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Nasal epithelial cells : effector cells in allergy

Vroling, A.B.

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Citation for published version (APA):Vroling, A. B. (2009). Nasal epithelial cells : effector cells in allergy.

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Nasal epithelial cells;effector cells in allergy

Aram Vroling

NASAL EPITHELIAL CELLS; EFFECTOR CELLS IN ALLERGY

The work described in this thesis was performed at the Department of Otorhinolaryngology at the Academic Medical Center, in collaboration with the Integrative Bioinformatics Unit at the Swammerdam Institute for Life Sciences and the Department of Respiratoy Medicine of the Academic Medical Center, all of the University of Amsterdam, The Netherlands.

The publication of this thesis was financially supported by:

HAL Allergy B.V.

Cover design and layout: A.B. VrolingPrinted by Wohrmann Print Service

copyright 2009 by Aram Vroling, Alkmaar, The NetherlandsAll rights reserved. No part of this publication may be reproduced, stored or transmitted in any way without prior permission from the author.

NASAL EPITHELIAL CELLS; EFFECTOR CELLS IN ALLERGY

ACADEMISCH PROEFSCHRIFT

ter verkrijging van de graad van doctoraan de Universiteit van Amsterdamop gezag van de Rector Magnificus

prof. dr. D.C. van den Boomten overstaan van een door het college voor promoties

ingestelde commissie, in het openbaar te verdedigen in de Agnietenkapel

op woensdag 18 februari 2009, te 10:00 uur

door

Aram Ben Vroling

geboren te Alkmaar

Promotiecommissie

Promotor: Prof. dr. W.J. Fokkens

Co-promotor: Dr. C.M. van Drunen

Overige leden: Prof. dr. P.W. Hellings

Prof. dr. P.J. Sterk

Prof. dr. M.L. Kapsenberg Prof. dr. R.A.W. van Lier

Prof. dr. F. Baas

Prof. dr. H.F. Kauffman

Faculteit der Geneeskunde

Table of contents

Outline of the thesis 7

Chapter 1 How epithelial cells detect danger; aiding the immune response. Allergy. 2008 Sep;63(9):1110-23.

11

Chapter 2 Allergen induced gene expression of airway epithelial cells shows a possible role for TNF-α.Allergy. 2007 Nov;62(11):1310-9.

41

Chapter 3 Primary nasal epithelium exposed to house dust mite extract shows activated expression in allergics.Am J Respir Cell Mol Biol. 2008 Mar;38(3):293-9

65

Chapter 4 Comparison of expression profiles induced by dust mite in airway epithelia reveals a common pathway.Allergy. 2008 Apr;63(4):461-7.

99

Chapter 5 Epithelial cells show a pleiotrope mediator response as a consequence of cell-cell contact disruption.Submitted.

121

Chapter 6 A strongly reduced synergistic response to TNF-α and IL-17 detected for the Th1 cytokine INF-γ in primary nasal epithelial cells from allergic individuals. Submitted

139

Chapter 7 General discussion 153

Appendices SummaryNederlandse samenvattingBibliographyDankwoordCurriculum vitae

167171175177179

7

Outline of the thesis

8

Outline of the thesis

Allergic diseases affect a large part of the western population, with a prevalence of more than 20% in the U.S.A.1. Though it seems a relatively harmless disease, its impact on society is enormous2. Patients have symptoms like runny nose, blocked nose, itching of the nose and/or eyes, sneezing, impaired smelling, and impaired hearing. These symptoms significantly affect their quality of life and performance on the job3. It has been estimated that absenteeism and low productivity due to allergies has cost U.S. companies more than $250 million in 19984. Although these indirect costs are high, they are just a fraction compared to the estimated direct healthcare costs of allergic rhinitis, which in 1996 were more than $6 billion5.

Effective treatment will improve quality of life and will also reduce the indirect and direct costs associated with the disease. Although, the two predominant treatments for nasal allergies, corticosteroids and antihistamines, are effective in a large group of patients, not all patients are satisfied with their current treatment2. Corticosteroids, due to their broad action, may cause unwanted side-effects, whereas antihistamines only work at the end of the immunological cascade, which may be responsible for their limited efficacy in asthma6. By developing new medication that specifically targets the beginning of the allergic response instead of effector cells, the efficacy and specificity of treatment might increase.

An interesting target for the development of new drugs is the airway epithelium, being the first cells an allergen encounters7, and the easiest to target with topical drugs. With this as a starting point we started our research to investigate how epithelial cells respond to allergen exposure. In particular we wanted to know if this response can teach us anything about the possible role of the epithelium in allergic inflammation.

In chapter 1 we describe the different receptors epithelial cells have on their surface and which they can use to detect changes in their environment. The three main groups are Toll-Like Receptors, NOD-Like Receptors and Protease Activated Receptors.

9

Outline of the thesis

In chapter 2 we investigate the response of airway epithelial cells to house dust mite allergen. By starting off with this cell line we hope to find genes that are regulated by exposure to house dust mite (HDM), and identify the processes these genes are involved in. This is the first step on the track that will lead to better understanding the role of the epithelium in the mucosal response to allergens.

The next step we take in chapter 3 where we look into healthy and allergic epithelium to see if we can find differences between them, and if these differences are default or that they only appear after exposure to house dust mite extract. In a four group study we compared the changes induced by exposure to HDM, but we were also able to look at initial differences in expression of these genes.

After these two steps we wanted to focus on the similarities that can be found between the airway epithelial cell line described in chapter 2 and the primary nasal epithelial cells described in chapter 3. In chapter 4 we compare expression levels and response to allergen exposure in H292 cells and primary nasal epithelial cells from healthy and allergic individuals. This will allow us to further define the response of epithelial cells to HDM exposure.

All the experiments so far have been using HDM extract, a crude mix of many different proteins, some known to be able to activate a receptor on epithelial cells. In chapter 5 we expand our repertoire of stimulants by looking into the response of the airway epithelial cells to proteolytic stimulation, leading to damage of the epithelial layer.

The response of epithelial cells consists often of production of cytokines and chemokines, which are known to have an effect on cells of the adaptive immune system, which in turn will also produce such mediators. In chapter 6 we investigate the response of nasal epithelial cells to the proinflammatory cytokines TNF-α and IL-17, hoping to unravel some of the more complex cross talk mechanisms that can be found in allergic inflammation.

10

Outline of the thesis

Finally in chapter 7 we address the implications of the data in this thesis, and explore the opportunities for further research.

In this thesis we hope to answer many of the above questions and by answering these questions we want to get insight into the role of nasal epithelial cells in allergy. This insight might lead to new strategies for treatment of allergic inflammation in the nose, thereby decreasing symptoms and improving the quality of life in patients suffering from allergic rhinitis.

Reference List

American Academy of Allergy, A. a. I. 1998. Task Force on Allergic Disorders. 1. Executive Summary Report.van Drunen, C., E. O. Meltzer, C. Bachert, J. Bousquet, and W. J. Fokkens. 2005. 2. Nasal allergies and beyond: a clinical review of the pharmacology, efficacy, and safety of mometasone furoate. Allergy 60 Suppl 80:5-19.Dykewicz, M. 2003. 7. Rhinitis and sinusitis. 3. J Allergy Clin Immunol 111:520-529.Hewitt Associates LLC. 1998. 4. The Effects of Allergies in the Workplace.Ray, N. F., J. N. Baraniuk, M. Thamer, C. S. Rinehart, P. J. Gergen, M. Kaliner, S. Josephs, 5. and Y. H. Pung. 1999. Healthcare expenditures for sinusitis in 1996: contributions of asthma, rhinitis, and other airway disorders. J.Allergy Clin.Immunol. 103:408-414.Van Cauwenberge, P., C. Bachert, G. Passalacqua, J. Bousquet, G. W. Canonica, S. R. 6. Durham, W. J. Fokkens, P. H. Howarth, V. Lund, H. J. Malling, N. Mygind, D. Passali, G. K. Scadding, and D. Y. Wang. 2000. Consensus statement on the treatment of allergic rhinitis. European Academy of Allergology and Clinical Immunology. Allergy 55:116-134.Takizawa, H. 2005. Bronchial epithelial cells in allergic reactions. 7. Curr.Drug Targets.Inflamm.Allergy 4:305-311.

11

1How epithelial cells detect

danger; aiding the immune response.

Aram B. Vroling, Wytske J. Fokkens, Cornelis M. van Drunen

Department of Otorhinolaryngology, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands

Allergy. 2008 Sep;63(9):1110-23.

12

Chapter 1

Abstract

The epithelial layer occupies a strategic, important location between an organism’s interior and exterior environment. Although as such it forms a physical barrier between both environments, it has become clear that the role of the epithelium extends far beyond this rather passive role. Through specialized receptors and other more general mechanisms the epithelial layer is not only able to sense changes in its environment, but also to actively respond to these changes. These responses allow the epithelium to contribute to wound healing and tissue repair, to the defense against micro-organisms, and to the control and regulation of the local immune response. In this review we will focus on signals acting on epithelium from the exterior environment, how these signals are processed, and identify research challenges.

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Chapter 1

Sensing the environment

Considering the role of the epithelium in sensing changes in its environment we need to take into account that many different kinds of epithelial surfaces exist. The environment of the epithelial cells lining the gastrointestinal tract will differ significantly from the environments encountered by airway epithelium or the epithelial layer of the urinary tract. This may well have consequences for the types and expression levels of epithelial receptors or their response. Where bladder epithelial cells respond efficiently to, for instance, LPS 1, intestinal epithelial cells are relative tolerant 2. Even within the airways there is a large variation: the epithelial cells lining the lower respiratory tract are from a different embryonic origin than the cells lining the upper respiratory tract, and within the nasal cavity one can find specialized olfactory epithelium alongside pseudo-stratified ciliated epithelium and “stratified” epithelium. Similarly there are differences in the epithelium from the ileum, colon, or other parts of the gastrointestinal tract. Moreover, numerous experiments on the role of epithelium in sensing its environment have been performed with (carcinoma) cell lines derived from local tissues. We can only emphasize that, although data obtained in these experiments is highly useful, it may not necessary reflect the true in vivo situation. Several mechanisms are known through which the epithelium can sense its environment. These comprise of both cell surface and intracellular receptors for which activation leads to a signal cascade and an altered gene expression profile.

Cell surface and intracellular receptorsThe receptors of the innate immunity have only a limited specificity and

can be activated by interaction with common protein motives or Pathogen Associated Molecular Patterns (PAMPs) found in micro-organisms. Inadvertent activation by host or microbial factors of these Pattern Recognition Receptors (PRRs) or the inability to be activated underlies the pathogenesis

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Chapter 1

of some common diseases underlining the relevance of these receptors in maintaining cellular homeostasis.

Toll-Like Receptors

The Toll-Like Receptors (TLRs) are probably the best studied group of Pattern Recognition Receptors. These receptors are the mammalian homologue of the Drosophila Toll receptor, that in this organism was held responsible for activation of host-defense mechanisms in response to infection 3. Although in Drosophila the Toll receptor family is more likely to play a role during embryonic development 4;5, transcriptional activation of some anti-microbial peptides is also dependent on Toll activation 6.

In most vertebrates, the Toll family comprises about 10 family members 7 with a highly conserved intracellular signaling domain that resembles the signaling domain found in the mammalian IL-1 receptor 8. After activation of the receptor, the TIR (Toll/IL-1 Receptor) domain interacts with different adaptor molecules that through activation of NF-κB and IFN-Regulatory Factors (IRFs) lead to transcription activation of a broad panel of genes. The homology between Toll-like family members also extends to the extracellular part of the receptor 9. Multiple Leucine-Rich Repeats (between 19 and 25) and a single membrane proximal cysteine motive are involved in specific binding to a wide variety of microbial and endogenous ligands 10 (Table 1). Unclear is how such conserved domains in Toll-like family members are able to recognize different ligands specifically, also given that hydrophobic interactions seem to be a prominent factor 11. Two aspects may help in the receptor specificity. Firstly, there is a distinct intracellular distribution of the receptors (Figure 1). TLR-1, -2, -4, and -6 are found at the cell surface where they mostly interact with bacterial cell wall components, whereas TLR-3, -8, and -9 that interact with viral or bacterial nucleic acids are found in intracellular compartments. Secondly, some of the specificity may come from the additional involvement

15

Chapter 1

of membrane bound or soluble factors interacting with the ligands, like MD2 12;13 and CD14 14;15 for LPS-mediated activation of TLR-4.

Receptor Ligand Source

TLR-1 Triacyl lipoproteins Bacteria and MycobacteriaTLR-2 Lipotechoic acid

Zymosan Lipopeptides Lipoarabinomannan Lipopolysaccharide Peptidoglycan

Gram-positive bacteria Yeast Mycobacteria Mycobacteria Gram-negative bacteria Gram-positive bacteria

TLR-3 viral dsRNA VirusTLR-4 Fibinogen

HSP-60 HSP-70 LPS Taxol Mannan

Endogenous Endogenous Endogenous Gram-negative bacteria Plants Yeast

TLR-5 Flagellin BacteriaTLR-6 Diacyl lipopeptides

Lipoteichoic acid Zymosan

Mycoplasma Gram-positive bacteria Yeast

TLR-7/-8 ssRNA Imidazoquinoline

Virus Synthetic

TLR-9 CpG-containing DNA Bacteria and virusTable 1: Ligands for Toll-like receptors. Toll like receptors are pat-tern recognition receptors that recognize specific ligands, both pa-thogenic and endogenous.

At the moment there are still some doubts about some of the ligand-specificities of the TLRs 16. The general issue is the purity of the ligands. For TLR-4 a number of host factors (HSP60, HSP70, Surfactant Protein-A) have been described that could activate the signaling cascade 17-19. However, it is technically very difficult to prove that this effect is not due to some minute contamination with LPS, as this is such a powerful ligand for TLR-4. Similarly, it would seem that the reported action of LPS on the TLR-2 receptor in TLR-4 knockout experiments was an effect of a minor contamination of a TLR-2 ligand in the LPS sample 20;21. Moreover, the activation of TLR-2 by

16

Chapter 1

peptidoglycans has also been called into question as some experiments have shown that lipidproteins, that are present as contaminants in peptidoglycan extracts, are responsible for the observed activation22.

TLR3

TRAF6IRAK1IRAK4

TRAF3 IRF7

IRF7p

IRF7p

IRF7p

IRF5

IRF5p

IRF5p

IRF5p

TRAF6IRAK1IRAK4

TAK1

MyD88MyD88TIRAP

MyD88TIRAP

IKKα IKKβ

IκBp50

p65

p50p65

p38JNK

MKKs

AP-1

NEMO

TRAM

TRIF

TRIF

TRIFRIP1RIP1

RIP1

NAP1

TRAF3

TKB1 IKKε

IRF7p

IRF7p

IRF7p

IRF3p

IRF3p

IRF3p

TRL3

TLR4TLR5TLR6TLR2TLR1

MyD88

TLR9TLR7/8

TLR3

Nuc

leus

Cyto

plas

mEn

doso

me

Envi

ronm

ent

LPSFlagellinBacterial cell

wall components

dsRNA dsRNA

CpG-DNA

ssRNA

ISRE5 ISRE7AP-1NF-kB ISRE7ISRE3

Type I Interferon Cytokines and Chemokines Interferon-β

Figure 1: TLR signaling. Schematic drawing of Toll Like Receptor signaling pathways leading to transcription factor activation. In red the different ligands for the different Toll Like Receptors are given. Modified from drawing on http://www.invivogen.com.

TLR–mediated signalingAfter binding of their ligands TLRs are capable of differentially activating

distinct downstream signalling events via several adaptor proteins (Figure 1). The first path leads to activation of transcription factor NF-κB in a MyD88-dependent way. The cytoplasmic TIR domain of the receptor forms a platform for the recruitment and activation of the adaptor molecule MyD88 and the

17

Chapter 1

kinases of the IL-1R-Associated Kinase (IRAK) family 23-25. Following IRAK-4 autophosphorylation and activation of the IRAK-1/2-complex 26, the TRAF-6 adaptor protein interacts with a second kinase complex, TAK-1 that ultimately leads to the inactivation of the NF-κB inhibitor IκB 27-30. After activation of NF-κB, the transcription factor is no longer retained in the cytoplasm and is able to translocate to the nucleus where it activates transcription of a variety of cytokines and chemokines (TNF-α, IP-10, IFN-γ and IL-1, -6, -8, -10, and -12) and other genes like COX-2 and SOCS. For TLR-2 and -4 signalling via this pathway is dependent on TIRAP 31, whereas this signalling pathway for TLR-3, -7, and -9 is independent of TIRAP and induces Type I Interferons via IRF-5 and -7 32.

The second, MyD88-independent, pathway also targets NF-κB through the receptor bound adaptor molecule TRIF 33;34, although the precise mechanism has not been resolved. This TRIF-dependent pathway also activates IFN-regulatory factor 3 (IRF-3) leading to the induction of IFN-β. For TLR-4 this pathway acts through TRIF and TRAM 35;36, whereas for TLR-3 only TRIF is required.

Natural occurring mutations in key signalling molecules will have consequences for the ability to respond to micro-organisms, although in all cases the deficiency has not been linked specifically to epithelial TLR expression. A number of families with IRAK-4 deficiency have been described that suffer from recurrent bacterial infections 37;38. Given that all TLRs can signal through IRAK-4 this would be hardly surprising and, as both for TRL-3 and TLR-4 TRIF mediated signalling to IRF-3 remains intact, the response to viral infections remains unaffected. Similar observations have been reported for mutations in TLR-4 or TLR-2. In the case of TLR-2, a specific polymorphism (R753N) has been linked to increased susceptibility to Tuberculosis 39. Interestingly, a different mutation associated with TLR-2, upstream of the coding region, affects the development of asthma 40. Only children from farmers with this mutation show a reduced prevalence

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Chapter 1

of asthma, linking TLR-2 to the protective effects seen in rural populations versus urban populations 41.

Airway epithelial expression of TLRsThere is very limited data on the expression pattern of the TLRs in the

upper airway and even less functional data. In situ hybridization 42 and immunohistochemistry 43 showed expression of TLR-2 and TLR-4 in nasal epithelium, but the expression of the other TLRs have not been investigated. Variable expression for TLR-2 and TLR-4 on the RNA level was evident in tissue samples from nasal polyposis, chronic rhinosinusitis, cystic fibrosis, and healthy controls 43. However, as this RT-PCR was done on whole tissues it can not be firmly established that the variable expression is a consequence of differential expression in the nasal epithelium.

