of at least three microarray experiments. When the complete list of differentially expressed genes was compared with the genes in the TMM results, we found a set of 43 co-regulated genes (of total 146), correlated with a minimum of two other genes in the group (mean 9.4 genes, median 7 genes). The majority of these genes are clearly early response genes (i.e. transcription factors). Sixteen genes showed correlations to more than 10 genes in this group and are listed below where the genes with the highest number of correlations given first: CYR61, JUNB, DUSP1, NR4A2, DUSP5, EGR1, NR4A1, FOSB, FOS, IER2, CXCL2, BTG2, EGR2, GADD45B, NR4A3 and EGR3 (see Supplementary Material, Table S3 for the complete set of co-regulated genes).

Expression of MMP10, INHBA, EPHA2 and IL8 is NPS concentration dependent
We found some of the NPSR1-regulated genes of particular interest and therefore the differential expression of MMP10, INHBA, EPHA2 and IL8 was confirmed with quantitative RT–PCR. These four genes were selected because INHBA is among the most significantly up-regulated genes (10.2-fold change) in the array results and is highly represented in GO pathways; MMP10 (11.3-fold change) was selected because little is known about MMP10 in asthma whereas many other MMPs such as MMP9 have been implicated (15); IL8 (2.7-fold change) is an important mediator of an inflammatory response (16) and EPHA2 (3.6-fold change) belongs to transforming growth factor beta (TGFB) superfamily like INHBA, therefore confirming the stimulation of this pathway. The cells cultured for quantitative analyses were treated and collected separately from the Affymetrix samples, and thus they represent biological replicates for the samples in the gene expression analyses. In the first RT–PCR assays, the NPSR1-A cells were stimulated with NPS (2 µM) for 6 h, and expression of EPHA2, MMP10, IL8 and INHBA in the NPSR1-A cells (with or without 2 µM NPS stimulation) was compared with their expresson level in HEK-293H cells (Fig. 3). Expression of INHBA showed the highest change after NPS stimulation (77-fold change). Expression levels of IL8, MMP10 and EPHA2 were increased 53-, 47- and 7.5-fold, respectively, in the stimulated NPSR1-A cells compared with unstimulated HEK-293H cells. The expression levels did not increase in the stimulated HEK-293H cells compared with unstimulated cells, indicating the specificity of NPS to NPSR1-A. In the case of MMP10 and IL8, there were significant changes (6- and 11-fold, respectively) even in the unstimulated NPSR1-A cells, suggesting the possibility of a modest level of autocrine signaling in these cells. To confirm this, the NPS expression levels in the stable cell lines were examined by quantitative RT–PCR. A basal expression level of NPS in the stable cell lines was detected (Ct=30; data not shown).


Figure 2343
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  		Figure 3. Expression of INHBA (activin A), interleukin 8 (IL8), matrix metallopeptidase 10 (MMP10) and EPH receptor A2 (EPHA2) upon NPS challenge of NPSR1-A cells. The HEK-293H parental cells (293H cells) and NPSR1-A cells were stimulated/treated for 6 h with (2 µM) NPS, parallel with unstimulated cells. The mRNA expression levels were measured by quantitative RT–PCR. The relative expression levels were compared with the basal expression levels (fold-change of 1) in 293H cells.

 
In a second set of experiments, the NPSR1-A cells were stimulated for 6 h with increasing concentrations (1 nM–5 µM) of NPS and the expression levels of IL8, INHBA, EPHA2 and MMP10 were compared with the expression in unstimulated NPSR1-A cells. Expression was NPS concentration dependent (Fig. 4A–D). However, 5 µM NPS concentration resulted in the same or lower level of expression. This is consistent with the earlier results obtained from GTP-binding assays (11), whereby no significant increase in GTP-binding was detected at high NPS concentrations (5–10 µM). In the case of MMP10, the peak expression level was seen at 0.1 µM NPS, whereas the peak expression levels were at 1 µM NPS for the other genes. These results demonstrate differences between genes in their responses to NPS–NPSR1 signalling.


Figure 2344
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  		Figure 4. Dose-dependent expression of (A) INHBA, (B) EPHA2, (C) IL8 and (D) MMP10 upon NPS stimulation of NPSR1-A cells. Varied NPS concentrations (1 nM–5 µM) were used to stimulate NPSR1-A cells for 6 h. The mRNA expression levels were measured by quantitative RT–PCR. The relative expression levels in the stimulated NPSR1-A cells were compared with those in unstimulated cells.

 
The NPSR1-A cells were also stimulated for 1, 2, 4, 6 and 10 h with 0.1 µM NPS and the expression levels of INHBA, EPHA2 and MMP10 were compared with the expression in unstimulated NPSR1-A cells collected at the same time points. As seen in Figure 5, INHBA levels were significantly above the basal levels even as early as at 1 h (3-fold change) and the highest expression level was detected at 4 h (28-fold change). MMP10 expression levels increased more slowly when compared with INHBA levels. The highest expression was detected at 10 h (26-fold change). Expression levels of EPHA2 stayed low across different early time points, but a significant increase was detected at 10 h (8-fold change).


Figure 2345
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  		Figure 5. Time-dependent expression of INHBA, EPHA2 and MMP10 upon NPS challenge. The NPSR1-A cells were stimulated with NPS (0.1 µM) for 1–10 h, and the mRNA expression levels were measured by quantitative RT–PCR. The relative expression levels in the stimulated NPSR1-A cells were compared with those in unstimulated cells at each time point.

 
Total MMP10 protein levels in cell supernatants of NPSR1-A cells upon NPS stimulation
The NPSR1-A cells were stimulated with 0.1–10 000 µM NPS for 24 and 48 h. Total MMP10 levels were detected with Human MMP-10 (total) Immunoassay. NPS dose–response was detected at both time points. The highest protein levels (3.8–5 ng/ml) were detected upon 100 nM NPS stimulation in agreement with the RNA expression results. There was no significant increase in protein concentrations at 48 h when compared with 24 h (Fig. 6).


Figure 2346
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  		Figure 6. MMP10 protein levels upon NPS challenge by Human MMP10 (total) Immunoassay. The NPSR1-A cells were stimulated with NPS (0.1–10 000 nM) for 24 and 48 h. Unstimulated cells were used as control. The MMP10 levels in cell supernatants were detected with MMP10 Immunoassay.

 
In vivo expression of MMP10, TIMP3 and NPSR1-A in the bronchus
To further validate the findings of the microarray experiment, two genes up-regulated by NPS–NPSR1-A activation were selected because of their potential involvement in tissue remodeling seen in asthma, namely MMP10 and TIMP3, and their expression patterns were studied in asthmatic and normal bronchial tissue sections. Strong expression of TIMP3 was observed in the lung epithelium (Fig. 7A) and, in some cases, specific staining in the basal cell layer could be detected. In addition, strong staining of TIMP3 was seen in subepithelial glands and some in the endothelial structures. MMP10 staining was positive in the epithelium (Fig. 7B), endothelium and smooth muscle, and faint staining was seen also in subepithelial glands. However, the staining pattern differed between different samples and some samples lacked smooth muscle staining. By visual inspection, we could not detect major changes in the expression levels of TIMP3 and MMP10 between asthmatics and controls. For NPSR1-A, the staining of smooth muscle and the subepithelial glands was high as shown earlier (3,11). However, NPSR1-A was also strongly expressed in the lung epithelium (Fig. 7C). Therefore, on the basis of their co-localization, NPSR1-A could be a putative regulator of TIMP3 and MMP10 production in the lungs.


Figure 2347
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  		Figure 7. Expression of (A) TIMP3, (B) MMP10 and (C) NPSR1-A in the normal bronchus as detected by immunohistochemistry. Co-localized expression in the epithelium is detected.