Significantly more data is available for lung epithelium 44-48 both on the expression level, as well as their functional activity. Primary small airway epithelial cells on the mRNA level express TLR-1 through TLR-6, whereas TLR-6 through TLR-10 could not be detected 48. This distribution is partly reflected in the bronchial epithelium cell line BEAS-2 that showed relative high mRNA levels for TLR-1 through -6, but did also express TLR-7 through -10, albeit on a lower level than for TLR-1 through -6 47. In both lung cell types specific stimulation of TLR-3 with double standed RNA (dsRNA) leads to transcription induction of a broad range of genes 46;47. These include chemokines and cytokines (IL-6, IL-8, GM-CSF, RANTES, TNF-α, and I-TAC), components of the TLR-signalling cascade (MyD88, TRIF, IRAK-2), and components that affect the extra cellular matrix (MMP-1, -8, -9, -10, -13). The changes induced by dsRNA in the TLR-signalling cascade are particular interesting. Although some of the mRNAs (MyD88, TRIF, IRAK-2) are up-regulated, while other remain unaffected (TRAM, TIRAP, IRAK-1, IRAK-3), this is only partly confirmed on the protein level. The clear exceptions being TRIF and IRAK-1 that are down-regulated at the protein level with mRNA

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levels going up for TRIP or remaining constant for IRAK-1 46.Adding to the complexity of the TLR-signaling cascade is that the expression

of the TLRs themselves is affected by external stimuli. Experimental models of viral or bacterial infection show functional changes to the TLR expression repertoire. After infection of epithelial cells with Respiratory Syncytial Virus (RSV) the expression of TLR-4 is strongly up-regulated, increasing the responsiveness of epithelial cells to LPS 49. This is not only a consequence of the up-regulation of the receptor, but also of the adaptor molecule MD-2 49, that like CD14 15 is required for an optimal response in epithelial cells 50. This suggests that a mechanism is in place whereby viral infections induce an activated alertness in epithelium for bacterial infections. Viral infections may also influence TLR-3 51;52, by which they may also directly affect anti-viral responses. Similar observations have been made with isolated proteins that have been identified as molecular patterns of micro-organisms 46. Firstly, stimulation of TLR-3 with dsRNA significantly up-regulates TLR-1 through -3, an effect that can also be observed when TLR-5 on primary epithelial cells is stimulated with Flagellin. This is no general effect on TLR gene regulation as dsRNA down-regulates TLR-5 and -6, while Flagellin does not affect these genes. Interestingly, activation of the heterodimer of TLR-2 and -6 through Zymosan strongly down-regulates most TLRs with only TLR-2 and -5 remaining unaffected. TLR expression is not only affected by receptor agonist, but also by inflammatory or allergic mediators 46. Where the inflammatory mediators IL-1β, TNF-α, and IFN-γ have a limited effect when given on their own, a strong induction of TLR-2 and -4 is seen when epithelial cells are exposed to either IL-1β or TNF-α in combination with IFN-γ. A similar picture emerges for IL-4 and IL-13, which by themselves have a limited effect on TLR-1 and -2 expression levels, but in combination with TNF-α strongly up-regulate the expression of these receptors. Overall it would seem that TNF-α potentiates both a Th1-driven (IFN-γ) as well as a Th2-driven (IL-4, IL-13) response.

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Protease activated receptors

Blood clotting and platelet activation involves a complex interplay between multiple proteases that through their action either activate or inactivate other proteases and/or cells. In this process Protease Activated Receptors (PARs) have been described and investigated first. Subsequently research interests have focused on PARs involved in injury and wound healing, but only recently the role of PARs in inflammation is gaining more and more attention. Multiple (cellular) sources, in addition to the coagulation enzymes, have been described to produce proteases that act through PARs. These include: trypsins (II, IV) produced by the pancreas, endothelial cells, or epithelium; mast cell proteases (tryptase, chymase); leukocyte proteases (cathepsin G, proteinase-3); or proteases from bacteria (gingipains-R), mites (DerP1, P3, P9), and fungi (pen C 13 ) 53.

To date, four distinct PARs have been cloned that all belong to the serpentine or 7-transmembrane type of receptors 54-59. Serpentine receptors are the most common found cell surface receptors and they share a common signal pathway that involves receptor coupled heterotrimeric G proteins. These G proteins in turn directly regulate the activity of different intracellular enzymes (guanylylcyclase, adenylylcyclase, phospholipase C) or indirectly affect the activity of calcium dependent enzymes by increasing intracellular Ca2+ levels via calcium channels in the plasmamembrane and/or in the endoplasmatic recticulum. Three of the receptors (PAR-1, -2, and -3) are located in a small region (5q13) on chromosome 5 and share a two exon genomic organization with PAR-4 located on chromosome 19(p12) 60-

62. A unique mechanism that uses the intrinsic enzyme activity of proteases to detect their presence underlies the activation of the PARs (Figure 2, modified from Reed et al 63). Each of the receptors contains a ligand binding domain, encoded within the small first exon, which can be activated through interaction of a peptide sequence that is contained within the N-terminal part

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of the receptor itself. In the inactive state, the N-terminal extension of the receptor protein prevents the interaction of the internal peptide sequence with the ligand binding domain of the receptor. This N-terminal extension can be clipped off through the action of an extra cellular protease, allowing the tethered ligand to activate the receptor 55.

Unactivated PAR

N

C C

N

Activated PAR

Ligand binding region

Gβ GγGα

Gα12/13 Gβ Gγ Gβ GγGq11α Gβ GγGαi

RhoGEFs

Rho IP3

Rho-activatedkinases

Ca2+

Phospholipase Cβ

DAG

PKC PI3K Adenylyl cyclase

G-protein-coupled receptor kinases

tyrosine kinases

Cell shape Secretion Integrinactivation

Metabolic responses

Transcriptional response

Cell mobility

cleavage

Cyt

opla

smE

nviro

nmen

tProteolyticAllergen

Figure 2: PAR signaling. Schematic drawing of prote-ase activated receptor acti-vation, with different adapter molecules leading to dif-ferent signaling pathways. Modified from Reed et al, J Allergy Clin Immunol 2004.

Several factors contribute to the specificity of the individual receptors. The first level is the primary sequence around the cleavage site that allows only some proteases to clip off the N-terminal extension (table 2). A broad collection of both endogenous (e.g. tryptase) and exogenous (e.g. allergens) proteases has now been described to act through PARs. The second level involves initial binding of the protease to either a distinct extracellular domain in the receptor itself or the initial interaction of the protease with another membrane bound protein. Initial binding α-thrombin to the hirundin-like domain in PAR-1 explains the higher potentiating activity of α-thrombin in comparison to γ-thrombin that is not able to interact with this domain 64;65. This hirundin-like domain is not only present in PAR-1, but can also be found in PAR-3 57. Interaction of the clotting factor FVIIa with the membrane bound

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Tissue Factor (TF) is essential for FVIIa’s ability to activate PAR-2, although proteases like trypsine or tryptase can activate PAR-2 without needing to interact with TF first 66. Interestingly, there is even collaboration between the PAR receptors with PAR-3 acting as co-stimulatory factor for PAR-4 67;68. Three other factors that regulate the signaling capabilities of the PARs are (1) glycosylation around the proteolytic site, which may inhibit cleavage 69, (2) cleavage of the N-terminal region of the receptor at a more proximal site that also removes the tethered ligand 70;71 and (3) secreted protease inhibitors. In vitro experiments have shown that peptides derived from the tethered ligand sequence can be used to either stimulate or to antagonize the receptors. Currently it does not seem likely that this mechanism occurs in vivo, although some data show that peptide analogs may trigger signal cascades in a PAR receptor independent way 72-74.

PAR-1 PAR-2 PAR-3 PAR-4Activating proteases(endogenous)

Thrombin Coagulation Factor Xa Chymotrypsin

Trypsin Coagulation factor Xa Mast cell tryptase Proteinase-3 Elastase Cathepsin-G

Thrombin Thrombin Trypsin

Activatingproteases(exogenous)

Der P1 Pen C13 Cockroach allergen Fungal allergens; Aspergillus & Alternaria

Agonist peptides

TFLLR-NH2 SLIGRL-NH2 TFRGAP-NH2 GYPGQV-NH2

Table 2: PAR receptors and activators. There are four Protease Activated Receptors (PARs) which are activated by endogenous proteases and play a role in blood clotting; in addition PAR-2 can also be activated by endogenous proteases. Upon activation the N-terminal tail is cleaved and the exposed sequence activates the receptor, these sequences can also be used as agonistic peptides.

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PAR–mediated signalingActivation of the serpentine receptors initiates the exchange of GDP for

GTP bound to heterotrimeric G proteins. This leads to signaling through two pathways via the Gα and the Gβγ subunits, respectively. The Gα subunit can interact with at least three different enzymes: adenylylcyclase, guanylylcyclase, and phospholipase C. The first two cyclases regulate the intracellular concentration of the second messengers cAMP and cGMP, the activated lipase cleaves the lipid plasmamembrane component phosphoinositide to form Inositol 1,4,5-trisphosphate (IP3) and Diacylglycerol (DAG). These four second messengers activate further downstream processes that involve distinct kinases. Activation is either direct through cAMP, cGMP, and DAG or indirect through Ca2+ that is released by IP3 from the endoplasmatic reticulum and mitochondrial stores 75. Different cell types may differentially respond to identical extracellular signals as distinct varieties of Gα subunits exist, that either couple to different downstream enzymes or that inhibit rather than stimulate a given pathway. Signaling events down-stream of the PAR receptors in epithelial cells have not been fully elucidated. This is partly due to our incomplete understanding of which Gα couples to which effector enzymes in epithelial cells, as this coupling is not necessarily identical in all cell types.

The best studied receptor signaling cascade is that of PAR-1, a receptor that in endothelial cells, fibroblasts, and platelets can interact with three different Gα’s (figure 2). The first is Gq11α that links the receptor to phospholipase C-β1 leading to the production of the second messengers IP3 and DAG from their membrane bound precursor phospatidylinositol 76. Specific receptors for IP3 on the endoplasmatic reticulum and on mitochondrial stores lead to a transient Ca2+ spike, which through calcium-binding proteins like calmodulin activate calcium-dependent kinases. Parallel to the calcium-dependent pathway, DAG activates PKC-α directly. In addition to their direct effects, both kinases also cross-talk to the MEK/ERK pathway. Although it is not clear what

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the relative contribution of the individual mediators in this Gq11α activated pathway is, the whole cascade is important in the control of blood clotting, as knock-out mice for Gq11α display increased bleeding times 77. The second group of G proteins that couples to PAR-1 is G12α and G13α. These G proteins couple to a cascade of Guanine-nucleotide Exchange Factors (GEFs) of the Rho and Rac-family. In endothelial cells the Rho pathway through Rho-kinase is involved in maintaining the structural integrity of the blood vessels, and the Rac pathway through myosin light-chain kinase is involved in cytoskeleton rearrangements. In addition to the activating pathways, PAR-1 can also signal via Gαi, an inhibitory G protein. This pathway leads to down-regulation of adenylyl cyclase and a subsequent drop in intracellular cAMP levels. This process could be especially relevant in inflammatory cells, as decreased intracellular cAMP levels are associated with transcriptional activation of chemokines and cytokines.

In addition to G-coupled protein signaling it has been described that PAR-2 can use a G-protein independent pathway, involving β-arrestins. It is believed that signaling via β-arrestins is involved in scaffolding of proteins that are involved in cell migration and actin assembly. This occurs via a β-arrestin-dependent dephosphorylation and activation of the actin filament-severing protein (cofilin). This PAR-2-evoked cofilin dephosphorylation requires both the activity of a cofilin-specific phosphatase (chronophin) and inhibition of

LIM kinase (LIMK) activity 78.

Epithelial expression of PARsThe expression and function of PARs in the airway epithelium and in the

different cells therein, has been studied to some extent, but the exact function in the inflammatory response remains unclear. In comparison to the issue of potential endogenous activators in the Toll-Like Receptors, this is a non-issue for the PARs. Inflammatory proteases, like tryptase and chymase (released upon activation of mast cells), are clear candidates to affect the inflammatory

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or allergic response. Moreover, as mutations in the protease inhibitors ADAM33 79 or SPINK5 80 have a clear clinical phenotype an involvement of PARs is suggested. PAR expression has been characterized in airway epithelial cells. Interestingly airway epithelial cells express all PAR-1 through -4, but only for PAR-1, -2, and -4 did activation lead to cytokine production, where PAR-3 agonist peptide did not evoke a response 81. Although PAR-3 can act as a co-receptor for PAR-4 it is not clear whether the absence of PAR-3 activation has any consequences for the activity of PAR-4. This is partly the result of the overlapping protease specificities of the receptors, as activators of PAR-4 (thrombin and trypsin) also activate PAR-1 (thrombin) and PAR-2 (trypsin).

Not only are the PARs expressed, they are also functional and may play a role in inflammation as their activation leads to cytokine production and release of PGE-2 81. The best studied is PAR-2, as this receptor has been associated with activation by allergens. Activation of PAR-2 by proteases is associated with house dust mite allergen 82, cockroach allergen 83, trypsin 84, or PAR-2 activating peptides 74 and has been reported for many different epithelial cell lines or primary cells 81;85. The extend of these responses nor their similarity is not fully clear, as in many studies only a limited set of outcome parameters (mostly IL-6 and IL-8) are reported. The link between epithelial PAR expression and clinical relevance has been addressed in a few studies. Inhibition of Factor Xa (a known activator of PAR-2) by fondaparinux, resulted in the reduction of airway hyperresponsiveness and a decreased epithelial mucin production in vivo. The later aspect was reflected in vitro, where Factor Xa enhanced AREG expression and mucin production in H292 cells 86. In this mouse ovalbumine (OVA) model no effect was seen on the influx of inflammatory cells. These outcomes are different from a similar model in rabbit sensitized for the major Parietaria allergen (Par-j1), where pre-treatment with PAR activating peptide reduces inflammation and relieves bronchoconstriction 87. Currently it is not clear where these differences

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originate from, but it is interesting to note that activating PAR peptides have been reported to exert an activity independent of the PAR receptor 72;73. A second study showing an epithelial mediated protective effect of PAR activation 88 also involved the use of a PAR activating peptide.

The expression levels of PARs are also affected by disease. Already in 2001 Knight et al described that in the lung epithelium of asthmatics the expression of PAR-2 is higher than in healthy individuals 89. Whether this reflects a protective mechanism or is part of the patho-physiology is unclear. More recently an interest has been taken in nasal PAR expression. It has been reported that nasal epithelial cells express PAR-2 and that in allergic individuals this expression is higher. Furthermore the increased PAR expression coincides with an increased number of eosinophils 90. In 2007 Lee et al confirmed these findings in a similar study in Korea 91. And more recently a study has been published by Rudack et al where they suggest that PAR-2 plays an active role in inflammation mechanisms of chronic rhinosinusitis 92. They showed an NF-κB dependent expression and the release of CXC chemokines (GRO-α, IL8), but no regulation of CC chemokines (eotaxin, RANTES, and TARC). These data show an expression on epithelial cells throughout the airway system and moreover this expression seems to be dependant on the inflammatory status of the individual. Whether this is the cause or consequence of this inflammation can be debated, but its presence and role in the immune response seems evident.

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Cytoplasmic receptors

A second class of receptors by which epithelial cells can detect the presence of micro-organisms can be found in the cytoplasm, although one could admit that this distinction is somewhat artificial. Some of the TLRs (TLR-3, and -7 through -9) are found in membrane vesicles inside the cytoplasm and are activated when (parts of) micro-organisms are taken up by a cell. Moreover, micro-organisms can produce toxins that can interfere with intracellular signaling events by binding to cytoplasmic enzymes, such as Pertussis toxin (PTx), which inactivates Gi/o type G proteins 93. These observations show that it is hard to define the concept of the “cytoplasmic receptor”. In this paragraph we will focus on those interactions that contribute to the defense mechanisms of the epithelial cell, rather than interactions that seem beneficial for the micro-organism.

The family of NLRs (NOD and Leucine Rich Repeats containing Receptors; also known as NOD-LRR (Nucleotide Oligomerization Domain with Leucine Rich Repeats), NACHT-LRR (NACHT is acronym of different proteins that have a nucleotide binding domain; NAIP, CIITA, HET-E, and TP1), or CATERPILLER (CARD, transcription enhancer, R (purine-binding, Pyrin, lots of LRRs)) is a large family of cytoplasmic pattern recognition receptors, which contains more than 20 family members in mammals 94-96. Common for all NOD-LRR receptors is a central nucleotide-binding oligomerization domain (NACHT), an N-terminal effector-binding domain and C-terminal leucine-rich repeats (LRRs). The NLRs can be subdivided in subfamilies based on effector domains; NODs and IPAFs (ICE protease-activating factor) containing CARD (Caspase recruitment domain) effector domains, NALPs (NACHT-LRR and Pyrin –domain-containing proteins) containing PYD (pyrin) effector domains, and NAIPs (neuronal apoptosis inhibitor protein) containing three BIR (baculoviral IAP repeat) domains as can be seen in figure 3. For many of these cytoplasmic receptors the ligands are not known, but are most

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likely involved in recognition of cytoplasmic PAMPs (Pathogen Associated Molecular Patterns) and endogenous danger signals.

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Figure 3: NLR signaling. Schematic drawing of the different components of the inflammasome with the NOD-Like-Receptors (NOD1, NOD2, NAIP, IPAF, CIITA, NALP1, and NALP-2, -14), the adaptermolecules (ASC, and Cardinal), and the effector molecules (RICK and caspase-1 & -5) leading to NF-κB transcription factor activation and conversion of pro-IL-1β to IL-1β.

NOD1 and NOD2 mediated signallingNOD1 and NOD2 are receptors that can sense cytoplasmic microbial

PAMPs. NOD1 has one and NOD2 has two CARD domains, and both receptors are involved in recognizing peptidoglycan fragments. NOD1 recognizes the peptide γ-D-glutamyl-meso-diaminopimelic acid (meso-DAP), which is found on gram-negative bacteria. NOD2 is the receptor for muramyldipeptide (MDP) which is a peptidoglycan constituent of both Gram

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positive and Gram negative bacteria.96;97. Upon recognition of the ligand the CARD-containing serine/threonine kinase RICK is recruited via CARD-CARD interactions. This in turn mediates ubiquitination of IKKγ, which leads (partly) to NF-κB activation 98. A number of potential regulators of NF-κB activity via NOD have been identified: TAK1, TRIP-6, GRIM-19, and ERBIN 99. In addition to activating NF-κB, NODs can also activate MAP kinase pathways. NOD2 can activate p38 and ERKs, while NOD1 can activate JNK 100;101.

NOD1 and NOD2 can act in synergy with various TLRs to enhance immune responses in Antigen Presenting Cells (APCs) 99. In human monocytes and Dendritic Cells (DCs), NOD1 and NOD2 agonists act cooperatively with LPS to stimulate the production of inflammatory cytokines (TNF-α and IL-6) 102. There have also been synergistic effects described between NOD1 and NOD2 and TLR-3, -4, and -9 103. The interaction between NOD2 and TLR-2 is not synergistic, and it has even been reported that NOD2 antagonizes TLR-2 stimulated production of IL-12, however this effect could not be repeated in another study 100;104.

NALPs mediated signallingThe NALP subfamily is the largest in the NLR family, it contains 14

members. (NALP1 to 14) and is characterized by the PYD effector domains 105. The function of the different NALPs are not well known, however several NALPs form inflammasomes when activated. These inflammasomes are critical for the production of certain proinflammatory cytokines, such as IL-1β and IL-18. Two distinct NALP inflammasomes have been identified: 1) the NALP1 inflammasome (comprising NALP1, the adaptor protein ASC (apoptosis-associated speck-like protein), Caspase-1, and Caspase-5), and 2) the NALP2/3 inflammasome (comprising of ASC, Cardinal, and Caspase-1, in addition to NALP2 or NALP3) 106. Activated NALPs, recruit ASC via their PYD effector domains and this complex interacts with Caspase-1 via a CARD-CARD interaction. In addition, NALP1 can recruit Caspase-5 via its

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CARD domain. NALP2, lacking a CARD domain can recruit Caspase-1 via a CARD-containing adaptor Cardinal.106-110. For activation of NALP1 cell rupture seems to be sufficient to induce inflammasome assembly 111. NALP3 seems more prone to activation by exogenous stimuli (PAMPs; bacterial RNA, (although not by LPS or LTA), antiviral compounds such as R848 and R837, and bacteria such as Staphylococcus aureus and Listeria monocytogenes. In addition, also endogenous danger signals released by dying cells, such as uric acid crystals and extracellular ATP, can activate NALP3 109;112;113

The last subfamily of NLR is NAIPs, their signaling is similar to the other NLRs as they signal via a CARD domain to activate a Caspase ( in this case Caspase-1) we will not go further into detail, also since there is very little known about the expression of and the function of NAIPs in epithelial cells.