 
Expression of MMP10 in sputum samples of asthmatic patients and healthy controls
Sputum samples from five asthmatic and three control individuals were stained for MMP10. There were no significant differences between asthmatics and controls: macrophages were MMP10 positive, whereas epithelial cells and neutrophils were negative. Also some MMP10 positive eosinophils were detected in asthmatic samples. Negative controls incubated with mouse immunoglubulins showed no staining (Fig. 8).


Figure 2348
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  		Figure 8. (A) Immunohistochemical staining of a human sputum sample from an asthmatic patient with monoclonal anti-MMP10 antibodies. Macrophages and eosinophils are positively stained. (B) The non-immune rabbit IgG fraction was used as a negative control.

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 REFERENCES
 
In this study, we examined the NPS agonist-induced and the NPSR1-mediated downstream target genes in NPSR1-A overexpressing cells. By microarray data analyses, we revealed novel downstream target genes for NPSR1-A upon NPS stimulation. The expression of four selected genes, MMP10, INHBA, EPHA2 and IL8, showed a significant dose–response to NPS as confirmed with quantitative RT–PCR. Also, differences in expression of INHBA, MMP10 and EPHA2 could be detected across different time points upon NPS stimulation, and a dose–response to NPS could be confirmed for MMP10 at protein level.

Our comparison with publicly available microarray data showed a clear co-regulation pattern of several interesting genes, adding more confidence to the quality of the data. We suggest here that NPS acting via the NPSR1-A receptor affects cell proliferation, morphogenesis and immune response as revealed by GO database searches. In addition, we show by immunohistochemistry that the NPSR1-A regulated genes MMP10 and TIMP3 are co-localized with NPSR1-A in the bronchial epithelium. Furthermore, MMP10 is also co-localized with NPSR1-A in macrophages and eosinophils of sputum samples from asthmatics and controls.

The decreased NPSR1-A cell proliferation rate detected with the BrdU proliferation assay is consistent with the results of the previously published cell growth assays (11). Moreover, our GO pathway results strongly implicate the proliferative effects being one of the main regulative functions of NPSR1 because proliferation was the most enriched group. Even though the NPS challenge did not further decrease proliferation of the stable cells as measured by the BrdU proliferation assay, we believe that this is due to a constant NPS production in HEK-293H cells that leads to a certain amount of autocrine signaling. NPS expression was confirmed by quantitative RT–PCR. Reinscheid et al. (14) have also shown proliferative effects of NPS acting via NPSR1 receptor, using a NPSR1-expressing human colon cancer cell line, Colo205. They detected a dose-dependent stimulation of thymidine incorporation in the selected cell line, indicating an increased proliferation rate.

We selected a microarray-based platform to study the NPSR1-A regulated downstream target genes. The NPSR1-A overexpressing cell line is a powerful tool for this kind of studies because in agreement with the earlier results on NPSR1 signaling (9,14), activity of GPCR Gs and Gq pathways (Ca2+ and cAMP secondary signaling) was shown to be elicited in the NPSR1-A cells upon NPS challenge (7). Altogether, 104 genes were significantly up-regulated and 42 down-regulated. The CGA (glycoprotein hormones, alpha polypeptide) gene showed the highest up-regulation in our microarray experiment (25.6-fold change). This gene encodes a protein subunit that is used in the production of all the human pituitary glycoprotein hormones: chorionic gonadotrophin, luteinizing hormone, follicle-stimulating hormone and thyroid-stimulating hormone (17). Interestingly, Xu et al. (10) have previously demonstrated that the highest Npsr1 expression levels can be found in the rat tissues playing a role in the glycoprotein hormone production, such as thyroid gland, hypothalamus and testis.