Epithelial expression of NLR receptors.Research on NOD in epithelial cells has focused mainly on the intestinal

epithelium, this because mutations in the NOD gene are a strong risk factor for the development of Crohn’s disease, the genotype-specific disease risk for heterozygous is 2.6 (95% CI 1.5–4.5) and for homozygous even 42.1 (4.3−∞) in German and British populations 114. Research has shown that NOD signaling down-regulates the TLR driven activation of cells by gut bacteria, so absence of a functional NOD signaling leads to increased NF-kB activation, resulting in chronic inflammation 104. The expression of NOD1 and NOD2 has been shown in lung epithelial cells, however these cells did not produce the inflammatory cytokines IL-6, IL-8, or MCP-1 in response to NOD1 agonistic meso-DAP (γ-D-glutamyl-meso-diaminopimelic acid) or NOD2 agonistic muramyldipeptide (MDP) 115. Whether these cytokines are the correct readout for NOD activation or whether NOD activation only acts through modulation of a TLR response is unclear. Till this date no report has been published where the expression of NOD1 or 2 has been shown in nasal epithelium, nor what their role there would be. However, seeing the widespread expression

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of NOD1 and NOD2 in various epithelia (tongue, salivary gland, pharyngeal, esophageal, intestinal, cervical, breast, lung, and kidney epithelial cells) 115 it may very well also be expressed in nasal epithelium, where it could interact with TLR signaling in recognizing endogenous and exogenous danger signals. The epithelial NLR field is largely under explored as almost no data has been published on epithelial expression of the other two subfamilies of NLRs (NALPs and NAIPs).

Research opportunities

This review highlights the many mechanisms available to epithelial cells to detect changes in its environment. Through a collection of receptors the epithelium is able to respond to structural components of micro-organisms like bacteria, viruses, and helminthes, or to enzymatic active components of potential allergens. If we would first focus on the external environment an already complex picture emerges. In everyday life we are constantly exposed to many different environmental factors, each with its potential to activate epithelial cells. Although relative unique cascades are downstream of the receptors there seem to be only a few transcription factors acting as targets of these cascades. With NF-κB such a prominent player in all signaling pathways we would need to consider in more detail how signals are processed when more than one signal is present.

Most experiments up to now have focused on the effects of single stimuli, but this is a situation that in every day life may have limited value. Epithelial surfaces like the airways and gut are normally exposed to a multitude of (pathogenic) micro-organisms or other antigenic triggers which would all simultaneously activate their specific receptors. There could be a separation in time for processing of extracellular signals, but then we need to assume that only signals are processed where a pathogenic situation has occurred or where the structural integrity of the epithelium has been compromised.

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Examples would be the receptors located in endosomes or the cytoplasm that can only be activated when the epithelium has been infected, or cell surface receptors that are located more towards the basal side below the tight junctions of the epithelium. A second option of how epithelial cells may deal with multiple activating factors in its environment might well be related to our limited understanding of how signaling cascades target transcription factors. Only a few studies have focussed on more than a single transcription factor, which was usually NF-κB. This introduces a strong bias and would obscure the potential involvement of other transcription factors. However, having said this, it is not trivial to look at other transcription complexes as a source of specificity for responses to environmental signals. A complicating factor in this approach is that outcome will strongly depend on what readout is used for activation. For instance, figure 1 shows that in the TLR cascade the transcription activation of some genes (TNF-α) may only depend on NF-κB activation, some only on the IRF factors (INF-α), and others on both (INFβ). Moreover this dependency may differ when a gene would be activated through another signaling pathway and could be further complicated when other transcription complexes are considered. Even within the NF-κB pathway more attention should be paid to what specific form NF-κB is activated, as multiple different subunits can interact to form distinct subtypes of NF-κB transcription complexes. Traditional activation of transcription by the p65/p50 NF-κB dimer can be seen for inflammatory cytokines, whereas the IκBζ/p50 dimer is required for IL-1R mediated IL-6 induction 116. Remarkably this same IκBζ/p50 dimer is not required for TNF-α mediated IL-6 induction, and additionally inhibits rather than stimulates expression from the TNFα promoter 117. If we would also take into account that NF-κB monomers can interact with IRF 118, Fos, or Jun 119 monomers, an even more complex picture emerges. One option to deal with selection bias would be to look at global transcription patterns using microarrays, but this approach would need a strong focus. It could be applied to the investigation of the stimulatory activity of multiple

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environmental factors simultaneously, especially where one would be able to compare a normal response versus the response in a diseased state.

Next we would like to consider the consequences of activation of all these signaling cascades. There is little doubt that the response of the epithelium contributes to an effective defense against potential pathogens. Indeed as discussed, multiple natural occurring mutations in key signaling molecules affect the ability to efficiently respond to pathogens. Interestingly also here our limited understanding is evident. Why do mutations affect the response to some bacteria and not others when they all signal through TLR-4 or only affect the response to some viruses when they all signal through TLR-3? Surely it will be that other signaling cascades are involved that could differ between the responses for one micro-organism to the next, but it does highlight the complexity and the necessity of interacting signaling pathways. Where the effects of signaling mutants in the response on micro-organisms seem trivial, one unfortunate side effect of these mutants is that some will contribute to a de novo immunological response to otherwise innocent (environmental) factors. When this factor is some protein from the organism itself we are faced with auto immunity, when this factor is found in the environment we are faced with allergy. Two distinct mechanisms may lead to this de novo immunological response. Either it is a direct effect of the environmental factor on the epithelium, leading to a stronger response, or the environmental factor is seen in the context of other danger signals, leading to a deviated response.

In our resume we have strongly focused on the epithelium looking toward the external environment; however this will not be the complete picture. Just like the involvement of Toll-Like Receptors in the development and differentiation of Drosophila, also the Toll-Like Receptors of higher eukaryotes are able to sample the internal environment. Heat shock factors as ligands for TLRs, mast cell tryptase as trigger for the PARs are just a few examples where it has become clear that the role of Pattern Recognition Receptors

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extends far beyond the recognition of just Pathogen Associated Molecular Patterns.

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41

2Allergen induced gene

expression of airway epithelial cells shows a possible role for

TNF-α.

Aram B. Vroling, Dirk Duinsbergen, Wytske J. Fokkens, Cornelis M. van Drunen

Department of Otorhinolaryngology, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands

Allergy. 2007 Nov;62(11):1310-9.

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Abstract

Epithelium is more than a physical barrier for pathogens and allergens, as it is also capable of producing mediators in response to these environmental factors. Some of these mediators have an immuno-modulatory function, suggesting that epithelium is an active component of the immune response. Here we fully characterize the expression profile of airway epithelial cells in response to house dust mite allergen.

H292 cells were exposed to house dust mite extract for 24 hours, RNA and supernatant was used for microarray analysis and multiplex ELISA respectively.

Out of 38,500 genes, 813 were differentially expressed by more than 2-fold and 116 even more than 5-fold. Interestingly, among the most up-regulated genes, a large number are involved in cell-to-cell communication. These include chemokines (CCL-8 and -20, CXCL-1, -2 and -3), cytokines (IL-1α, IL-6, IL-11), anti-inflammatory factors (PTX-3, IL-13Rα, TNF-αIP3), and factors that are involved in repair of the mucosal tissue (LOXL-2, NID-2, HBEGF, MUC-5AC and MUC-5B). Pathway analysis showed that a number of these genes are transcriptionally regulated by TNF-α, which we could detect by Q-PCR at earlier time points after house dust mite exposure. In addition we could detect increased protein levels for TNF-α, IL-6, IL-8, GM-CSF, G-CSF, and IFN-γ using ELISA.

Our data shows a broad range of mediators produced upon allergen exposure, by these mediators epithelial cells can participate in the immune response via recruitment and activation of cells of the immune system.

43

Chapter 2

Introduction

Allergic disease affects a large part of the western population, with a prevalence of more than 20% in the U.S.A. 1. Patients can have symptoms like runny nose, itching of the nose and/or eyes, sneezing, skin rashes, shortness of breath, nausea, fatigue, and can even die of anaphylactic shock. These symptoms can significantly affect the quality of life and performance on the job2. The impact on society is enormous; it has been estimated that absenteeism and low productivity due to allergies cost U.S. companies more than $250 million in 19983;4. Although these indirect costs are high, they are just a fraction compared to the estimated overall health care expenditures attributable to sinusitis, which in 1996 were more than $6 billion5.

Effective treatment will improve quality of life and will also reduce the indirect and direct costs associated with the disease. Although, the two predominant treatments for nasal allergies, corticosteroids and antihistamines, are effective in a large group of patients, not all patients benefit from it4. Corticosteroids, due to their broad action may cause unwanted side-effects, whereas antihistamines only work at the end of the immunological cascade, which may be responsible for their limited efficacy in asthma6. By developing new medication that specifically targets cells in the beginning of the allergic response instead of effector cells, the efficacy and specificity of treatment might be increased.

An interesting target for the development of new drugs are airway epithelial cells, being the first cells an allergen encounters7. Recent research has shown that they are more than just a physical barrier. They are capable of responding to environmental changes, such as invading pathogens, allergen exposure, or wounding8-10. One of the methods that has been used to investigate this is in a model where cultured epithelial cells (e.g. squamous cell carcinoma cell line NCI-H292) are subjected to various stimuli. These reports have shown that they can produce inflammatory mediators such as IL (interleukin)-6, IL-8,

44

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and PGE (prostaglandin)-2 upon activation by allergens11;12. Epithelial cells are activated via the Protease Activated Receptors (PARs), and in particular PAR-2 has been described for activation by allergens13. PARs are innate receptors that have originally been described on endothelial cells as part of the blood clotting system, but that can also be found on many other cell types. It has been shown that not only thrombin but also the proteases that can be found in allergenic mixtures can activate PAR-2. The epithelial response to the allergenic mixture is not preceded by earlier sensitization to the allergen, and many proteolytic allergens are capable of activating these epithelial cells14;15. In our own research we have investigated the mediator release by epithelial cells in response to disruption, with or without concomitant PAR activation. We found a variety of produced mediators much more diverse than the previously described production of IL-6 and IL-8. For instance we found activation increased production of IL-1β, IFN-γ, TNF (Tumor Necrosis Factor)-α, IL-4, G-CSF (Granulocyte Colony Stimulating Factor), GRO (Growth Related Oncogene), EGF (Epidermal Growth Factor), IGF-BP3 (Insulin-like Growth Factor Binding Protein 3), LIF (Leukemia Inhibitory Factor), IP-10 (10 kDa Interferon-γ induced Protein), TGF (Transforming Growth Factor) -β, and VEGF (Vascular Endothelial Growth Factor).

The aim of the experiments described in this manuscript is to map the response of airway epithelial cells to house dust mite allergen (HDM). By better knowledge of the affected genes we can get closer to understanding the role of the epithelium in the mucosal response to allergens. This understanding will help to develop new medication that could inhibit the contribution of the epithelium to the initiation and maintenance of the allergic response. To characterize the expression profile of airway epithelial cells upon HDM stimulation we used a transcriptomics and proteomics approach.

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Materials and methods

Cell culture.NCI-H292 human airway epithelial cells (American Type Culture Collection,

Manassas, VA, USA) were cultured in RPMI 1640 medium (Invitrogen, Breda, The Netherlands) supplemented with 1.25 mM L-glutamine, 100 U/mL

penicillin, 100 µg/mL streptomycin and 10% (v/v) fetal bovine serum (HyClone, Logan, UT, USA). Cells were grown in fully humidified air containing 5% CO2 at 37°C and were sub cultured weekly.

Induction experiment.Cells were cultured to 80% confluence in a 6 wells plate. Before stimulation

experiment, cells were pre-incubated with Hanks’ balanced salt solution (HBSS) for 24 hours. Culture medium was removed and cells were then stimulated with HDM extract diluted in HBSS (2 μg/mL) or with HBSS alone (control condition) for 24 hours. Supernatants were removed and stored for further analysis; cells were used for RNA extraction.

HDM extract.HDM extract was kindly provided by Prof. Dr. M. L. Kapsenberg (AMC,

Netherlands) as a lyophilized powder. It was dissolved in PBS, and then dialyzed against PBS to remove contaminating salts and diluted to a final concentration of 8 μg/mL.

RNA extraction.Total RNA from each sample was extracted using Trizol (Life Technologies,

Inc., Gaitersburg, MD, USA) according to manufacturer’s protocol, followed by purification by nucleospin RNA II (Machery-Nagel, Düren, Germany). The RNA concentration was measured on the nanodrop ND-1000 (NanoDrop Technologies inc., Wilmington, DE, USA). RNA quality was checked by

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Agilent 2100 bio-analyzer (Agilent Technologies, Palo Alto, CA, USA).

Microarray Affymetrix u133 plus 2.0.Human Genome U133 Plus 2.0 Genechip Array (Affymetrix inc., Santa

Clara, CA, USA) representing 47,000 transcripts, including 38,500 well-characterized genes, was used in the analysis of HDM-induced genes. Technical handling of microarrray experiments were performed at the MicroArray Department (MAD) of the University of Amsterdam (Amsterdam, The Netherlands), a fully licensed microarray technology centre for Affymetrix Genechip® platforms and official Dutch Affymetrix Service Provider. In short, biotin-labeled cDNA samples were prepared as described in the Affymetrix expression analysis technical manual (Affymetrix) using 7.5 μg of purified total RNA as template for the reaction. For this the One-Cycle cDNA Synthesis Kit (Affymetrix) was used. The Array images were acquired using a GeneChip Scanner 3000 (Affymetrix) and analyzed with Rosetta Resolver (Rosetta Biosoftware, Seattle, WA, USA).

Microarray data analysis.Genechip images were loaded into Rosetta Resolver where we used a

factorial design to compare the three chips that were hybridized with RNA of control treated cells with the three chips that were hybridized with RNA of the HDM treated cells. This experimental design for the analysis will only yield genes of which the average expression in one condition statistically differs from the average expression of the other condition. To select for all statistically significant differentially expressed genes we used post-test statistics, which are described in statistical analysis section.

Real-time reverse transcriptase PCR.Quantitative PCR (polymerase chain reaction) was used to validate the

differential expression of selected genes. RNA isolated from control treated

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and HDM treated cells using the nucleospin RNA II, subsequently cDNA was synthesized using the MBI Fermentas first strand cDNA synthesis kit (Fermentas GmbH, St. Leon-Rot, Germany). PCR was performed on Bio-Rad iCycler (Bio-Rad, Veenendaal, The Netherlands). TaqMan® (TaqMan is a registrated trademark. The trademarkholder is Roche molecular systems, Pleasanton, CA, USA) primer and probe sequences for IL-6, IL-8, GAPDH (glyceraldehyde-3-phosphate dehydrogenase) and β-actin16-19 were obtained from Sigma-Aldrich (Haverhill, UK). For the other genes we ordered TaqMan® gene expression assays from Applied Biosystems (Nieuwerkerk a/d IJssel, The Netherlands). The sequences for PCR reactions are: IL-6; sense: 5’-TGA-CAA-ACA-AAT-TCG-GTA-CAT-CCT-3’, probe: 5’-FAM-TTA-CTC-TTG-TTA-CAT-GTC-TCC-TTT-CTC-AGG-GCT-G-TAMRA-3’, antisense: 5’-AGT-GCC-TCT-TTG-CTG-CTT-TCA-C-3’, IL-8; sense: 5’-CCA-CAC-TGC-GCC-AAC-ACA-GAA-ATT-ATT-G-3’, probe: 5’-FAM-AAG-CTT-TCT-GAT-GGA-AGA-GAG-CTC-TGT-C-TAMRA-3’, antisense: 5’-GCC-CTC-TTC-AAA-AAC-TTC-TCC-ACA-ACC-C-3’, β-actin; sense: 5’-TGA-GCG-CGG-CTA-CAG-CTT-3’, probe: 5’-Texas red-ACC-ACC-ACG-GCC-GAG-CGG-BHQ2-3’, antisense: 5’-TCC-TTA-ATG-TCA-CGC-ACG-ATT-T-3’, GAPDH; sense: 5’-GAA-GGT-GAA-GGT-CGG-AGT-C-3, probe: 5’-Texas red-CAA-GCT-TCC-CGT-TCT-CAG-CC-BHQ2-3’, antisense: 5’-GAA-GAT-GGT-GAT-GGG-ATT-TC-3’. The ordered assays had the following assay IDs: CCL20; Hs00171125_m1, IL11; Hs00174148_m1, IRAK2; Hs00176394_m1, COL5A1; Hs00609088_m1, PTX3; Hs00173615_m1, SCARA3; Hs00212206_m1, SERPINE2; Hs00299953_m1, SERPINF1; Hs00171467_m1, TNFAIP3; Hs00234712_m1, ZNF488; Hs00399237_m1, KRT4; Hs00361611_m1, NID2; Hs00201233_m1, IL13RA2; Hs00152924_m1, and MCTP1; Hs00226801_m1. We performed all PCR assays three times, on all three experimental replicates. Expression changes are calculated using comparitive Ct, indicating the difference in threshold cycle between the control condition and the HDM treatment, after correcting a sample for the expression of the housekeeping gene.

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Protein multiplex Luminex Bio-Plex assay.Supernatants of HDM and control treated cells were stored at -20°C

until analysis. Cytokine levels in supernatant of cells were determined using the xMAP technology (Luminex Corporation, Austin, TX, USA). A Bio-Plex Human Cytokine 17-Plex Panel kit (Bio-Rad, Veenendaal, The Netherlands) was used and analyzed on the Bio-Plex workstation (Bio-Rad). All standards were diluted in the same serum free culture medium where the cells were put in during treatment. Concentrations were calculated from a dilution series of standards using the Luminex software. Lower detection limits are indicated per cytokine.

Statistical analysis.Experiments were performed in triplicate and simultaneously. Genechip

images and datasets were uploaded into the server of the NBIC (Netherlands Bioinformatics Centre) using the Rosetta Resolver Biosoftware package, which was also used for statistical analyses. Triplicates were compared in a factorial design using an error-weighted one-way analysis of variance (ANOVA). To control for multiple comparisons we applied a Bonferroni multiple test correction. We reported as significant genes only those that reached significance at level P≤0.01. Testing 54,675 probes at this level, we expected the average number of false positive results to be 550 or less. We found that 3,491 reached the significance level of 0.01, exceeding the average number of false positives.

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Results

House dust mite protease specific induction of epithelial cells.To see whether our model functions as described in literature and to

investigate the properties of our stimuli we exposed epithelial cells to 1) HDM extract 2 ug/mL, 2) heat treated HDM extract 2 ug/mL (30 min. at 95°C) (HDM HT), 3) LPS 1 ug/mL, 4) heat treated LPS 1 ug/mL (LPS HT) and to TNF-α 50pg/mL. When cells were exposed to HDM their IL-8 production increased 19 times, this increase could almost fully be abrogated by heat treatment of the HDM extract, inactivating the protease activity. When cells are exposed to LPS they also show an increased production of IL-8, this increase could not be abrogated by heat treatment (see figure 1).

0

200

400

600

800

1000

1200

Blank

HDM

HDM HT

LPS

LPS H

TTN

F

IL-8

(pg/

mL)

Figure 1: IL-8 production by H292 cells after dif-ferent stimuli. Values are average and standard deviation over triplicate samples. Blank = culture medium exposed control, HDM = house dust mite exposed (2 ug/mL), HDM HT = heat treated HDM (2 ug/mL), LPS = lipopolysaccharide (1 ug/mL), LPS HT = heat treated LPS (1ug/mL), TNF = Tu-mor necrosis factor alpha (50 pg/mL)

Gene expression ratio in airway epithelial cells between HDM stimulated and controls.

Global expression values were measured in three experiments where cells were either stimulated with HDM diluted in HBSS or with HBSS alone as the control condition. Using the Rosetta Resolver software, we calculated the ratio of the expression between the two conditions. Genes were considered significantly differentially expressed when p≤0.01 after one-way ANOVA with a Bonferroni multiple test correction. The 54,675 sequences that are present on the Affymetrix U133 plus 2.0 represent approximately 38,500 genes. In

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our experiments, we found 614 sequences significantly up-regulated and 474 sequences down-regulated by more than 2-fold. These represent 458 up-regulated and 353 down-regulated genes.

Validation of the microarray data.A total of 62 out of 56,675 sequences on the chip were marked as controls

by Affymetrix, based on their constitutive expression levels. Among these were sequences for the stably expressed genes 18S, 28S rRNA, β-actin, and GAPDH which are commonly used for normalization in gene expression profiling experiments. These sequences were used as an internal validation. The average expression ratio between control and HDM stimulated conditions of these 62 assigned sequences in our experiment was on average 1.05 (±0.31) showing that these control genes were indeed not affected by HDM stimulation.

Additionally, we selected 11 up-regulated genes (CCL-20 (MIP-3α; Macrophage inflammatory protein 3 alpha), PTX (pentraxin)-3, IL-8, IL-6, IL-11, IL-13RA, SERPIN (serine proteinase inhibitor)-E2, TNF-AIP3 (TNF-α inducible protein 3), NID (nidogen)-2, MCTP (multiple C2-domains with two transmembrane regions)-1, IRAK (interleukin-1 receptor-associated kinase)-2) and 5 down-regulated genes (COL (collagen)-5A, SERPIN-F1, KRT (cytokeratin)-4, SCARA (scavenger receptor class A)-3, and ZNF (zinc finger protein)-488) for independent confirmatory PCR. Expression levels in all PCR samples were normalized against GAPDH and β-actin. These housekeeping genes were selected as their expression is not affected by HDM stimulation, with their fold-change in our microarray experiment being -1.15 and -1.01 respectively. We could not calculate an expression ratio for PTX-3 since expression of PTX-3 in the control treated cells was below detection level of the PCR. Table 1 shows the ratios derived from the microarray experiment and the expression ratios calculated from the PCR. The correlation between the PCR results and the microarray results

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is high (uncorrected R=0.73 with R2=0.54) and even higher when corrected for the outlier CCL-20 (corrected R=0.92 with R2=0.85), indicating an 85% correlation between the PCR results and the microarray results.

Mediator PCR expressi-on fold change

microarray ratio’s

CCL-20 7.8 (± 0.4) 100.0 (± 1.5)IL-8 9.2 (± 1.3) 39.3 (± 1.3)IL-13RA 5.4 (± 0.4) 29.8 (± 1.5)IL-6 7.5 (± 1.1) 26.8 (± 1.3)IL-11 1.8 (± 0.3) 23.2 (± 1.3)SERPIN-E2 4.7 (± 0.4) 18.9 (± 1.3)TNF-AIP3 3.7 (± 0.4) 12.3 (± 1.1)NID-2 4.8 (± 0.5) 10.4 (± 1.4)MCTP-1 2.9 (± 0.4) 8.8 (± 1.1)IRAK-2 3.4 (± 0.4) 7.4 (± 1.3)COL-5A -1.7 (± 0.4) -5.6 (± 1.5)SERPIN-F1 -2.1 (± 0.4) -8.3 (± 1.2)KRT-4 -3.0 (± 0.3) -10.0 (± 1.6)SCARA -1.3 (± 0.3) -10.3 (± 1.4)ZNF-488 -1.6 (± 0.4) -11.2 (± 1.5)PTX-3 N.D. 47.7 (± 1.5)

Table 1: Validatory PCR of significantly different genes. 16 genes were selected for confir-matory PCR. All values are given as mean (± SD). PCR expression is given as fold change between control and HDM stimulated, and normalized against GAPDH and β-actin

Gene ontology.After checking our microarray data by PCR and checking the expression

of assigned housekeeping genes, we concluded that our microarray data were indeed valid. For further detailed analysis, we focused initially on the genes that were up-or down-regulated more than 5-fold, limiting the number to 46 down-regulated and 70 up-regulated genes. Using their ascribed gene ontology, we sorted the genes by their biological process or molecular function. We found 38 genes known to be associated with cell communication, in this group there are cytokines, chemokines, growth factors, and receptors. Other

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groups we found are immunity and defense (5), receptor activity (4), enzyme inhibitor activity (5), nucleic acid binding (9), metabolism (14), transport (4), calcium ion binding (8). A number of genes (6) were single members of an ontology group, and are here pooled as “miscellaneous”. No ontology was known for 24 genes. Table 2 shows the regulated genes and the ontology groups they are appointed to.

Table 2. Highly differentially expressed genes sorted by ontology in functional groups. In our array 813 genes were significantly differentially expressed, and 116 even more than 5-fold. This table lists those genes according to the ontology they are assigned to.

Gene alias Fold change

Gene name/description P-value Accession #

Cell communication

CCL20 100.0 MIP-3α 0 NM_004591

IL1A 41.0 interleukin 1 α 0 NM_000575

IL8 39.3 interleukin 8 0 NM_000584

CXCL3 37.9 GRO-γ 0 NM_002090

CXCL2 31.5 GRObeta 0 NM_002089

IL6 26.8 interleukin 6 0 NM_000600

STC1 25.3 stanniocalcin 1 0 NM_003155

IL11 23.2 interleukin 11 0 NM_000641

EREG 18.7 Epiregulin 3.64E-43 NM_001432

FST 17.7 Follistatin 0 NM_013409

LOXL2 12.6 lysyl oxidase-like 2 0 NM_002318

MUC5AC 12.1 mucin 5, subtypes A and C 2.06E-43 AW192795

CXCL1 11.9 GRO-α 0 NM_001511

IL1RL1 11.3 interleukin 1 receptor-like 1 1.23E-16 NM_003856

DUSP6 11.3 dual specificity phosphatase 6 0 NM_001946

AREG 10.3 amphiregulin 0 NM_001657

HBEGF 10.1 heparin-binding EGF-like growth factor 0 NM_001945

S100A9 8.3 calgranulin B, S100 calcium binding protein A9 8.94E-17 NM_002965

SLIT2 8.3 slit homolog 2 (Drosophila) 2.58E-42 NM_004787

EDNRA 8.2 endothelin receptor type A 8.23E-20 NM_001957

IRAK2 7.4 interleukin-1 receptor-associated kinase 2 3.24E-24 NM_001570

COL8A1 7.1 collagen VIII alpha 1 0 NM_001850

MUC5B 6.6 mucin 5, subtype B 1.55E-26 AI697108

PARK7 6.4 Parkinson disease 7 0 NM_007262

RASD1 5.5 RAS, dexamethasone-induced 1 4.74E-17 NM_002341

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LTB 5.5 lymphotoxin beta 4.74E-17 NM_002341

ITGB8 5.4 integrin beta 8 5.89E-16 NM_002214

BIRC3 5.3 baculoviral IAP repeat-containing 3 9.08E-20 NM_001165

IL1B 5.2 interleukin 1 beta 1.44E-17 NM_000576

SPRY4 5.2 Sprouty homolog 4 3.11E-09 NM_030964

CHRNB1 5.1 cholinergic receptor, nicotinic, beta polypeptide 1 8.18E-13 NM_000747

ASB9 -5.1 ankyrin repeat and SOCS box-containing 9 8.66E-18 NM_024087

CRABP2 -5.2 cellular retinoic acid binding protein 2 1.59E-16 NM_001878

EPHA4 -5.3 ephrin receptor A4 2.43E-09 NM_004438

MASS1 -5.3 monogenic, audiogenic seizure susceptibility 1 5.98E-08 NM_032119

COL5A1 -5.6 collagen V alpha 1 2.76E-09 NM_000093

VIPR1 -5.8 vasoactive intestinal peptide receptor 1 2.85E-15 NM_004624

PPP2R2B -7.3 protein phosphatase 2, regulatory subunit B, beta 0 NM_004576

Imunity and defense

PTX3 47.7 pentraxin 3 0 NM_002852

PBEF1 5.8 pre-B-cell colony enhancing factor 1 0 AA873350

C1QTNF1 5.4 C1q and tumor necrosis factor related protein 1 2.50E-22 NM_030968

SELENBP1 -5.2 selenium binding protein 1 8.08E-12 NM_003944

DEFB1 -5.6 defensin, beta 1 1.12E-17 NM_005218

Receptor activity

IL13RA2 29.8 interleukin 13 receptor, alpha 2 0 NM_000640

F3 5.3 coagulation factor III 2.52E-41 NM_001993

PTPRB 5.4 protein tyrosine phosphatase, receptor type, B 6.40E-29 NM_002837

SCARA3 -10.3 scavenger receptor class A, member 3 4.46E-43 NM_016240

Enzyme inhibitor activity

SERPINE2 18.9 serine proteinase inhibitor, clade E, member 2 0 NM_006216

SERPINB4 8.0 serine proteinase inhibitor, clade B, member 4 0 NM_002974

SERPINB8 5.9 serine proteinase inhibitor, clade B, member 8 5.56E-14 NM_002640

CDKN1C -8.1 cyclin-dependent kinase inhibitor 1C 1.52E-12 NM_000076

SERPINF1 -8.3 serine proteinase inhibitor, clade F, member 1 0 NM_002615

Nucleic acid binding

TNFAIP3 12.3 tumor necrosis factor, alpha-induced protein 3 0 NM_006290

C10orf48 10.2 chromosome 10 open reading frame 48 1.46E-28 NM_173576

TNIP2 6.8 TNFAIP3 interacting protein 2 7.57E-23 NM_024309

ETV5 6.2 ETS translocation variant 5 3.64E-19 NM_004454

KLF4 6.1 Kruppel-like factor 4 8.39E-13 NM_004235

MAF -6.1 Transcription factor Maf 1.05E-25 NM_005360

SEMA6D -6.8 semaphorin 6D 2.30E-28 AL036088

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KIAA1305 -8.5 unknown 4.27E-13 XM_370756

ZNF488 -11.2 zinc finger protein 488 1.75E-13 NM_153034

Metabolism

ADAM8 14.8 A disintegrin and metalloproteinase domain 8 0 NM_001109

SUV420H2 8.4 suppressor of variegation 4-20 homolog 2 5.25E-12 NM_032701

RAB3B 7.7 RAS related protein 3B 2.29E-10 AU156710

ABCC3 6.9 ATP-binding cassette, sub-family C, member 3 1.38E-34 NM_020037

CYP26B1 5.6 cytochrome P450, fam 26, subfam B, polypeptide 1 1.20E-09 NM_019885

KIAA0703 5.5 Probable calcium-transporting ATPase KIAA0703 0 NM_014861

MICAL2 5.0 microtubule associated monoxygenase, calponin and LIM domain cont. 2

3.87E-23 NM_014632

HPGD -5.3 Hydroxyprostaglandin dehydrogenase 15-(NAD) 1.53E-12 NM_000860

AKR1C3 -5.4 aldo-keto reductase family 1, member C3 0 NM_003739

PAPSS2 -6.3 3'-phosphoadenosine 5'-phosphosulfate synthase 2 0 NM_004670

ALPP -7.2 Alkaline phosphatase, placental type precursor 1.06E-22 NM_001632

C5orf4 -7.4 chromosome 5 open reading frame 4 4.55E-10 NM_016348

KRT5 -8.6 cytokeratin 5 1.59E-18 NM_000424

KRT4 -10.0 cytokeratin 4 3.07E-13 NM_002272

Transport

CHAC1 9.6 cation transport regulator-like 1 3.78E-42 NM_024111

SLCO4A1 9.1 solute carrier organic anion transp fam mem 4A1 0 NM_016354

SLC7A11 6.9 solute carrier family 7, member 11 0 NM_014331

DMBT1 5.6 deleted in malignant brain tumors 1 1.10E-30 NM_004406

Calcium ion binding

NID2 10.4 nidogen 2 0 NM_007361

CAPN8 5.4 Calpain 8 8.50E-39 AW242997

CACNG4 -5.9 calcium channel, gamma subunit 4 3.80E-06 NM_014405

MYL9 -6.0 myosin, light polypeptide 9 1.43E-09 NM_006097

MATN2 -6.4 matrilin 2 0 NM_002380

LMCD1 -8.1 LIM and cysteine-rich domains 1 6.78E-19 NM_014583

SLC40A1 -8.5 solute carrier family 40, member 1 0 NM_014585

LOC63928 -17.0 hepatocellular carcinoma antigen gene 520 2.21E-38 NM_022097

Miscellaneous

TMEM46 35.9 Transmembrane protein 46 0 AW664964

IER3 7.1 immediate early response 3 0 NM_003897

SYNGR3 -5.3 synaptogyrin 3 1.55E-25 NM_004209

FLJ32115 -5.7 chromosome 12 open reading frame 46 3.15E-32 NM_152321

SNCA -5.7 synuclein, alpha 2.43E-19 BG260394

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DKFZ-P586A0522

-15.5 methyltransferase like 7A 0 NM_014033

Unclassified

IGFL1 25.4 insulin growth factor-like family member 1 0 NM_198541

230778_at 21.2 unknown 3.43E-19 AA010315

TRBV5-4 13.5 T cell receptor beta variable 5-4 6.09E-25 AF043179

ARRDC3 12.1 arrestin domain containing 3 0 NM_020801

MCTP1 8.8 multiple C2-domains with two transmem regions 0 NM_024717

DKFZ-P564D166

7.8 tetratricopeptide repeat, ankyrin repeat 6.37E-12 XM_371074

APBB1 6.6 amyloid beta (A4) precursor protein-binding, B1 3.33E-09 U62325

LRRC49 6.4 Leucine rich repeat containing 49 1.40E-45 NM_017691

LOC149194 5.3 unknown 6.91E-09 AI937119

SCEL 5.2 Sciellin 1.04E-27 NM_003843

LOC339260 -5.2 unknown 1.02E-17 BC043529

MGC24665 -5.2 unknown 2.73E-11 NM_152308

238463_at -5.3 unknown 7.20E-11 AA448328

KARCA1 -7.1 kelch/ankyrin repeat containing cyclin A1 6.53E-07 NM_001007255C1orf21 -7.5 chromosome 1 open reading frame 21 1.54E-12 NM_030806

242396_at -7.7 unknown 1.68E-11 AA195408

228653_at -8.1 SAM domain containing 1 2.62E-07 NM_001030060LBH -9.0 likely ortholog of mouse limb-bud and heart gene 5.02E-12 NM_030915

239638_at -9.0 unknown 7.71E-16 AI608696

235892_at -10.2 unknown 2.55E-27 AI620881

APCDD1 -12.4 adenomatosis polyposis coli down-regulated 1 0 NM_153000

ARD38 -16.0 ankyrin repeat domain 38 0 NM_181712

RCSD1 -21.8 RCSD domain containing 1 0 AI659418

The ontology analysis revealed that from the group of genes that was up-regulated the strongest (more than 15 fold), a substantial number (10 out of 16), belong to the cell communication group, and that a partly overlapping number had been reported to be under the transcriptional control of TNF-α signaling. Using network analysis we built a computational model from interactions between genes described in literature. In this case we used our geneset containing genes whose expression was statically significantly changed by more than 2-fold. Figure 2 shows the scheme containing genes within our dataset that have a known interaction with TNF-α. The found

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expression pattern corresponds with the expression pattern based on the effects of TNF-α on the genes described in literature.

Figure 2: Interaction network of TNF-α regulated genes. Computational model of interactions as described in literature. The input for our model was a list of differentially expressed genes (P≤0.01, abs. fold change > 2) with known transcriptional regulation by TNF-α. In this scheme genes are depicted at their cellular location, and are colored by fold change in expression between saline and HDM exposure (green= down-regulated, red is up-regulated).

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To explore this observation further, we looked for expression of the TNF receptor (CD120a) and TNF-α in our microarray experiments. mRNA for the receptor (CD120a) is present in both control and HDM-stimulated samples, with no significant difference in expression levels between these two conditions. However, TNF-α mRNA was undetectable in the microarray. When we investigated the TNF-α expression using a TaqMan® gene expression assay, we could detect TNF-α mRNA; however, the expression levels were quite low, on average 4,000 times lower than β-2-microglobuline (range 1,000-17,000), a highly abundant constitutively expressed gene in epithelial cells. When we look at the expression profile of TNF-α at different time-points after HDM stimulation, and compare that to medium control, we see that TNF-α is highest after 2.5 hours, but is still higher at 24 hours (see figure 3).

-1

0

1

2

3

4

5

6

0 0.25 0.5 0.75 2.5 4.5 7.5 24

time (hour)

fold

cha

nge

Figure 3: TNF-α mRNA expression following HDM exposure. Upon HDM exposure epithe-lial cells up-regulate the expression of TNF-α. This graph shows the expression followed in time after the start of sti-mulation. Fold changes calculated compared to control stimulated cells, after normalization for β-2-microglobulin.

Protein expression of chemokines and cytokines under stimulating and control conditions.

As indicated above, our initial analysis showed that a substantial number of highly up-regulated genes belong to the cell communication group (table 2). Next we investigated if we could confirm expression of some of these genes, and especially TNF-α, on a protein level. Using the Luminex platform, we measured 17 known chemokines and cytokines in the supernatant of the

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HDM stimulated or control condition cells (Table 3). In the samples that were taken after 24 hours exposure to HBSS, we could detect secreted protein for IL-6, IL-7, IL-8, IL-13, GM-CSF (granulocyte-macrophage colony-stimulating factor), and IFN-γ. For all these proteins, we could also see significant mRNA levels in our microarrays. Moreover, the protein concentrations of IL-6, IL-8, GM-CSF, IFN-γ, and IL-13 were statistically significantly higher after HDM stimulation than after culture medium condition. In the supernatants taken after 24 hours of HDM stimulation, we could detect significant expression of TNF-α and the other chemokines and cytokines, but not for IL-12.

mediator control HDMIL-2 (8) n.d. 36.7 (±10.1)IL-4 (2) n.d. 42.8 (±2.7)IL-6 (8) 302.7 (±16.4) ≥1500IL-8 (2) 496.1 (±32.2) ≥1500IL-10 (2) n.d. 5.3 (±1.9)GM-CSF (2) 8.2 (±14.3) 76.5 (±15.4)IFN-γ (8) 10.6 (±2.1) 97.9 (±5.0)TNF-α (2) n.d. 26.7 (±2.1)IL-1β (2) n.d. 6.4 (±0.5)IL-5 (8) n.d. 8.5 (±1.9)IL-7 (8) 55.5 (±5.2) 39.7 (±4.5)IL-12 (8) n.d. n.d.IL-13 (2) 11.7 (±0.0) 19.1 (±6.4)IL-17 (2) n.d. 57.6 (±2.0)G-CSF (30) n.d. 161.4 (±6.2)MCP-1 (30) n.d. 37.1 (±2.6)MIP-1β (8) n.d. 22.3 (±2.1)

Table 3: Protein production of H292 cells in 24 hours in which they are exposed to HDM or control condition. Epithelial cells were exposed to house dust mite extract for 24 hours, after which the supernatant of the cells was analyzed for presence of cytokines and chemokines. Concentrations are presented as average (±SD) of triplicate experiment in pg/mL. Lower de-tection limits are indicated behind respective mediator (pg/mL). n.d. = not detectable; below detection level.