Our Affymetrix results give also an indication that the NPS–NPSR1-A pathway may regulate other neuropeptides by up-regulation of the TAC1 gene (tachykinin, precursor 1; 9.3-fold change). It encodes four products of the tachykinin peptide hormone family that exert their effects through the GPCRs NK1, NK2 and NK3 (18). According to the recently published phylogenetic analysis (19), the TACRI-R3 genes encoding these receptors belong to the ß-group of rhodopsin receptors together with NPSR1 (GPR154). The TAC1 encoded tachykinins include substance P (SP) and neurokinin A (NKA). The tachykinins and their receptors are widely expressed in neuronal and non-neuronal cells in different human tissues (18). In the airways, a distinct subpopulation of primary afferent nerves is considered a principal source of SP and NKA (20). In addition, expression of SP in the airway epithelium, smooth muscle and inflammatory cells has been detected (21–24). Tachykinins have also been measured in bronchoalveolar lavage fluid (BAL), induced sputum, and plasma in both healthy and asthmatic subjects. The amount of SP is increased in BAL fluid of atopic patients in comparison to non-allergic subjects (25,26). Both SP and NKA are capable of contracting human bronchi and bronchioli, and they are potent vasodilators (24). Tachykinins have also a variety of immunomodulatory effects that putatively contribute to inflammatory processes. In animal models, the amount of tachykinins has been shown to increase in the airway neurons upon allergen challenge (27–29). As NPSR1 appears to have a similar pulmonary expression pattern as TAC1 gene products, it is plausible to expect that NPSR1 may contribute to the regulation of tachykinin functions such as modulation of inflammatory responses and bronchoconstriction.

Candidate genes for respiratory diseases are found among the up-regulated genes. Two genes that have previously been associated with asthma, tenascin C (TNC) and prostaglandin-endoperoxide synthase 2 (PTGS2) (30,31), were identified as differentially expressed in this experiment. TNC is an adhesion modulatory glycoprotein, known to be involved in processes such as extracellular matrix remodeling, both during development and after injury (32). PTGS2 encodes an enzyme that catalyzes the conversion of arachidonic acid to prostaglandin H2. This is an inducible gene that is found primarily in inflamed tissues, and its expression has been found to be enhanced in the asthmatic airways. Further, a gene that was recently identified as a chronic obstructive pulmonary disease (COPD) candidate gene, SERPINE2, was found to be up-regulated around 4-fold upon NPS–NPSR1-A signaling (33). This gene is located in a region on chromosome 2q that has shown overlapping linkage to both COPD and asthma-related traits (34).

Interestingly, INHBA was very strongly up-regulated (10.2-fold change). INHBA (activin A) is a pleiotropic regulator that belongs to the TGFB superfamily. A recent study suggests that INHBA may play role in asthma (35). Karagiannidis et al. detected increased serum levels of INHBA in asthmatic patients with untreated moderate asthma. They also showed that ovalbumin sensitization in a mouse model of allergic asthma induced Inhba expression in the lung. Furthermore, they demonstrated proliferative (stimulatory or inhibitory) effects of INHBA. Another NPSR1-A up-regulated gene is EPHA2 that belongs to the ephrin (EPH) receptor subfamily of the protein-tyrosine kinase family. EPH and EPH-related receptors have been implicated in mediating developmental events, particularly in the nervous system. EPHA2 is a mediator in TGFB1 signaling. TGFB1 plays an essential role in cellular processes such as proliferation, differentiation, embryonic development, angiogenesis, wound healing and inhibition of epithelial cell growth (36). IL8 is a member of the chemokine (CXC) family. It is secreted by several cell types and is one of the major mediators of the inflammatory response (16). It functions as a chemoattractant for several cell types including lymphocytes, neutrophils and eosinophils (37–39).

The molecular signatures of NPS–NPSR1-A signaling resemble those of microarray experiments performed to investigate the molecular and genetic basis of immunity, asthma and other respiratory disorders. First, the enriched GO terms that relate to immunity in our EASE analysis (see roots above response to stimulus, Fig. 2) are all found among the top five enriched categories for a compendium of genes that were selected for specific expression in immune cells (40). Furthermore, in a recent review aiming at finding new molecules associated with asthma through microarray analysis, a list of genes up-regulated in activated mast cells and eosinophils are presented (41). Out of 25 unique genes presented, nine (36%) are found in our list (INHBA, NR4A1, EGR2, NR4A2, MAFF, PTGS2, IL8, FOSB and NR4A3), making them interesting targets for future studies.