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Discussion

Our results show that airway epithelial cells are able to express a wide variety of genes when exposed to house dust mite. Furthermore, the expression profile upon house dust mite provocation shows genes that indicate that these cells can interact with cells of the immune system and in this way contribute to the immune response. In our experiments we identified 813 genes out of a total of 38,500 that were either up- or down-regulated by more than 2-fold; 116 of these genes even more than 5-fold (70 up- and 46 down-regulated). Among these are genes known to be produced by epithelial cells upon allergen stimulation (IL-6, IL-8)12, and genes known to be produced by epithelial cells in stress or damage situations (IL-1β, ICAM (intercellular adhesion molecule)-1, TGF-β)20-22. Additionally, we found genes not previously associated with the response to allergens (FST (follistatin), PTX-3, LOXL (lysyl oxidase-like)-2)23-25, and genes not previously described to be expressed by epithelial cells (IL-13RA2)26. The mayor value of this study lies in the extent of the investigated response, which we show here in our experiments to extend far beyond the responses described in similar models, where expression of only a limited number of proteins or genes was possible.

Interestingly, of the genes up-regulated more than 20-fold, almost half (8/17) is involved in cell communication, and “cell communication” is also the largest group of the ontology groups we found. When we look at cytokine and chemokine expression profiles in our microarray data there are differences. Of the 42 chemokines that are known, 9 are expressed in our microarray experiment, 3 of which are unaltered (CCL-16 (HCC-4), XCL-1 (lymphotactin), and XCL-2 (SCM-1β)). The other expressed chemokines are regulated by the house dust mite extract (CXCL-1 (+11.9), CXCL-2 (+31.5), CXCL-3 (+37.9), CXCL-8 (+39.3), CCL-20 (+100), CCL-28 (-4.6)). In contrast to the chemokines, a substantial number of the 53 known cytokine genes are

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present on the array and reveal a constitutive expression (IL-7, IL-13, IL-15, IL-20, IL-23, IL-1RA, LIF, CD40L, FasL, 4-1BBL, APRIL, BlyS, MIF). An additional 8 show differential regulation (IL-6 (+26.8), IL-11(+23.2), LTB(+5.5), TRAIL(-2.9), TGF-β1( -2.3), TGF-β2(+2.4), IL-1α(+41.4), IL-1β(+5.2)). The expression of these genes may play a role in the maintenance of the milieu in the mucosa in which the cells of the innate and adaptive immune system function. This indicates that expression of chemokines may be a more regulated than continuous process, suggesting that the epithelium may not contribute strongly to constant turnover of inflammatory cells by chemotaxis under normal conditions but contribute to recruitment under inflammatory conditions. The epithelium does constitutively express cytokines, thereby constantly influencing the milieu in which the cells of the immune system function.

When we looked for common regulators of the 813 differentially expressed genes, we found that 49 (6%) of all the differentially expressed genes have been reported to be influenced by TNF-α. In the group of 5-fold up- or down-regulated genes this is even more; 16% (18 genes out of 116). In the microarray experiment, we could not detect any significant mRNA expression of TNF-α in culture medium controls, or at 24 hours after stimulation with allergen. However, using a more sensitive PCR we could show that TNF-α mRNA was present, and its expression was significantly higher in HDM stimulated cells than controls, at earlier time-points, showing the transient nature of this expression. Also we could detect TNF-α protein in the supernatant after HDM stimulation, which corresponds with the transient expression of the TNF-α mRNA.

In the group of proteins linked to TNF-α, we found genes that have been investigated in connection to allergic disease (ICAM-1, ICAM-2, IL-6, IL-8, IL-11, MUC) or with severe asthma-associated aspirin intolerant nasal polyposis (COX-2/PTGS-2). The TNF-superfamily is also represented by TRAIL (TNFAIP3), a protein regulating apoptosis. TNF-superfamily is also

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indirectly represented by TIMP (tissue inhibitor of metalloproteinase)-3, an inhibitor of the protease responsible for the release of soluble TNF-α from the membrane-bound precursor. The prominent presence of TNF-α induced genes in our expression screen suggests this “TNF-α cluster” may play a role in the allergen-induced response of epithelium. Indeed some of the proteins have been reported to be induced by TNF-α, and we have shown that even concentrations that epithelial cells are capable to secrete can induce IL-8 production (see figure 1). However these results cannot exclude that another factor may also be involved in signaling of cells to neighboring cells or to themselves.

TNF-α is normally associated with Th1 diseases, such as rheumatoid arthritis, and the gene-expression that is influenced by TNF-α may contribute to the pathophysiology of these diseases27. Our data show that TNF-α and TNF-induced genes may also be involved in a Th2-mediated response. Given that there is growing interest in adopting anti-TNF-α in Th2-mediated diseases it might be well worth the effort to try and discriminate which of the genes regulated by TNF-α are responsible for the efficacy of the anti-TNF-α treatment 28;29.

In our experimental setup we chose to look at the mRNA profile after 24 hours of continuous exposure, rather than immediately after initial exposure, this because epithelium in patients is exposed to allergens for a prolonged time. A consequence of this approach is that genes that are transiently up-regulated upon PAR activation might not be detected, but it does allow us to detect mediators that are induced by previously secreted mediators (as is the case for TNF-α). Even though our data is derived from a model that uses a squamous cell carcinoma cell line in an in vitro experiment, we were able to find some similarities to the data that Lilly and co-workers present in their study where they compared the expression profile of cells obtained from bronchial brushes from asthmatics before and after allergen challenge30. Like they we also find genes involved in the immune function and cell growth, and

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we find a number of genes previously associated with allergy, but not before linked to epithelial cells.

The epithelial response is not specific for a single type of allergen or epitope, as is the case with allergen-mediated cross-linking of specific IgE antibodies on the surface of mast cells causing degranulation and histamine release. In our model, the proteolytic activity of the allergen is the cause of response and other allergenic mixtures could potentially induce a similar response. In both in vitro and in vivo experiments, involvement of airway epithelial cells in the response to allergens has been established. Due to the methods used, the magnitude and the variety of signaling molecules that the airway epithelium can express has previously not been shown to be so vast. The role of these mediators can lie in maintaining the physical integrity of the mucosal tissue, in attracting cells that lead to an increased immune response, or to alter the activity of those cells. It may also lie in deviating the response away from immune activation, for instance by attracting phagocytosing cells. More likely, epithelial cells are capable of influencing all these processes, some to a greater extent than others, and further research is needed to find out how the epithelium modifies the allergic response. Our research provided us with a large number of leads through which the epithelium may alter or influence the tissue response to allergens. In this respect it will be of importance to see how, and if this mapped response to allergen may differ between allergic or healthy individuals.

Reference List

American Academy of Allergy, A. a. I. 1998. Task Force on Allergic Disorders. 1. Executive Summary Report.Dykewicz, M. 2003. 7. Rhinitis and sinusitis. 2. J Allergy Clin Immunol 111:520-529.Hewitt Associates LLC. 1998. 3. The Effects of Allergies in the Workplace.van Drunen, C., E. O. Meltzer, C. Bachert, J. Bousquet, and W. J. Fokkens. 2005. 4. Nasal allergies and beyond: a clinical review of the pharmacology, efficacy, and safety of mometasone furoate. Allergy 60 Suppl 80:5-19.Ray, N. F., J. N. Baraniuk, M. Thamer, C. S. Rinehart, P. J. Gergen, M. Kaliner, S. Josephs, 5. and Y. H. Pung. 1999. Healthcare expenditures for sinusitis in 1996: contributions of

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asthma, rhinitis, and other airway disorders. J.Allergy Clin.Immunol. 103:408-414.Van Cauwenberge, P., C. Bachert, G. Passalacqua, J. Bousquet, G. W. Canonica, S. R. 6. Durham, W. J. Fokkens, P. H. Howarth, V. Lund, H. J. Malling, N. Mygind, D. Passali, G. K. Scadding, and D. Y. Wang. 2000. Consensus statement on the treatment of allergic rhinitis. European Academy of Allergology and Clinical Immunology. Allergy 55:116-134.Takizawa, H. 2005. Bronchial epithelial cells in allergic reactions. 7. Curr.Drug Targets.Inflamm.Allergy 4:305-311.Holgate, S. T., J. Holloway, S. Wilson, F. Bucchieri, S. Puddicombe, and D. E. Davies. 8. 2004. Epithelial-mesenchymal communication in the pathogenesis of chronic asthma. Proc.Am.Thorac.Soc. 1:93-98.Mutsaers, S. E., J. E. Bishop, G. McGrouther, and G. J. Laurent. 1997. Mechanisms of 9. tissue repair: from wound healing to fibrosis. Int.J.Biochem.Cell Biol. 29:5-17. Asokananthan, N., P. T. Graham, D. J. Stewart, A. J. Bakker, K. A. Eidne, P. J. Thompson, 10. and G. A. Stewart. 2002. House Dust Mite Allergens Induce Proinflammatory Cytokines from Respiratory Epithelial Cells: The Cysteine Protease Allergen, Der p 1, Activates Protease-Activated Receptor (PAR)-2 and Inactivates PAR-1. J Immunol 169:4572-4578. Cromwell, O., Q. Hamid, C. J. Corrigan, J. Barkans, Q. Meng, P. D. Collins, and A. 11. B. Kay. 1992. Expression and generation of interleukin-8, IL-6 and granulocyte-macrophage colony-stimulating factor by bronchial epithelial cells and enhancement by IL-1 beta and tumour necrosis factor-alpha. Immunology 77:330-337. Asokananthan, N., P. T. Graham, J. Fink, D. A. Knight, A. J. Bakker, A. S. McWilliam, 12. P. J. Thompson, and G. A. Stewart. 2002. Activation of Protease-Activated Receptor (PAR)-1, PAR-2, and PAR-4 Stimulates IL-6, IL-8, and Prostaglandin E2 Release from Human Respiratory Epithelial Cells. J Immunol 168:3577-3585. Cocks, T. M. and J. D. Moffatt. 2001. Protease-activated Receptor-2 (PAR2) in the 13. Airways. Pulmonary Pharmacology & Therapeutics 14:183-191. Hong, J. H., S. I. Lee, K. E. Kim, T. S. Yong, J. T. Seo, M. H. Sohn, and D. M. Shin. 2004. 14. German cockroach extract activates protease-activated receptor 2 in human airway epithelial cells. Journal of Allergy and Clinical Immunology 113:315-319. Shin, S. H., Y. H. Lee, and C. H. Jeon. 2006. Protease-dependent activation of nasal 15. polyp epithelial cells by airborne fungi leads to migration of eosinophils and neutrophils. Acta Otolaryngol. 126:1286-1294. Birks, E. J., P. B. J. Burton, V. Owen, A. J. Mullen, D. Hunt, N. R. Banner, P. J. R. Barton, 16. and M. H. Yacoub. 2000. Elevated Tumor Necrosis Factor-{alpha} and Interleukin-6 in Myocardium and Serum of Malfunctioning Donor Hearts. Circulation 102:352III. Dolganov, G. M., P. G. Woodruff, A. A. Novikov, Y. Zhang, R. E. Ferrando, R. Szubin, 17. and J. V. Fahy. 2001. A Novel Method of Gene Transcript Profiling in Airway Biopsy Homogenates Reveals Increased Expression of a Na+-K+-Cl- Cotransporter (NKCC1) in Asthmatic Subjects. Genome Res. 11:1473. Magner, W. J., A. L. Kazim, C. Stewart, M. A. Romano, G. Catalano, C. Grande, N. 18. Keiser, F. Santaniello, and T. B. Tomasi. 2000. Activation of MHC Class I, II, and CD40 Gene Expression by Histone Deacetylase Inhibitors. J Immunol 165:7017. Brabender, J., R. V. Lord, K. Wickramasinghe, R. Metzger, P. M. Schneider, J. M. 19. Park, A. H. Holscher, T. R. DeMeester, K. D. Danenberg, and P. V. Danenberg. 2002. Glutathione S-Transferase-Pi Expression Is Downregulated in Patients With Barrett’s Esophagus and Esophageal Adenocarcinoma. Journal of Gastrointestinal Surgery 6:359-367. Kovacs, E. J. 1991. Fibrogenic cytokines: the role of immune mediators in the 20. development of scar tissue. Immunol.Today 12:17-23. Raeburn, D. and S. E. Webber. 1994. Proinflammatory potential of the airway epithelium 21. in bronchial asthma. Eur.Respir.J. 7:2226-2233. Doucet, C., D. Brouty-Boye, C. Pottin-Clemenceau, C. Jasmin, G. W. Canonica, 22.

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and B. Azzarone. 1998. IL-4 and IL-13 specifically increase adhesion molecule and inflammatory cytokine expression in human lung fibroblasts. Int.Immunol. 10:1421-1433. Phillips, D. J. 2005. Activins, inhibins and follistatins in the large domestic species. 23. Domest.Anim Endocrinol. 28:1-16. Baruah, P., I. E. Dumitriu, G. Peri, V. Russo, A. Mantovani, A. A. Manfredi, and P. 24. Rovere-Querini. 2006. The tissue pentraxin PTX3 limits C1q-mediated complement activation and phagocytosis of apoptotic cells by dendritic cells. J.Leukoc.Biol. Molnar, J., K. S. Fong, Q. P. He, K. Hayashi, Y. Kim, S. F. Fong, B. Fogelgren, K. 25. M. Szauter, M. Mink, and K. Csiszar. 2003. Structural and functional diversity of lysyl oxidase and the LOX-like proteins. Biochim.Biophys.Acta 1647:220-224. Fichtner-Feigl, S., W. Strober, K. Kawakami, R. K. Puri, and A. Kitani. 2006. IL-26. 13 signaling through the IL-13alpha2 receptor is involved in induction of TGF-beta1 production and fibrosis. Nat.Med. 12:99-106. Janes, K. A., S. Gaudet, J. G. Albeck, U. B. Nielsen, D. A. Lauffenburger, and P. K. 27. Sorger. 2006. The response of human epithelial cells to TNF involves an inducible autocrine cascade. Cell 124:1225-1239. Russo, C. and R. Polosa. 2005. TNF-alpha as a promising therapeutic target in chronic 28. asthma: a lesson from rheumatoid arthritis. Clin.Sci.(Lond) 109:135-142. Thomas, P. S. 2001. Tumour necrosis factor-alpha: the role of this multifunctional 29. cytokine in asthma. Immunol.Cell Biol. 79:132-140. Lilly, C. M., H. Tateno, T. Oguma, E. Israel, and L. A. Sonna. 2005. Effects of allergen 30. challenge on airway epithelial cell gene expression. Am.J.Respir.Crit Care Med. 171:579-586.

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3Primary nasal epithelium

exposed to house dust mite extract shows activated expression in allergics.

Aram B. Vrolinga, Martijs J. Jonkerb, Silvia Luitena, Timo M. Breitb, Wytske J. Fokkensa, Cornelis M. van Drunena

a Department of Otorhinolaryngology, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlandsb Integrative Bioinformatics Unit, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands

American Journal of Respiratory Cell and Molecular Biology. 2008 Mar;38(3):293-9

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Abstract

Nasal epithelial cells form the outer layer that protects against environmental factors. However this defense is not just physical, it has been shown that epithelial cells respond by producing of inflammatory mediators that may affect local immune responses. In this research we set out to characterize potential differences between the responses of nasal epithelium from healthy and allergic individuals to house dust mite allergen (HDM). These differences will help us to define local mechanisms that could contribute to allergic disease expression. Epithelial cells were cultured from nasal biopsies taken from five healthy and five allergic individuals. These cultures were exposed for 24 hours to culture medium containing house dust mite allergen, or to culture medium alone. Isolated RNA was used for microarray analysis. Gene ontology of the response in healthy epithelium revealed mainly up-regulation of chemokines, growth factors, and structural proteins. Moreover we saw increased expression of two transcription factors (NF-κB and AP-1) and their regulatory members. The expression pattern of epithelium from allergic individuals in the absence of the HDM stimulus suggests that it already is in an activated state. Most striking is that, while the already activated NF-κB regulatory pathway remained unchanged in allergic epithelium, the AP-1 pathway is down-regulated upon exposure to HDM allergen; this is contrary to what we see in healthy epithelium. Clear differences in the expression pattern exist between epithelial cells isolated from healthy and allergic individuals at baseline and between their responses to allergen exposure; these differences may contribute to the inflammatory response.

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Introduction

The mucosal layer in the nose is constantly exposed to viruses, bacteria, parasites, and harmless allergens. It is crucial that a correct immune response is initiated to all these environmental factors. When harmless allergens are mistaken for dangerous pathogens the immune system will mount an unwanted inflammatory response to the allergens, resulting in allergic inflammation. An important player in the initiation of immune responses is the antigen presenting cell that resides in the mucosal tissue; the dendritic cell (DC). In recent years it has become increasingly clear that the peripheral DC initiates the immune response within an active local tissue environment, and that epithelial cells can play a role in this initiation process. Epithelial cells are more than a physical barrier and are themselves able to detect and respond to environmental signals. Epithelial cells can produce mediators that affect recruitment of immunocompetent cells to the local tissue and help create a microenvironment where these cells function 1;2

In relation to the initiation of the immune response it is very interesting that epithelial cells produce mediators that can influence DCs. An example of such a mediator is MIP-3α, the chemokine for CCR-6 positive Langerhans cells (LCs), which is produced by bronchial epithelial cells after a variety of stimuli 3;4. Not only recruitment is affected by epithelial mediators, but epithelial expressed GM-CSF and TGF-β guide the differentiation of respectively, myeloid DCs and LCs from their precursors 5;6. Activation of tissue resident LCs is partly dependent on locally produced TNF-α and IL-1 7;8, and recently TSLP (thymic stromal lymphopoietin) produced by epithelial cells was shown to be important in the activation of DC mediated allergic inflammation 9-11. Currently, there are just a few players for which the effect on DC function has been documented. Even less is known on potential differences in mediators produced by epithelial cells from healthy or allergic individuals or if any of these differences contribute to the expression or development of allergic

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disease.In previous experiments we have investigated the epithelial response of

a bronchial epithelial cell line H292 to house dust mite allergen. There we could show that epithelial cells display a broad and diverse expression of genes in response to exposure to allergen and that a substantial number of these regulated genes have a function in cell communication based on their gene ontology classification. Moreover we identified a potential regulatory network centered around TNF-α and NF-κB 12.

In this research we wanted to expand on these observations and investigate the response induced by house dust mite allergen in primary epithelium from healthy and house dust mite allergic individuals. Cultures of primary epithelial cells obtained from nasal biopsies were exposed to house dust mite extract diluted in culture medium, or with culture medium alone. RNA from this experiment was used for microarray analysis and the resulting expression profiles were subjected to bioinformatical, biostatistical, and interaction network analysis. Characterization of potential differences in the expression pattern at baseline or in response to allergen exposure will provide valuable insight into the role of the epithelium in the allergic response. Identification of the mechanism by which nasal epithelium influences the allergic response can lead to development of new therapies that target the epithelial cells.

Materials and Methods

Patient characteristics.This study was reviewed and approved by the medical ethical committee

of the Amsterdam Medical Center and all participants read and signed an informed consent. Five allergic volunteers (age 19-55) and five healthy non-smoking volunteers (age 21-32) were selected based on skin prick test for house dust mite (HDM) and other common allergens, and a nasal allergen

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provocation to asses their response. Only mono-typically HDM allergic and non-allergic volunteers were included. Allergic individuals had refrained from using any medication for their allergy in the four weeks prior to the visit when biopsies were taken. Biopsies were taken from the lower edge of the inferior turbinate, 1 and 2 cm from the anterior end, using Fokkens’ forceps with a cup diameter of 2.5 mm. Local anesthesia was achieved by application of adrenalin and cocaine under the inferior turbinate without touching the biopsy site, during 10 minutes.