The results also support our previous findings in a macrophage cell (7). In order to study NPSR1 functions in immune cells, we showed that NPS induces phagocytosis of unopsonized bacteria as well as both directed (chemotaxis) and random cell migration (wound healing). In this study, immune responses such as ‘defense responses to pest, pathogens and parasites’ as well as ‘chemotaxis’ were among the most enriched GO pathways.

A comparison of our results to microarray data in the TMM microarray database suggested a co-regulation of several genes in the NPSR1-A regulated list, of which some will be discussed subsequently. These genes include, e.g. FOS/JUN family members, early growth response gene members, dual specificity phosphatases and nuclear receptor subfamily members. Many of these genes showed correlations in over 10 distinct experiments. The group of co-regulated genes contains members of the FOS gene family: FOS and FOSB, as well as a Jun gene family member JUNB. The proteins encoded by FOS family genes contain a leucine zipper domain and they can dimerize with proteins of the JUN family forming the transcription factor complex AP-1 (42–44). The AP-1 components, FOS and FOSL1 (=FRA), are also shown to play a role in activation of IL8 (45). EGR1, EGR2 and EGR3 are zinc-finger domain-containing transcription factors encoded by immediate-early genes (46). Co-regulation of nuclear receptor members NR4A1, NR4A2 and NR4A3 has also been reported in other studies. Pei et al. (47) treated macrophages with lipopolysaccharide, cytokines or oxidized lipids. These agents triggered the expression of all three nuclear receptors. Nuclear receptor signaling is important in the regulation of inflammatory stimuli in macrophages. The EGR1–R3/NR4A1–3 pathway has also been indicated in {gamma}{delta}-T-cell recognition of non-peptide antigens (48).

As we showed by immunohistochemistry, MMP10 is strongly expressed and co-localized with NPSR1-A in the pulmonary epithelium and also in macrophages and eosinophils of human sputum samples. This is the first report linking an asthma susceptibility gene and MMP10, whereas many other MMPs such as MMP9 have been implicated in asthma (15). MMP10 (stromelysin 2) is mostly epithelial and degrades, e.g. the gamma-2-chain of laminin-5, laminin-1, fibronectin, different collagen chains, elastin and gelatin (49,50), and is upregulated in tumor cells in lung cancers (51). Ritter et al. (52) showed recently that poly(I:C), a synthetic analog of viral dsRNA, triggers a strong, inflammatory response in primary small airway epithelial cells leading to viral exacerbations of asthma partly via increased release of MMP10.

On the basis of our Affymetrix data, we selected also TIMP3 for further studies. TIMP3 is located in the extracellular matrix and inhibits most matrix metalloproteinases. Imbalance of MMPs and their specific tissue inhibitors (TIMPs) has been shown to be relevant in asthma, such as MMP9/TIMP1 (53). Genetic studies have further demonstrated an association between TIMP1 polymorphisms and asthma (54). TIMP3 is an important enzyme during lung development (55) and inhibits the activity of another asthma susceptibility gene, ADAM33 (56).

Taken together, our results suggest that NPS acting via NPSR1-A induces early response genes and affects cell proliferation, morphogenesis and immune responses. Many downstream target genes revealed by these microarray analyses are of interest regarding the pathogenesis of asthma and NPS signaling in the brain.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
SUPPLEMENTARY MATERIAL
 REFERENCES
 
Neuropeptide S
In all cases, we activated NPSR1 with synthetic NPS (SFRNGVGTGMKKTSFQRAKS, purchased from MedProbe, Oslo, Norway). NPS was stored at –20°C in 7% HAc stock solution to ensure its stability. Otherwise it was stored in sterile H2O at –20°C and remained stable as far as no additional freeze-thaw cycles were done.

Cell culture
Stable NPSR1-A overexpressing cell lines have been described earlier (11). Briefly, to construct NPSR1-A stable cells, NPSR1-A was cloned into a pQM vector under CMV or SRalpha promoters (Quattromed AS, Tarto, Estonia). HEK-293H cell line was selected as the parental line, because it does not express endogenous NPSR1-A. The cells were transfected with Lipofectamine 2000 (Gibco BRL/Invitrogen, Carlsbad, CA, USA) and clones were cultured under puromycin selection. NPSR1-A positive clones were characterized by RT–PCR or quantitative RT–PCR and by western blotting. Stable cell lines and parental HEK-293H cells (ATCC, Teddington, UK) were cultured at 37°C in a humidified 5% CO2 incubator in 293 SFM II medium (Gibco BRL/Invitrogen) supplemented with 1% penicillin/streptomycin. Stable cell lines were constantly cultured under puromycin selection (0.8 µg/ml) (Sigma-Aldrich, St. Louis, MO, USA).

BrdU cell proliferation assay
Two different NPSR1-A positive cell lines and one untransfected cell line were used. Cells (2x104 cells/well) were cultured in a 96-well round-bottom plate for 3 days in the presence or absence of 1 µM NPS. Cells were labeled with BrdU for 14 h, whereafter proliferation was analyzed by colorimetric Cell Proliferation ELISA, BrdU (Roche, Basel, Switzerland) according to manufacturer's instructions. The proliferation inhibiting reagent, cyclohexamide (100 µg/ml; Sigma-Aldrich), was used as negative control.

Apoptosis assay
Two different NPSR1-A positive cell lines and parental HEK-293H cell line were cultured on glass slides for 1–2 days in puromycin-free medium with or without 1 µM NPS (1x105 cells per assay). The degree of apoptosis was visualized using DeadEnd Colorimetric TUNEL System (Promega, Madison, WI, USA) according to manufacturer's instructions.

Microarray sample preparation and hybridizations
The NPSR1-A cells (line A3) with the highest NPS-induced GTP-binding activity (11), and parental HEK-293H cells were used in the experiment. The cells were seeded at a density of 1x106 cells/ml. Both NPSR1-A and HEK-293H cells were stimulated with NPS (2 µM) and total RNA was isolated after 6 h with RNAeasy Mini Kit (Qiagen, Oslo, Norway) according to the instructions of the manufacturer. As a further negative control, NPSR1-A cells were cultured without stimulation, and RNA was collected in parallel. The HEK-293H cells with a very low endogenous NPSR1-B expression level were treated with NPS in order to study the putative activating effect of NPS on possible other receptors. This was made as a precaution even though HEK-293H cells were previously shown to have no detectable GTP-binding activity upon NPS stimulation (11). The RNAs from duplicate samples of unstimulated NPSR1-A cells, NPS-stimulated NPSR1-A cells and NPS treated HEK-293H cells were stored at –70°C in 70% EtOH and 3 mM NaAc until used. RNA concentrations were measured by a UV spectrophotometer and the quality determined using the RNA Nano LabChip kit on the Agilent 2100 Bioanalyzer (Agilent Technologies, DE, USA).

Total RNA (8 µg) from each sample was used for target cDNA synthesis according to the Affymetrix protocol. Six hybridizations and scannings (Affymetrix GeneChip Scanner 3000) were carried out using standard Affymetrix protocols for gene expression technology (www.affymetrix.com) at the Karolinska Institutet Bioinformatics and Expression Analysis Core Facility. HumanGenome U133 plus 2.0 (HGU133plus2) array targeting over 47 000 transcripts and variants were used in the experiment.

Microarray data analysis
The normalization and statistical analyses of the microarray data were performed using the statistical software R (http://www.R-project.org), by implementing the packages Affy, limma, HGU133plus2 and kth (57–59). Background signals were subtracted using the robust multiarray average method (60) and the quantile method was used to normalize the logarithm 2 (log2)-intensity distributions between arrays. Log2 expression values were calculated for each probe set on the basis of its Perfect Match values, by fitting an additive model using Tukey's medianpolish procedure.