Primary epithelial cell culture.Primary cells were obtained by digesting nasal biopsies of volunteers with

0.5 mg/mL collagenase 4 (Worthington Biochemical Corp., Lakewood, NJ, USA) for 1 hour in Hanks’ balanced salt solution (Sigma-Aldrich, Zwijndrecht, the Netherlands). Subsequently cells were washed with Hanks’ balanced salt solution (HBSS) and resuspended in BEGM (Lonza Clonetics, Breda, the Netherlands) and seeded in two wells of a 6 wells plate. Culture medium was replaced every other day. Cells were grown in fully humidified air containing 5% CO2 at 37°C.

House dust mite extract and exposure experiment.House dust mite extract containing biologically non-relevant trace amounts

of LPS was kindly provided by Prof. Dr. M. L. Kapsenberg (AMC, Netherlands) as a lyophilized powder. It was dissolved in phosphate buffered saline (PBS), and then dialyzed against PBS and diluted to a stock concentration of 8 μg/μL. Cells were cultured for two weeks to 80% confluence and were subsequently pre-incubated with HBSS for 8 hours prior to exposure to HDM. Pre-incubation medium was removed and cells were exposed to HBSS containing a previously determined optimal concentration of house dust mite extract (2 μg/mL) or with HBSS alone for 24 hours. Supernatants were removed and stored for further analysis; cells were used for RNA extraction.

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RNA extraction.Total RNA from each sample was extracted using Trizol (Life Technologies,

Inc., Gaitersburg, MD, USA) according to manufacturer’s protocol, followed by purification by nucleospin RNA II (Machery-Nagel, Düren, Germany). The RNA concentration was measured on the nanodrop ND-1000 (NanoDrop Technologies inc., Wilmington, DE, USA) and RNA quality was checked on the Agilent 2100 bio-analyzer (Agilent Technologies, Palo Alto, CA, USA).

Microarray Affymetrix u133 plus 2.0.Human Genome U133 Plus 2.0 Genechip Array (Affymetrix inc., Santa

Clara, CA, USA) representing 47,000 transcripts, including 38,500 well-characterized genes was used in the analysis of HDM-induced genes. Technical handling of microarray experiments was performed at the MicroArray Department (MAD) of the University of Amsterdam (Amsterdam, The Netherlands), a fully licensed microarray technology centre for Affymetrix Genechip® platforms and official Dutch Affymetrix Service Provider. In short, biotin-labeled cDNA samples were prepared as described in the Affymetrix expression analysis technical manual (Affymetrix) using 3 μg of purified total RNA as template for the reaction. For this the One-Cycle cDNA Synthesis Kit (Affymetrix) was used. The Array images were acquired using a GeneChip Scanner 3000 (Affymetrix) and analyzed with Affymetrix GeneChip® Operating Software (Affymetrix).

Microarray data analysis and statistics.The quality of the images was checked by visual inspection, and all

raw data passed the quality criteria for average background, scale factors, percentage present calls, 3’/5’ ratios GAPDH, 3’/5’ ratios beta-actin, hybridization spike-in controls, poly-A spike-in controls. The data also passed a set of quality control checks provided by the affy, affyPLM and affyQCreport packages from Bioconductor (http://www.bioconductor.org/). Expression

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values were calculated using the robust multi-array average (RMA) algorithm 13, and statistically analyzed for differential gene expression using ANOVA (MAANOVA package, version 0.98.8 14). The permutation based Fs test was used for hypothesis testing 15, and all p-values were adjusted for false discovery rate correction 16. In order to quantify the effect of HDM extract on gene expression, pair-wise statistical tests were performed to analyze: 1) the effect of HDM on epithelial cells from healthy individuals separately, 2) the effect of HDM on epithelial cells from allergic individuals separately, and 3) the difference in response to HDM in allergic compared to healthy individuals.

Ontology-, cluster-, and network analysis.Gene ontology was done using the online gene ontology program GOstat

(http://gostat.wehi.edu.au/) where we used curated datasets and subsets to investigate the overrepresentation of gene ontology groups 17. Cluster analysis on all significantly affected genes was done by transforming the means of the expression values for a gene in the four groups (healthy or allergic after control exposure, or after HDM exposure) to Z-scores and using unsupervised K-means clustering based on correlation, for this we used Spotfire DecisionSite Functional Genomics. Network analysis was performed using Pathway Architect software (Stratagene, La Jolla, CA, USA), here we used the curated dataset to build a regulation interaction network. We overlaid the colors used in our K-means cluster analysis to clarify the relative expression changes of the genes in our network.

Quantitative PCR.Quantitative PCR was used to validate the differential expression of

selected genes. Isolated RNA from control treated and HDM treated cells was used to synthesize cDNA using the MBI Fermentas first strand cDNA synthesis kit (Fermentas GmbH, St. Leon-Rot, Germany). PCR was performed

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on Bio-Rad iCycler (Bio-Rad, Veenendaal, The Netherlands). mRNA specific TaqMan® gene expression assays for ACTB, ATF3, CCL20, CNFN, EGR1, FLG, GAPDH, GATA3, GCSF, GROA, IL1RA, IL12B, IL13Rα2, IL8, KRT4, MCP1, MIP1B, PLAUR, SPINK7, TNFA, TNFR1, TNFIP3 were ordered form from Applied Biosystems (Nieuwerkerk a/d IJssel, The Netherlands). We performed all PCR assays three times, on all samples. Fold changes were calculated using the comparative Ct method.

Results

Validation of the microarray results.After incubation with HDM extract, we identified 555 probe sets that

were statistically differentially expressed in primary epithelium from healthy individuals and 301 probe sets that were statistically differentially expressed in primary epithelium from allergic patients. This collection of probe sets was first curated by, when possible, discarding the less specific x_at probe sets and the splice variant specific s_at probe sets. For analysis we further selected only those genes for which the expression level of their probe sets change by more than 1.5-fold (see supplemental table E1). In healthy epithelium the original 555 probe sets correspond to 209 uniquely annotated genes and 19 unannotated and/or hypothetical sequences in our curated dataset. Of these genes, 206 were up-regulated and 22 were down-regulated. In allergic epithelium the original 301 probe sets correspond to 87 uniquely annotated genes and 12 unannotated and/or hypothetical sequences in our curated dataset. Here 62 genes showed increased and 37 decreased expression.

The results of this microarray experiment were validated by independent real time PCR on the same starting material used for the microarray analysis. A selection of 20 genes that had revealed an increased, decreased, or unchanged expression level in either the healthy or allergic curated gene set was used for this validation experiment. After normalization for three

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household genes (GAPDH, β-actin and β-2-microglobulin), the relative change in expression of these genes measured by PCR was directly comparable to the relative change obtained from our microarray experiment, both for the healthy epithelium (r = 0.621, p = 0.006) and for the allergic epithelium (r = 0.735, p < 0.001) (data is shown in supplementary table E2).

Global analysis reveals an activated state in primary allergic epithelium.Within our curated dataset the expression of only a few genes (9/311)

shows identical statistically significant changes upon HDM exposure in healthy and allergic epithelium. The expression of the other genes either change in healthy or in allergic epithelium, and in many cases shows a differential response in both groups (Figure 1).

Figure 1: Gene expressions in diffe-rent groups. This scatter plot shows the fold changes for the genes in our curated dataset as calculated in the healthy control group (x-axis), and in the allergic group (y-axis). Genes that show less than a fold change of 1.5 in both groups are omitted.

The absence of an overlap in the response could be due to a differential change in the expression profile by the HDM extract, to intrinsic differences between healthy and allergic epithelium at baseline, or a combination of both. To investigate the relative contribution of these factors we first used Principal Components Analysis (PCA) on the whole data collection of microarray chips. Most of the variation observed for the chips (57 %, Figure 2) lies in the difference between healthy and allergic epithelium (PC1). What was unexpected that the next largest contribution (12 %) comes from the variability

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of the individuals within the healthy epithelium group (PC2), with only a very limited variation in the allergic epithelium group. Exposure to HDM extract only has third largest contribution (7 %) to the observed variation between all the genes in the healthy and allergic group.

Figure 2: Principal components analysis. A (left): showing the relative contribution to variance for the first 10 principal components. B (right): showing a scatter plot of all 20 arrays and their individual relative contribution to principal component 1 (PC1) and principal component 2 (PC2). Array ID’s are indicated in the graph.

The PCA showed that not only differences in response to allergen, but also differences at baseline between healthy and allergic contribute to variation; therefore we used K-means clustering on the expression levels in the four groups. This allows us to compare groups of genes that share specific expression patterns. The 311 genes that change their expression in healthy and/or allergic epithelium can be effectively separated into 12 clusters (Figure 3). Baseline expression of a given gene in 5 out of 12 clusters is substantially higher in allergic epithelium than in healthy epithelium. Together these clusters (number 7, 9, 10, 11, and 12) represent 74 % of all regulated genes. The K-mean cluster analysis shows that for some of these clusters expression after HDM exposure is high and remains largely unchanged in the allergic group and is up-regulated to a certain degree in the healthy group. For cluster 9 (8 genes) the expression level in healthy epithelium after HDM

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exposure is even higher than in the allergic group, in cluster 11 (46 genes) the expression level is similar, while in cluster 12 (129 genes) the expression level in the healthy group does not quite reach the expression level of the allergic group. Evidently, a substantial group of genes in the epithelium from allergic individuals already displays an activated state at baseline. Interestingly, some clusters (number 3, 4, 7, 10) show changes in the allergic group while the expression in the healthy group remains unchanged, while two genes (cluster 5) even show an opposite response to allergen exposure.

Figure 3: K-means clusters. Here all 311 genes from curated dataset (see results section) were transformed to Z-scores, and were organized in 12 clusters using K-means clustering. Supplemental table 1 gives all 311 genes and their respective cluster number. AB = allergic at baseline, AH = allergic after HDM exposure, CB = healthy control at baseline, CH = healthy control after HDM exposure.

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The activated state in allergic epithelium is reflected by only a few ontology classes.

With only a limited number of expression profiles describing the effect of HDM exposure in healthy and allergic epithelium we wondered whether these patterns could be linked to specific functions of these genes.

Ontology analysis for genes that change their expression due to exposure to HDM extract in the primary epithelium from healthy individuals shows that these genes are principally involved in cell-to-cell contact. Cell communication (GO:7154) is by far the biggest group (66 genes) that is significantly overrepresented in the curated healthy data set (p=2.4x10-5). Related ontology groups are also overrepresented: signal transduction (GO:7165, p=4.6x10-4), receptor binding (GO:5102, p=2.8x10-5), extra-cellular space (GO:5615, p=8.9x10-5), and transcription factor activity (GO:3700, p=2.9x10-6). These changes seem to be a consequence of a general response of healthy epithelium to environmental factors as the ontology group response to external stimulus (GO:9605, p=4.4x10-7) and the related groups response to wounding (GO:9611, p=5.8x10-6), response to abiotic factors (GO:9628, p=1x10-4), and response to stress (GO:6950, p=2.4x10-5) are all significantly overrepresented in healthy gene set. Other processes that are affected are cell proliferation (GO:8283, p=3.3x10-9), (negative regulation) of cell death (GO:43069 p=3.5x10-4 and GO:8219 p=1.9x10-4 respectively), and intracellular junctions (GO:5911, p=3x10-4). A similar picture emerges when we combine the K-mean cluster analysis with the gene ontology class analysis. As clusters 9, 11, and 12 represent 58% of all genes that behave differently between healthy and allergic epithelium with expression in healthy epithelium going up by HDM exposure and expression remaining high in allergic epithelium, the activated status of allergic epithelium is best described by genes involved in cell communication and cell proliferation.

Although HDM extract also affects the expression pattern in the primary epithelium of allergic individuals, these genes do not seem to

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represent a specific gene ontology class. The only class that is significantly overrepresented is protease inhibitor activity (GO:0030414, p=8.9x10-3), but of this group only a few genes are statistically altered in allergic epithelium (SPINK-5, SPINK-6, SPINK-7, SERPIN-B3, C3, and WFDC-5).

Involvement of the NF-κB and AP-1 transcription factor complexes in the differential gene expression of healthy and allergic epithelium.

Now we found cell communication and cell proliferation activated in epithelial cells of allergic individuals, we wanted to find regulators that could be responsible for this activated state. To find these regulators we used regulation network analysis. In our network analysis (see Figure 4) we saw that the expected players involved in cell communication: inflammatory markers (IL-1α, IL-1β, and TNF-α), chemokines (IL-8, GRO-α, GRO-β, and IP-10), growth factors (EREG, AREG, and HBEGF), and receptors (TLR-3, PLAUR, PTGER-4, and IL-7R). More interestingly, this regulation seems to be mediated by proteins from the transcription factor complexes NF-κB (NFKB1, NFKB2, NFKBIA, and NFKBIZ), and AP-1 (FOS, JUN, JUNB, FOSL1, and ATF-3). When we use a color to identify in which cluster the genes from figure 3 are in the regulation network analysis we see that these transcription factor complexes also share a similar expression pattern. The activated state we identified above is also reflected at the transcription factor level, with the NF-κB and the non-canonical AP-1 transcription factors (JUNB and FOSL1) mapping to cluster 11. Interestingly, the canonical AP-1 transcription factors (FOS and JUN) behave differently. FOS and ATF-3 are expressed at similar levels at baseline in healthy and allergic epithelium (cluster 8) and expression goes up in healthy epithelium, but goes down in allergic epithelium. For JUN the baseline expression is higher in allergic (cluster 9), but again expression in allergic epithelium is down-regulated and in healthy epithelium is up-regulated upon allergen exposure.

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Figure 4: Regulation Interaction Network. We used the 311 genes in the curated data set to create a regulatory network, showing known regulatory interactions between these genes. Colours correspond with the colours used in figure 3.

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Discussion

The first important observation coming from our data is that for a selective group of genes the allergic epithelium already is in an activated state. Baseline expression levels for these genes are higher in allergic individuals than corresponding levels in healthy individuals. Moreover for these genes, allergen exposure in healthy individuals leads to an increase in their expression levels, while the levels in allergic individuals remain largely unchanged. In our analysis of which processes are affected by the allergic status we find the genes belonging to the ontology classes cell communication and proliferation. In cell communication we find chemokines (IL-8, GRO-α, GRO-β, and IP-10) and cytokines (IL-1α, IL-1β, IL-1ε, and TNF-α) which have a known and well documented functions in the recruitment and activation of cells of the immune system. In cell communication we also find genes for growth factors (CTGF, HBEGF, AREG, EREG, and FGF-5) and intercellular junction (TJP-1, -2, CLDN-1, -4, and OCLN). These groups are known to be involved in the repair of damaged epithelium that, in this case, could be the consequence of cleavage of intracellular junction proteins by the protease activity contained within the HDM allergen extract. Where a substantial number of the regulated genes in epithelium from healthy individuals can be categorized in well-defined gene ontology classes, this does not hold true for the regulated genes in allergic epithelium. Only a single class of protease inhibitors is significantly enriched. Most likely this reflects a protective mechanism in allergic epithelium to counteract the activity of the protease activities contained within the allergen extract or those released by tissue resident mast cells (tryptase, chymase). Although relatively little attention has been given to the role of proteases in health and disease it is important to note that mutations in one of the protease inhibitors SPINK-5, as well as the protease ADAM-33, have been associated to asthma 18;19. Other proteases like the matrix metallo-proteinases play a crucial role in the local

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tissue remodeling associated with asthma 20;21.In this research we set out to characterize the response of epithelial cells;

in order to do so we chose to culture cells taken from a biopsy for two weeks, after which the cell cultures contain only epithelial cells, thus eliminating any contamination of gene expression by other cell-types. One of our concerns was that culturing the cells would alter the baseline expression, or the ability to respond, in a way that would prevent us from detecting differences between healthy and allergic individuals. Given that we detect so many well-established mediators that have been shown prior both in vitro and in vivo gives us confidence that the differences in the expression profiles are likely to reflect to situation in situ. Another deliberate choice in our model was to stimulate the cells for 24 hours and then investigate the mRNA profile. We wanted to mimic the continuous exposure that the nasal epithelium is subject to in vivo. As a consequence we were able to determine the relevant expression patterns of the NF-κB and AP-1 transcription factor families. However, our data do not reveal how this state is achieved after allergen exposure. For this purpose, earlier time points after induction should be investigated and this could well have consequence for the overall outcome of our experiment, with other genes and/or regulatory pathways playing a more prominent role.

There is little overlap in the response to HDM extract in primary epithelium from healthy and allergic individuals. As described above a substantial group of genes that are up-regulated in healthy individuals are already expressed at high levels in allergic epithelium even without exposure to allergen. To expand on this observation it would seem that the induced expression of these “activated” genes is part of the normal response in healthy primary epithelium. How is this activated status in allergic epithelium induced or maintained? Both healthy and allergic individuals are constantly exposed to house dust mite allergen in every day life. Epithelial cells of healthy individuals do express PAR-2 (Protease Activated Receptor 2), though at lower levels than in allergic individuals, so in vivo activation of epithelial cells

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by protease containing HDM extract prior to isolation could occur in both 22. However, in vitro experiments with the epithelial cell line H292 have shown that the effect of PAR2 activation normally is transient, with expression levels of the responding genes returning to baseline levels 72 hours after the initial exposure to HDM extract. Therefore it does not seem likely that the activated state we see at baseline can be directly maintained during the two weeks of culturing that precedes our HDM-induction experiment, not without a positive feedback loop.

In previous work we have suggested that TNF-α, induced by HDM exposure, could be a central player both in mediating the HDM effect, as well as in restimulating epithelial cells. No matter whether we assume a direct effect of HDM or an indirect effect through a HDM-induced feedback loop, the outcome is different for healthy and allergic epithelium. This would lead us to conclude that the allergic status could be aggravated in allergic individuals because of an inability to shut this response down. This would explain the high level of correspondence between genes activated in healthy epithelium and those already activated in allergic epithelium. Alternatively, the “activated” status could be not related to the in vivo action of HDM on epithelium directly, but to the indirect effect that allergen-induced mediators from other tissue resident cells like mast cells have on the epithelium. However, it is not entirely clear how this would explain the correspondence between the HDM-induced genes in our in vitro experiment and the “activated” state in allergic epithelium.

When we assume a differential regulation of the HDM-response in epithelium of healthy and allergic individuals, this seems also reflected in the response of two important transcription factor complexes. The transcription factor NF-κB is a family of (hetero)dimeric proteins that has been linked to inflammation in many cell types, and is known to regulate (and to be regulated by) many different mediators 23;24. That we find genes belonging to the NF-κB family up-regulated in response to allergen in epithelium from healthy individuals

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is perhaps not surprising, but given the complex interactions between the family members it is hard to firmly establish NF-κB as the responsible factor for the observed induction of genes 25. The NF-κB proteins NFKB1/p105 and NFKB2/p100 are the cytoplasmic precursors of respectively the nuclear factors p50 and p52 26;27. Whereas homodimers of p50 or p52 are linked to transcriptional repression, the formation of heterodimers with the NF-κB REL-family members is linked to transcriptional activation 28;29. In our microarray experiments RELB is expressed at a considerably higher level in allergic compared to healthy epithelium and the expression levels are unaffected by allergen exposure. Interestingly, it has been described that knock-out mice for NFKB1 show a reduced stress response with lower IL-6 and COX-2/PTGS-2 levels, suggesting that at least in the overall outcome NFKB1 is required for a correct response to environmental signals 30. Given that the expression of these genes remains high when allergic epithelial cells are cultured for two weeks in absence of HDM suggests that NF-κB in allergic individuals could be (partly) responsible for maintaining the activated state. Although these observations could well explain the activated state in allergic epithelium or the induction of genes in healthy epithelium it fails to account for how this activated state is maintained or why this does not occur in epithelium from healthy individuals. Further experiments are needed to investigate whether epigenetic modification of key-regulators differs between healthy and allergic epithelium, or if the presence of an activating auto-feedback loop in allergic epithelium could be responsible for the maintenance of the allergic phenotype in tissue culture. Also, one might wonder whether the observed effect can be observed for other allergens and whether allergen-specific PAMP receptors could be involved in the response of epithelial cells to allergens.