A linear model was fitted to the expression data for each probe using the least squares method. Contrasts were specified to make pair-wise comparisons between all groups, and coefficients and estimated standard errors were computed based on the fitted linear models. The estimated coefficients and standard errors were used to compute moderated t-statistics and log-odds of differential expression (B-values), using empirical Bayes shrinkage of the standard errors towards a common value (58). The prior assumption of the extent of differential expression used was 0.01. The full set of expression data are available through the ArrayExpress database, submission number E-MEXP-829.

GO enrichment analysis
Annotation of the differentially expressed probes to GO terms (61) and subsequent enrichment analysis of terms under the GO class of Biological Process were performed using the EASEonline annotation tool (http://apps1.niaid.nih.gov/david/) (62), in the up- and down-regulated lists separately. All probes on the HGU133plus2 array were used as background in the enrichment analyses. The EASE score (E-score) was used to identify enriched categories. This is a metric calculated by removing one hit from the enriched list and then calculating the Fisher exact probability by comparing list hits and totals in the assayed list when compared with the background. The score is a conservative adjustment of P-values generated by the one-tailed Fisher's exact test that penalizes the significance of categories supported by few genes (62). An EASE score (P-value) below 0.05 was considered significant.

TMM microarray database comparison
To search for co-regulated genes among those significant differentially expressed (NPS stimulated NPSR1-A versus unstimulated NPSR1-A and NPS stimulated HEK-293H), we queried the TMM microarray database (http://benzer.ubic.ca/cgi-bin/find-links.cgi) to find out which other genes have shown similar expression patterns, i.e. high correlations, in publicly available microarray data sets (63). The TMM microarray database is currently based on information from 100 human microarray experiments, which have been filtered for unreliable features by the database curators. Standard Pearson correlation coefficients have been calculated, pair-wise, between all genes in an experiment and have been used to define both positive and negative co-expression. Co-expression has been defined by using several cut-offs, such as Bonferroni-corrected P-values for the Pearson correlation coefficient, and requirements for the magnitude of the correlation to be among the top 5% or the bottom 5% in the experiment. Ultimately, this resulted in that genes with absolute correlations below 0.6–0.7 were not considered. A threshold of 3 was used in our analysis, meaning that the correlation had been observed for each considered pair of genes in at least three independent experiments. For all genes in our list, we compared the overlap between their TMM correlated genes and the complete list of differentially expressed genes to find instances of potential co-regulation. To define groups of co-regulated genes, i.e. putative pathways, we required the gene to be correlated with at least two other genes in the group.

RNA isolation, cDNA synthesis and quantitative RT–PCR analyses
For dose–response experiments, varied concentrations of NPS were used for a fixed time and fixed concentration was used for varying times. In the first experiment, NPSR1-A and HEK-293H cells were seeded at 1x106 cells/ml and treated with NPS (1 nM–5 µM) for 6 h. Secondly, same numbers of NPSR1-A cells were stimulated with 0.1 µM NPS for 1, 2, 4, 6 and 10 h. Unstimulated cell samples were collected in parallel. Total cellular RNA was isolated with the RNAeasy Mini Kit (Qiagen), and eluted in 30 µl of ddH2O. Reverse transcription was performed with TaqMan reverse transcription reagents (Applied Biosystems, Rotkreuz, Switzerland) with random hexamers according to manufacturer's protocol.

Quantitative RT–PCR
The expression of matrix metallopeptidase 10 (MMP10), interleukin 8 (IL8), INHBA (activin A) and EPH receptor A2 (EPHA2) was confirmed with quantitative RT–PCR using the TaqMan (for MMP10 and GAPDH control) or SYBR Green (for INHBA, IL8 and EPHA2) methods. In addition, the level of NPS expression was studied in stable and parental cell lines. The PCR primers and probes were designed using Primer ExPress software version 1.2 (Applied Biosystems) and are shown in Table 2. The primers and the probe for the control gene GAPDH were purchased from Applied Biosystems. The quantitative PCR amplifications were performed in a total volume of 25 µl, containing 7 µl of 1:10 diluted cDNA template, 12.5 µl TaqMan Universal PCR Master Mix (Applied Biosystems) or 12.5 µl SYBR Green PCR Master Mix (Applied Biosystems), 200 nM of each primer and 100 nM of MMP10 and GAPDH probes. The quantitative PCR was performed with 7500 Fast Real-Time PCR System (Applied Biosystems). The following reaction conditions were used: 50°C for 2 min and 94°C for 10 min; following 45 cycles of 92°C for 14 s and 1 min at 60°C. The dissociation stage was added to SYBR Green reactions to confirm the specificity of the primers. All assays were carried out in triplicate. Relative quantification and calculation of the range of confidence was performed with the comparative {Delta}{Delta}CT method as described elsewhere (64).


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  		Table 2. Quantitative RT–PCR primers and probes

 
Human MMP10 immunoassay
The NPSR1-A cells (5x105 cells/ml) were stimulated with increasing concentrations of NPS (0.1 nM–10 µM) and supernatants were collected at 24 and 48 h and stored at –20°C until used. Total MMP10 concentrations were analyzed in duplicate samples with an ELISA-based Human (total) MMP10 Immunoassay (R&D Systems, UK) according to manufacturer's instructions.

Immunohistochemistry
Expression and localization of MMP10 and TIMP3 were studied in formalin-fixed, paraffin-embedded bronchus tissue sections from asthmatics and normal healthy controls. Mouse monoclonal anti-MMP10 (1:200) and anti-TIMP3 (1:300) antibodies were purchased from Novocastra Laboratories Ltd (Newcastle, UK) and Calbiochem (San Diego, CA, USA), respectively. MMP10 antibodies recognize amino acid residues 342–476 corresponding to the hemopexin domain of MMP10 and therefore detect both a pro-peptide containing latent protein and an active form of MMP10. The slides were blocked with 3% H2O2 for 10 min at room temperature, and thereafter heated at 95°C in water bath in DakoCytomation Target Retrieval Solution for 20 min (in the case of MMP10) or pre-treated at 37°C with 1% trypsin (TIMP3). Immunohistochemical analyses were performed using the DakoCytomation StreptABCoplex/HRP Duet method (DakoCytomation, Glostrup, Denmark). Diaminobenzidine (DAB) and AEC High Sensitivity Substrate Chromogen were used as chromogens for TIMP3 and MMP10, respectively, and Mayer's Hemalun solution as counterstain. Negative controls were performed with mouse immunoglobulins. NPSR1-A staining was performed as described earlier (3,11).

Expression of MMP10 was also studied in sputum samples of asthmatic patients and healthy controls. Sputum was induced with 3% hypertonic saline and processing was done as previously described (65). Cytospin preprations were used for immunocytochemistry. The slides were fixed with 3% PFA for 5 min, washed two times with PBS for 5 min and blocked with 3% H2O2 for 10 min and thereafter washed with PBS for 5 min. The slides were incubated with mouse monoclonal anti-MMP10 (1:250) antibodies for 2 h at room temperature and thereafter with the secondary anti-mouse antibodies for 35 min. DAB was used in as chromogen and Mayer's Hemalun solution as counterstain. Negative controls were performed with mouse immunoglobulins.

Statistical analysis
All data are expressed as mean±SD, unless stated otherwise.


   SUPPLEMENTARY MATERIAL
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
REFERENCES
 
Supplementary Material is available at HMG Online.


   ACKNOWLEDGEMENTS
 
We would like to thank Alli Tallquist, Riitta Känkänen, Morag Dixon and the personnel at the Karolinska Institutet Bioinformatics and Expression Analysis Core Facility for their skillful laboratory work. This work was funded by Sigrid Juselius Foundation, Päivikki and Sakari Sohlberg Foundation, Academy of Finland, Swedish Research Council, The Helsinki University Central Hospital Research Fund (EVO), Emil Aaltonen Foundation, The Pulmonary Association Heli, The Finnish Anti-Tuberculosis Association Foundation, The Hilda Kauhanen Memorial Foundation, Ida Montin's Foundation, Väinö and Laina Kivi Foundation and Paulo Foundation.

Conflict of Interest statement. None declared.


   REFERENCES
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 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 REFERENCES
 

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