What is interesting is that epithelial cells also show differential expression for some of the AP-1 family members. In these family members it is strange that the non-canonical members FOSL1 and JUND show the same pattern as NF-κB whereas FOS and JUN, the genes for c-FOS and c-JUN protein, do

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show an increase in healthy individuals, but are actually down-regulated in allergic. If this expression could be linked to a protective mechanism against the effects of NF-κB activation, than this could explain some of the differences we see between healthy and allergic. It has been described that in Drosophila AP-1 is required to down-regulate NF-κB target genes by interfering with promoter binding 31. If this mechanism also applies in humans, then the up-regulation of FOS/JUN in healthy epithelium may contribute to the down-regulation of NF-κB regulated genes, and the absence of this response in allergic epithelium may contribute to the maintenance of the activated state seen in allergic epithelium. Interestingly, the FOS-related AP-1 family member ATF-3 displays a similar expression profile as the FOS gene itself. Identified as a stress factor, this bZIP protein in its homodimeric form acts as transcriptional repressor, but when it heterodimerizes with members of the JUN family it acts as a transcriptional activator 32.

Airway epithelium is becoming more widely accepted as an active player in the response to allergens, however it was unknown if the response to allergen exposure would differ between allergic and healthy individuals. We have shown that not only the response differs, but also the expression at baseline is different between healthy and allergic individuals. Linking our transcriptional observations to functional consequences is hard. Foremost, it is not clear how differences in the transcription level translate into effects on the protein level. This is further complicated by the formation of different homo- and hetero-dimers that each can have their distinct effects, by the functional interaction of the NF-κB and AP-1 transcription factors at promoter sites, and even by the formation of hetero-dimers between NF-κB, and AP-1 family members. Despite all these considerations the allergic epithelium seems an important target in the treatment, and possibly prevention, of allergic disease. Reducing the activated state in allergic epithelium may have direct consequence for the manifestation of complaints, for instance by reducing the influx of effector cells, and understanding how and why the

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response in allergic epithelium differs from that in healthy epithelium may help in preventing the initiation of the allergic response.

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tein

1, z

inc

1.72

0.

007

-1.1

1 0.

961

3 D

9042

7 ch

r7q2

2.1

2011

69_s

_at

BH

LHB

2 ba

sic

helix

-loop

-hel

ix d

omai

n co

ntai

ning

, cla

ss B

, 2

1.11

0.

763

2.11

0.

029

11

BG

3260

45

chr3

p26

2286

17_a

t B

IRC

4BP

ba

culo

vira

l IA

P re

peat

-con

tain

ing

4 bi

ndin

g pr

otei

n -1

.66

0.01

9 -1

.17

0.8

7 A

A14

2842

ch

r17p

13.2

20

5289

_at

BM

P2

bone

mor

phog

enet

ic p

rote

in 2

1.

14

0.83

2.

81

0 12

A

A58

3044

ch

r20p

12

2320

94_a

t C

15or

f29

chro

mos

ome

15 o

pen

read

ing

fram

e 29

1.

12

0.90

5 1.

94

0.01

6 12

A

U14

4048

ch

r15q

14

2269

01_a

t C

17or

f58

chro

mos

ome

17 o

pen

read

ing

fram

e 58

1.

94

0.00

1 1.

06

0.99

9 10

A

I214

996

chr1

7q24

.2

2194

96_a

t C

2orf2

6 ch

rom

osom

e 2

open

read

ing

fram

e 26

1.

06

0.96

6 1.

59

0 12

N

M_0

2301

6 ch

r2q1

3

87

Chapter 3Fo

ld

chan

geP

valu

eFo

ld

chan

geP

-va

lue

Clu

ster

as

cess

ion

Chr

omos

omal

ID

S

ymbo

ls

Nam

es

alle

rgic

al

lerg

ic

heal

thy

heal

thy

num

ber

num

ber

Loca

tion

2177

67_a

t C

3 co

mpl

emen

t com

pone

nt 3

-1

.62

0.01

2 -1

.32

0.14

9 7

NM

_000

064

chr1

9p13

.3-p

13.2

20

5625

_s_a

t C

ALB

1 ca

lbin

din

1, 2

8kD

a 2.

19

0.20

7 -1

.49

0.01

3 3

AW

0149

27

chr8

q21.

3-q2

2.1

2055

25_a

t C

ALD

1 ca

ldes

mon

1

1.04

0.

998

2.61

0

2 N

M_0

1849

5 ch

r7q3

3 20

9715

_at

CB

X5

Chr

omob

ox p

rote

in h

omol

og 5

-1

.15

0.53

-1

.55

0.00

5 6

L075

15

chr1

2q13

.13

2054

76_a

t C

CL2

0 ch

emok

ine

ligan

d 20

(MIP

3 al

pha)

-1

.24

0.11

8 4.

88

0 8

NM

_004

591

chr2

q33-

q37

2274

58_a

t C

D27

4 C

D27

4 an

tigen

-1

.57

0.00

3 2.

15

0.00

1 11

A

I608

902

---

2233

81_a

t C

DC

A1

cell

divi

sion

cyc

le a

ssoc

iate

d 1

-1.0

1 0.

998

-1.6

7 0.

021

6 A

F326

731

chr1

q23.

3 23

6313

_at

CD

KN

2B

cycl

in-d

epen

dent

kin

ase

inhi

bito

r 2B

-1

.24

0.30

2 1.

550

11A

W44

4761

ch

r9p2

1 20

9667

_at

CE

S2

carb

oxyl

este

rase

2

1.71

0.

02

1.02

0.

999

3 B

F033

242

chr1

6q22

.1

2023

57_s

_at

CFB

co

mpl

emen

t fac

tor B

-1

.53

0.03

7 -1

.42

0.05

1 7

NM

_001

710

chr6

p21.

3 23

9629

_at

CFL

AR

C

AS

P8

and

FAD

D-li

ke a

popt

osis

regu

lato

r 1.

33

0.37

1 2.

410

12A

I634

046

chr2

q33-

q34

2093

57_a

t C

ITE

D2

Cbp

/p30

0-in

tera

ctin

g tra

nsac

tivat

or 2

1.

44

0.03

4 1.

96

0 12

A

F109

161

chr6

q23.

3 23

8967

_at

CLD

N1

clau

din

1 1.

08

0.90

3 3.

78

0 12

A

I924

046

chr3

q28-

q29

2014

28_a

t C

LDN

4 cl

audi

n 4

1.05

0.

934

2.66

0.

002

12

NM

_001

305

chr7

q11.

23

2243

29_s

_at

CN

FN

corn

ifelin

4.

29

0.03

6 -1

.15

0.83

5 4

AB

0495

91

chr1

9q13

.2

2220

73_a

t C

OL4

A3

colla

gen,

type

IV, a

lpha

3

1.05

0.

941

1.93

0

2 A

I694

562

chr2

q36-

q37

2269

39_a

t C

PE

B2

cyto

plas

mic

pol

yade

nyla

tion

elem

ent b

indi

ng p

rote

in 2

1.

24

0.39

8 1.

76

0 12

A

I202

327

chr4

p15.

33

2200

90_a

t C

RN

N

corn

ulin

2.

23

0.00

4 1.

01

0.99

9 3

NM

_016

190

chr1

q21

2091

01_a

t C

TGF

conn

ectiv

e tis

sue

grow

th fa

ctor

-1

.08

0.82

3 2.

81

0 11

M

9293

4 ch

r6q2

3.1

2236

79_a

t C

TNN

B1

Cat

enin

bet

a-1

-1.0

3 0.

945

1.82

0.

016

11

AF1

3008

5 ch

r3p2

1 20

4470

_at

CX

CL1

ch

emok

ine

ligan

d 1

(GR

O a

lpha

) -1

.18

0.37

8 2.

48

0 11

N

M_0

0151

1 ch

r4q2

1 20

4533

_at

CX

CL1

0 ch

emok

ine

ligan

d 10

(IP

10)

-1.0

8 0.

652

2.5

0.01

2 11

N

M_0

0156

5 ch

r4q2

1 20

9774

_x_a

t C

XC

L2

chem

okin

e lig

and

2 (G

RO

-bet

a)

-1.3

4 0.

14

4.68

0

9 M

5773

1 ch

r4q2

1 20

2437

_s_a

t C

YP1B

1 cy

toch

rom

e P

450,

fam

ily 1

, sub

fam

ily B

, pol

ypep

tide

1 1.

13

0.55

4 -2

.06

0 6

NM

_000

104

chr2

p21

2107

64_s

_at

CYR

61

cyst

eine

-ric

h, a

ngio

geni

c in

duce

r, 61

1.

02

0.98

5 1.

710.

005

12A

F003

114

chr1

p31-

p22

2367

07_a

t D

AP

P1

dual

ada

ptor

of p

hosp

hoty

rosi

ne a

nd 3

-pho

spho

inos

itide

s 1.

08

0.81

4 2.

08

0.00

1 11

A

A52

1016

ch

r4q2

5-q2

7 20

9782

_s_a

t D

BP

A

lbum

in D

box

-bin

ding

pro

tein

1.

52

0.01

8 1.

06

0.99

9 10

U

7928

3 ch

r19q

13.3

22

2793

_at

DD

X58

D

EA

D (A

sp-G

lu-A

la-A

sp) b

ox p

olyp

eptid

e 58

-1

.51

0.00

7 -1

.2

0.30

5 7

AK

0236

61

chr9

p12

2332

94_a

t D

EN

ND

2C

DE

NN

/MA

DD

dom

ain

cont

aini

ng 2

C

1.23

0.

742

1.52

0

12

AI2

1445

1 ch

r1p1

3.2

2318

58_x

_at

DK

FZp7

61E

198

NA

1.

36

0.89

8 1.

53

0.02

3 12

B

C00

4895

ch

r11q

13.1

22

5355

_at

DK

FZP

761M

1511

N

A

-1.5

8 0.

013

1.01

0.

999

7 A

K02

6748

ch

r5q3

5.2

88

Chapter 3Fo

ld

chan

geP

valu

eFo

ld

chan

geP

-va

lue

Clu

ster

as

cess

ion

Chr

omos

omal

ID

S

ymbo

ls

Nam

es

alle

rgic

al

lerg

ic

heal

thy

heal

thy

num

ber

num

ber

Loca

tion

2350

85_a

t D

KFZ

p761

P04

23

prag

min

1

0.98

5 1.

83

0.02

4 12

B

F739

767

chr8

p23.

1 20

4720

_s_a

t D

NA

JC6

Dna

J (H

sp40

) hom

olog

, sub

fam

ily C

, mem

ber 6

2.

46

0.00

7 1.

18

0.97

2 10

A

V72

9634

ch

r1pt

er-q

31.3

21

5041

_s_a

t D

OC

K9

dedi

cato

r of c

ytok

ines

is 9

1.

53

0.00

5 1.

06

0.99

9 10

B

E25

9050

ch

r13q

32.3

20

8370

_s_a

t D

SC

R1

Dow

n sy

ndro

me

criti

cal r

egio

n ge

ne 1

-1

.04

0.94

1 1.

55

0.00

8 8

NM

_004

414

chr2

1q22

.1-2

20

1041

_s_a

t D

US

P1

dual

spe

cific

ity p

hosp

hata

se 1

-1

.02

0.94

1 1.

76

0.00

2 8

NM

_004

417

chr5

q34

2094

57_a

t D

US

P5

dual

spe

cific

ity p

hosp

hata

se 5

1.

18

0.76

9 2.

68

0 12

U

1699

6 ch

r10q

25

2088

91_a

t D

US

P6

dual

spe

cific

ity p

hosp

hata

se 6

-1

.42

0.05

5 3.

06

0 12

B

C00

3143

ch

r12q

22-q

23

2228

02_a

t E

DN

1 en

doth

elin

1

-1.1

0.

761

2.7

0 12

J0

5008

--

- 20

2668

_at

EFN

B2

ephr

in-B

2 -1

.2

0.38

5 1.

83

0 11

B

F001

670

chr1

3q33

22

7404

_s_a

t E

GR

1 ea

rly g

row

th re

spon

se 1

-1

.74

0.00

1 3.

23

0 9

AI4

5919

4 ch

r5q3

1.1

2418

43_a

t E

IF5

euka

ryot

ic tr

ansl

atio

n in

itiat

ion

fact

or 5

-1

.06

0.89

4 1.

5 0.

046

8 A

A21

5701

ch

r14q

32.3

2 20

6127

_at

ELK

3 E

TS- r

elat

ed p

rote

in E

RP

1.

26

0.66

7 1.

74

0.01

3 12

N

M_0

0523

0 ch

r12q

23

2195

32_a

t E

LOV

L4

Elo

ngat

ion

of v

ery

long

cha

in fa

tty a

cids

pro

tein

4

1.52

0.

025

1.33

0.

011

10

NM

_022

726

chr6

q14

2138

95_a

t E

MP

1 ep

ithel

ial m

embr

ane

prot

ein

1 1.

32

0.57

1 1.

74

0.03

8 12

B

F445

047

chr1

2p12

.3

2013

41_a

t E

NC

1 ec

tode

rmal

-neu

ral c

orte

x 1.

04

0.97

1.

96

0 12

N

M_0

0363

3 ch

r5q1

2-q1

3.3

2123

36_a

t E

PB

41L1

er

ythr

ocyt

e m

embr

ane

prot

ein

band

4.1

-like

1

-1.7

0.

005

-1.2

7 0.

437

1 A

A91

2711

ch

r20q

11.2

-q12

20

6710

_s_a

t E

PB

41L3

er

ythr

ocyt

e m

embr

ane

prot

ein

band

4.1

-like

3

-1.0

1 0.

967

-1.5

8 0.

017

6 N

M_0

1230

7 ch

r18p

11.3

2 20

3499

_at

EP

HA

2 E

PH

rece

ptor

A2

-1.1

9 0.

387

1.61

0.

001

12

NM

_004

431

chr1

p36

1438

_at

EP

HB

3 E

PH

rece

ptor

B3

1.47

0.

025

1.58

0.

221

12

X75

208

chr3

q21-

qter

20

2454

_s_a

t E

RB

B3

Tyro

sine

kin

ase-

type

cel

l sur

face

rece

ptor

HE

R3

1.35

0.

074

1.5

0.01

6 12

N

M_0

0198

2 ch

r12q

13

2057

67_a

t E

RE

G

epire

gulin

-1

.01

0.86

6 3.

43

0 9

NM

_001

432

chr4

q13.

3 22

4657

_at

ER

RFI

1 E

RB

B re

cept

or fe

edba

ck in

hibi

tor 1

-1

.19

0.39

2.

77

0 9

AL0

3441

7 ch

r1p3

6.12

-36.

33

2135

06_a

t F2

RL1

co

agul

atio

n fa

ctor

II (t

hrom

bin)

rece

ptor

-like

1

1.02

0.

963

2.31

0

12

BE

9653

69

chr5

q13

2217

66_s

_at

FAM

46A

fa

mily

with

seq

uenc

e si

mila

rity

46, m

embe

r A

1.26

0.

162

1.58

0.

009

12

AW

2466

73

chr6

q14

2295

18_a

t FA

M46

B

fam

ily w

ith s

eque

nce

sim

ilarit

y 46

, mem

ber B

1.

65

0.14

6 1.

48

0.03

8 2

AA

5310

23

chr1

p36.

11

2198

95_a

t FA

M70

A

fam

ily w

ith s

eque

nce

sim

ilarit

y 70

, mem

ber A

3.

19

0.04

1 1.

09

0.99

9 4

NM

_017

938

chrX

q24

2284

59_a

t FA

M84

A

fam

ily w

ith s

eque

nce

sim

ilarit

y 84

, mem

ber A

1.

36

0.19

3 1.

87

0.00

1 12

B

F063

776

chr2

p24.

3 22

5864

_at

FAM

84B

fa

mily

with

seq

uenc

e si

mila

rity

84, m

embe

r B

-1.1

6 0.

37

1.69

0.

054

12

AL0

3986

2 ch

r8q2

4.21

21

2374

_at

FEM

1B

fem

-1 b

eta

1.05

0.

981

1.83

0.

004

12

NM

_015

322

chr1

5q22

15

5513

6_at

FG

D6

FYV

E, R

hoG

EF

and

PH

dom

ain

cont

aini

ng 6

-1

.18

0.48

8 1.

53

0.01

1 11

B

C01

3319

ch

r12q

22

2103

10_s

_at

FGF5

fib

robl

ast g

row

th fa

ctor

5

1.02

0.

993

1.51

0

12

AB

0165

17

chr4

q21

89

Chapter 3Fo

ld

chan

geP

valu

eFo

ld

chan

geP

-va

lue

Clu

ster

as

cess

ion

Chr

omos

omal

ID

S

ymbo

ls

Nam

es

alle

rgic

al

lerg

ic

heal

thy

heal

thy

num

ber

num

ber

Loca

tion

2157

04_a

t FL

G

filag

grin

3.

18

0.02

4 -1

.29

0.37

6 3

AL3

5650

4 ch

r1q2

1.3

2199

95_s

_at

FLJ1

3841

N

A

1.3

0.31

7 2.

34

0 2

NM

_024

702

chr1

7q25

.3

2414

55_a

t FL

J900

86

NA

1.

82

0.00

7 1.

07

0.99

9 10

A

W13

5306

--

- 20

6496

_at

FMO

3 fla

vin

cont

aini

ng m

onoo

xyge

nase

3

-1.6

0.

02

-1.1

6 0.

821

1 N

M_0

0689

4 ch

r1q2

3-q2

5 20

9189

_at

FOS

P

roto

-onc

ogen

e pr

otei

n c-

fos

-1.3

3 0.

19

1.82

0

8 B

C00

4490

ch

r14q

24.3

20

4420

_at

FOS

L1

FOS

-like

ant

igen

1

-1.2

4 0.

276

2.96

0.

004

11

BG

2512

66

chr1

1q13

24

2320

_at

FOX

O3A

fo

rkhe

ad b

ox O

3A

1.05

0.

912

1.66

0.

027

12

AI4

3558

6 ch

r6q2

1 21

3056

_at

FRM

D4B

FE

RM

dom

ain

cont

aini

ng 4

B

1.06

0.

997

1.86

0

11

AU

1450

19

chr3

p14.

1 20

3706

_s_a

t FZ

D7

frizz

led

hom

olog

7 (D

roso

phila

) 1.

5 0.

031

1.24

0.

747

10

NM

_003

507

chr2

q33

2071

12_s

_at

GA

B1

GR

B2-

asso

ciat

ed b

indi

ng p

rote

in 1

1.

61

0.03

7 1.

35

0.13

5 10

N

M_0

0203

9 ch

r4q3

1.21

20

4537

_s_a

t G

AB

RE

ga

mm

a-am

inob

utyr

ic a

cid

A re

cept

or, e

psilo

n -1

.08

0.82

4 1.

550.

005

12N

M_0

0496

1 ch

rXq2

8 20

3725

_at

GA

DD

45A

gr

owth

arr

est a

nd D

NA

-dam

age-

indu

cibl

e, a

lpha

-1

.01

0.99

3 1.

64

0.00

4 11

N

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7880

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6251

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218

chr1

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1-q3

1.1

2014

66_s

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jun

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0

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war

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ch

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20

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11

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30

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78

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kalli

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596

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01

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2068

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sity

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prot

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1.

08

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68

0.00

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527

chr1

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0.

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59

chr1

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7099

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1.29

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003

12

AW

2760

78

chr1

1p11

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2251

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0.

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10

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6939

7 ch

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91

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chan

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49

NA

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53

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7 1.

08

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2 4

AA

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50

chr1

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55

chr1

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61

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27

0.64

10

A

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3671

1_at

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chr7

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4777

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0.

002

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999

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56

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41

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2296

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m

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seq

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e 1

1.22

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388

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023

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9758

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FAP

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24

2963

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74

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629

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2 W

7233

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chr1

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roto

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1.81

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001

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NM

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467

chr8

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12-

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13

2097

57_s

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51

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ch

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05

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ch

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2272

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N

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09

0.81

3 1.

62

0.02

8 12

B

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63

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63

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1355

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4967

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AU

1469

83

chr1

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2382

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68

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5528

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1.76

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4939

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87

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2366

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H

1507

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1470

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1.43

0.

897

2 0.

001

12

R97

781

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2393

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4199

ch

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4 0.

269

5 B

F672

169

chr1

2q24

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2340

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55

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t N

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85

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3 --

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CH

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tain

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0.

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821

chr1

1p15

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2172

99_s

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nibr

in

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624

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32

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52

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3733

7 ch

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7535

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3218

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2056

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2099

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645

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032

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12

NM

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622

chr5

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1-q1

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2042

86_s

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PM

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1 P

MA

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02

0 12

N

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1578

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1 1.

07

0.99

9 7

NM

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397

chr7

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q33

2197

56_s

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9 B

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chr1

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981

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2289

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chr6

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3062

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3.51

0

11

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963

chr1

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02_a

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AB

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1.

9 0.

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1.04

0.

999

10

BE

5523

83

chr1

p32-

p31

2394

05_a

t R

AB

7 R

AB

7, m

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632

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3 22

7897

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37

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2092

7 ch

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19

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69

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T8

6196

ch

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6 12

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931

1.76

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12

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chr2

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3180

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1.04

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10

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52

chr1

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2199

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236

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3 15

5345

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42

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01

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364

chr1

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3 20

4803

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2 12

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30

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2097

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39

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5317

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829

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6 N

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15

7048

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979

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340

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944

1.65

0.

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12

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976

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2230

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1 0.

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6 A

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944

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A

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7697

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4490

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5078

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47

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2028

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0.55

3 1

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113

chr2

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1 20

9761

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0.28

2 1

AA

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94

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1 15

5454

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1.76

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24

chr1

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2051

85_a

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r, K

azal

type

5

1.63

0.

002

1.2

0.99

7 3

NM

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846

chr5

q32

1553

973_

a_at

S

PIN

K6

serin

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6

2.01

0.

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1 0.

94

3 B

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2003

ch

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3720

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7 se

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21

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17

0.99

5 3

AF2

6819

8 ch

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3.1

2206

64_a

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all p

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h pr

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1.

64

0.04

5 1.

47

0.76

4

NM

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518

chr1

q21-

q22

2125

58_a

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PR

Y1

Spr

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hom

olog

1

1.13

0.

554

1.55

0.

001

12

BF5

0866

2 ch

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2040

11_a

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PR

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98

99

4Comparison of expression

profiles induced by dust mite in airway epithelia reveals a

common pathway.

Aram B. Vrolinga, Martijs J. Jonkerb, Timo M. Breitb, Wytske J. Fokkensa, Cornelis M. van Drunena

a Department of Otorhinolaryngology, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands

b Integrative Bioinformatics Unit, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands

Allergy. 2008 Apr;63(4):461-7.

100

Chapter 4

Abstract

Airway epithelial cells have shown to be active participants in the defense against pathogens by producing signaling and other regulatory molecules in response to the encounter. In previous manuscripts we have studied the effect of house dust mite extract on both an epithelial cell line (H292) and primary nasal epithelial cells. When we compare these responses we conclude that the H292 cells more closely resemble nasal epithelium of healthy controls (share 107 probe sets) than of allergic individuals (share 17 probe sets). Interestingly, probably because of an absent intra-individual variation between samples, more probe sets (8280) change expression significantly in H292, than in either healthy (555) or allergic (401) epithelium. A direct comparison of all the responses in these epithelial cells reveals a core-response to house dust mite of just 29 genes. Most of these genes (MIP-3α, IL-8, GRO-β, GRO-α, IL-1β, AREG, TNF-αIP3, HBEGF, PTGS-2, BMP-2, LDLR, PLAUR, PLAU, NFKB2, NFKB1, JUN, ATF3-, EGR-1, NPC-1, TICAM-1, EPHA-2, CTGF, DUSP-1, SPRY-1, TLR-3, complement factor C3, IVNS1ABP, Serpin-B3, PSAT1) have described links with allergy or inflammation and could even play a role in the relationship between viral infections and allergic exacerbations or allergy development.

101

Chapter 4

Introduction

Airway epithelial cells have shown to be active participants in the defense against pathogens by producing signaling and other regulatory molecules in response 1;2. In this way airway epithelium contributes to the tissue microenvironment where cells of the immune system reside and are activated as part of the defense mechanism. A detailed analysis on the protein and mRNA level of such a response in primary nasal epithelium and in a bronchial cell line 3;4 by our group has identified a very diverse and seemingly well coordinated response. In our experiments we have investigated the effect of house dust mite (HDM) extract on primary nasal epithelial cells and a bronchial epithelial cell line.

We detected a complex, but defined network was that showed the involvement of epithelial produced and secreted TNF-α both in primary nasal epithelial cells and in a bronchial epithelial cell line. More recently it has been shown that activation of protease activated receptor (which can occur by proteolytic allergens) promotes chicken egg ovalbumine sensitization in BALB/c mice, and that this process can be abrogated by adding a TNF-α blocking antibody 5. In the analysis of the primary nasal epithelial cells and their response to allergen we showed an activated state in the epithelial cells of the allergic individuals. This activated state is reflected in a limited number of genes in allergic epithelium that at baseline already display a high level of expression that, in primary epithelium from healthy individuals, is only reached after house dust mite (HDM) extract exposure. Moreover we saw increased expression of two transcription factors (NF-κB and AP-1) and their regulatory members in healthy epithelium upon allergen exposure. Most striking is that, while the already activated NF-κB regulatory pathway remained unchanged in allergic epithelium, the AP-1 pathway is down-regulated upon exposure to HDM allergen. This could indicate a protective effect by expression of AP-1 since it has been described to bind to NF-κB binding site, thereby inhibiting

102

Chapter 4

NF-kB regulated transcription. With availability of additional data of the effect of HDM extract on a

bronchial epithelial cell line, a more detailed analysis of this response in airway epithelia can be achieved. In this manuscript we would like to explore 1 the pros and cons of using a cell line as a surrogate model for primary epithelium, (2) the similarities and dissimilarities in the HDM response of epithelial cells from a nasal or bronchial mucosal origin, and (3) further define the HDM response in order to try and identify new targets for development of medication in allergy and/or asthma.

Material & Methods

Datasets.The datasets we used for this comparison were taken from two

manuscripts that were previously published; one describing the response of H292 carcinoma cell line to house dust mite allergen 3 and one describing the response of primary cells of allergic and healthy individuals to house dust mite allergen 4. For detailed information on cell culture protocols, experimental conditions, RNA extraction methods, and microarray data validation please refer to these articles. The complete dataset from these experiments is accessible through the NCBI Gene Expression Omnibus repository (http://www.ncbi.nlm.nih.gov/geo), series accession number GSE9150 and GSE9151

Experimental procedures.Primary cells were obtained from nasal biopsies of volunteers after

getting their informed consent in a locally approved study 4. The epithelial cell line NCI-H292 was received as a gift from the Department of Pulmonology, Academic Medical Center, Amsterdam, The Netherlands. Cells were grown to 80% confluence in a 6 wells plate in their respective culture medium; BEGM (Lonza Clonetics, Breda, the Netherlands) or RPMI 1640 medium (Invitrogen,

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Breda, The Netherlands) supplemented with 1.25 mM L-glutamine, 100 U/mL penicillin, 100 µg/mL streptomycin and 10% (v/v) fetal bovine serum (HyClone, Logan, UT, USA). Before stimulation experiment, cells were pre-incubated with Hanks’ balanced salt solution (HBSS) for 24 hours. Culture medium was removed and cells were then stimulated with house dust mite extract diluted in HBSS (2 μg/mL) or with HBSS alone (control condition) for 24 hours. Supernatants were removed and stored for further analysis; cells were used for RNA extraction.

Data analysis.Expression values were calculated using the robust multi-array average

(RMA) algorithm 6, and statistically analyzed for differential gene expression using ANOVA (MAANOVA package, version 0.98.8 7). The permutation based Fs test was used for hypothesis testing 8, and all p-values were adjusted for false discovery rate correction 9. In order to quantify the effect of HDM extract on gene expression, pairwise statistical tests were performed to analyze: 1) the effect of HDM on epithelial cells from healthy individuals separately, 2) the effect of HDM on epithelial cells from allergic individuals separately, and 3) the effect of HDM on H292 epithelial cells.

A Gene Set Enrichment Analysis strategy was used to determine if the changes from baseline in the primary epithelial data set was similar to the changes in H292 cells. H292 data was first RMA normalized and for every probe set in the H292 data a t-statistic was calculated using an empirical Bayes’ t-test. This t-test is an indication of how different a probe set is between HDM-stimulated and unstimulated. Then the H292 data was ranked by t-statistic values. Subsequently we calculated the ‘enrichment statistics’ of four defined primary gene sets (up- or down-regulated in epithelium from allergic or healthy individuals) in the H292 data set. When the normalized enrichment score (NES) is positive then the primary epithelium gene set is mainly up-regulated, if negative it is mainly down-regulated in H292. We calculated the statistical significance levels of these enrichments using a gene-permutation strategy.

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Results

Comparing H292, primary allergic and primary healthy epithelium on whole array level.

The first impression on the similarities (or dissimilarities) was obtained using a correlation analysis on the complete dataset from each of the individual microarray chips. Overall the correlation coefficients between the individual arrays are high (R>0.875), independent of whether the absolute signal levels (Pearson correlation in Figure 1A) or the relative signal levels (Spearman rank correlation in Figure 1B) of the arrays are used for analysis. Detailed study of figure 1 does show however that all arrays within a certain class (primary healthy, primary allergic, H292) resemble each other more closely than they resemble arrays of the two other classes. Furthermore, primary epithelium of allergic or healthy individuals is more similar to each other than either of them is to the H292 cell line.

Figure 1: Correlation analysis of the expression profiles. We used Pearson’s correlation on the absolute signal levels (A), or Spearman rank correlation of the relative rank of the signal levels (B). The individual arrays are labeled to indicate their origin (A = primary allergic, C = primary healthy, H = H292), the replicate number (1-5 for Allergic, 1-3 for H292, and 2,3 + 7-9 for Healthy), and their exposure to house dust mite extract (Sal = vehicle only (saline), HDM = HDM extract).

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Comparing allergic and healthy epithelium to H292 based on the effect of HDM extract.

Although correlation analysis gives some clues about relationships on a global level, it is not well suited to investigate the individual responses of genes to house dust mite extract. Using a factorial design with multiple testing corrections, we identified 8995 probesets that are significantly different between vehicle and HDM extract conditions, in at least one of three classes (primary healthy, primary allergic, H292).

The Venn diagram (figure 2) shows for this collection of probesets the similarity in their responses among the different groups. Several conclusions can be drawn. Firstly, there is only a limited overlap in the response to HDM with just 1 of 8995 probesets showing a similar response in all three groups and 140 probesets showing a similar response shared between two of the three groups. Secondly, the response to HDM is far more extensive in H292 (8279 probesets) than in primary healthy (555 probesets) or allergic (301 probesets) epithelium. Thirdly, the response in H292 is more similar to the response in primary healthy epithelium than to the response in allergic epithelium. Also, the Venn diagram shows, as we have reported before 4, that there is hardly any overlap of similar behavior (9 probesets) between the effects of HDM on primary epithelium from healthy and allergic individuals.

Figure 2: Venn diagram showing statistically significant genes. Statis-tically up-regulated genes (red), and statistically down-regulated genes (green). Uniquely differentially regu-lated genes are shown outside the overlapping areas, if genes are up regulated in more than one group they are in the overlapping areas of the circles.

The data from the expression profiles of the individual genes shows that the response induced by HDM in the bronchial cell line H292 is more similar to primary healthy epithelium (overlap of 106 probesets) than to the

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response in allergic epithelium (overlap of 26 probesets). To further explore this observation we used a Gene Set Enrichment Analysis (GSEA) based approach 10. Four gene sets were created; statistically down-regulated in allergic (173 genes), statistically up-regulated in allergic (128 genes), statistically down-regulated in healthy (153 genes), statistically up-regulated in healthy (402 genes), and compared these gene sets to the expression profiles in H292. Table 1 summarizes the GSEA-analysis and shows that primary epithelial from healthy individuals behave as expected. Up-regulated genes of the primary cells are enriched in the up-regulated (NES = + 2.961, P = 0.000) gene set of H292, whereas the down-regulated genes are enriched in the down-regulated (NES = + 2.007, P = 0.000) gene set of H292. The situation for the gene sets in allergic epithelium is strikingly different. Now both up-regulated (NES = + 1.296, P = 0.068) and down-regulated genes (NES = +1.900, P = 0.000) are both enriched in the up-regulated gene set of H292. Moreover, significance level of the comparison between up-regulated allergic versus up-regulated H292 (P = 0.068) is far below the significance of up-regulated healthy versus up-regulated H292 (P = 0.000), confirming that the H292 response to HDM extract is more similar to the response in healthy primary epithelium than to the response in allergic epithelium.

Gene Set Gene pool size NES Nominal p-valAllergic down-regulated 173 + 1.900 0.000Allergic up-regulated 128 + 1.296 0.068Healthy down-regulated 153 - 2.007 0.000Healthy up-regulated 402 + 2.961 0.000

Table 1: Gene Set Enrichment Analysis. RMA normalized data of H292 was compared to defined probe sets from primary epithelium according to methods described in the Material and Methods section. Positive value for NES indicates that this gene set of primary epithelium is predominantly up-regulated in H292, whereas negative values indicate that the dataset is predominantly down-regulated in H292. Nominal significance levels are indicated. (NES, normalized enrichment score).

Identification of a core response to HDM extract in airway epithelium.In our earlier studies, we have used genes from primary epithelium that

change their expression in response to HDM extract to form a regulatory

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network. When we map these genes in the H292 data set (See Supplementary Table 1 A-D) we find that some of these genes are affected in a similar fashion, suggestive of a core response to HDM extract in airway epithelium. Four of these genes have shared response between H292 and allergic epithelium; two are up-regulated (SerpinB3 and PSAT1) and two are down-regulated (complement factor C3 and IVNS1ABP). The comparison between H292 and primary epithelium from healthy individuals reveals a larger overlap with 24 genes that are up-regulated in both groups (MIP-3α, IL-8, GRO-α GRO-β IL-1β , AREG, TNF-αIP3, HBEGF, PTGS-2, BMP-2, LDLR, PLAUR, PLAU, NFKB2, NFKB1, JUN, ATF-3, EGR-1, NPC-1, TICAM-1, EPHA-2, CTGF, DUSP-1, SPRY-1) and one gene that is down-regulated in both groups (TLR-3). In order to see how the genes that show overlap between healthy controls, allergic and H292 interact we used a network analysis. In our network we look at direct interaction, and filter out the interactions that are not of a regulatory type. We are then left with a network that contains 24 genes (of the 29 unique genes that show overlap) (see figure 3).

Figure 3: Network analysis of overlapping genes. An interaction network was built using the genes that are regulated in primary epithelium of allergic and healthy that show overlap with the genes that are affected in H292 cells. 24 of the 29 genes are involved in this network.

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Comparing H292, primary allergic, and primary healthy epithelium on the mediator level.

The response of different cell types to distinct stimuli is frequently evaluated by analyzing expression profiles of secreted mediators. These mediators are assigned to specific functions or characteristic responses. Not only can secreted chemokines recruit immunocompetent cells to local tissues, but cytokines or growth factors can affect the functions of these cells. In order to evaluate the response of HDM extract on epithelial cells at this mediator level we examined this class of molecules in more detail.

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Figure 4: Comparison of the change in expression for so-luble mediators. Expression levels of 147 probe sets that are assigned to cytokines or chemokines are plotted between H292 and primary allergic epithelium (A), or H292 and primary healthy epithelium (B). What is shown is that a poor negative corre-lation exists between H292 and allergic, and a positive correlation between H292 and healthy. The labels for the in-dicated probe sets are: IL-8 (numbers 1 and 2), MIP-3α (number 3), GRO-β (number 4), IL-1α (number 5), GRO-α (number 6), IP-10 (number 7), IL-1β (numbers 8 and 9), GRO-γ (number 10), IL-6 (number 11), and IL-11 (num-ber 12).

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In total we find of the 147 probe sets that are ascribed to cytokines and chemokines of which 32 change their expression significantly in H292, 10 probe sets are significantly altered in primary healthy epithelium, and only 1 probe set altered in allergic epithelium. This single probe set in allergic epithelium is specific for IL-8. Whereas in allergic epithelium IL-8 is down-regulated, it is up-regulated both in H292 and primary epithelium from healthy individuals (see figure 4A). Of the 10 probe sets that display an altered expression due to HDM extract exposure in healthy epithelium, we find 8 that are also altered in H292. More relevant than just investigating the statistical significance of the HDM-affected genes is to also consider the fold change of these probe sets between primary epithelium and H292. Figure 4B shows again that only for the comparison between the mediators of H292 and primary healthy epithelium there is a clear positive correlation, albeit that on the fold changes in H292 are clearly higher than in healthy primary epithelium.

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DIscussion

In this article we have presented a detailed analysis of the similarities and differences of the effect of HDM extract on the expression profiles in airway epithelial cells. Given the diverse background of the epithelia tested (cell line versus primary cells, allergic versus healthy, bronchial origin versus nasal origin) some relevant conclusions can be drawn. These conclusions will have implications for our understanding of the advantages of using a cell line as a model and may even help to understand the contribution of airway epithelia to the allergic response.

The whole array analysis has shown a high level (80.1%) of correlation (R > 0.895) between all individual arrays, independent of their origin. Given that this analysis is the summation of approximately 55,000 probe sets and that all cells tested are of epithelial origin this might not come as a surprise. However, this does not imply that despite the ability of cell lines to respond to protease activity, H292 would be the perfect model for primary nasal epithelium 11. When we investigate the response of each epithelium to house dust mite extract on the macro level a different picture emerges. Now only a very limited number of genes behave in a similar fashion in all three epithelia. This is not surprising. Since our previous analysis of the response to HDM extract in primary epithelium obtained from healthy and allergic individuals had already revealed a limited overlap between these two cell types. Inclusion of a third factor (H292) in this analysis will only serve to reduce the number of similarities. When we investigate the response of H292 to HDM we observe that both the fold-changes are more pronounced and that the number of genes affected is higher than in primary epithelium. The latter observation might well be related to the limited variation that can be expected in a triplicate H292 experiment. The experimental variation in a cell line is likely to be significantly less than the variation in primary epithelia, where intra-individual differences will result in a reduced statistical power to detect differences.

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In this respect a cell line is a clear bonus, as also small differences can be detected, differences that to the process under investigation can be as important as bigger differences. The more pronounced fold-changes induced by HDM in H292 could be related to the fact it is a cell line, or its bronchial nature, however the precise reason remains unclear. For instance, the response to Lipopolysaccharide is also stronger in bronchial epithelia than in nasal epithelia (data not shown). As nasal epithelia are more likely to come in contact to environmental triggers, a mechanism seems to be in place that partly protects nasal epithelia from eliciting a too strong response. Despite the observation that the response in H292 is more similar to the response in healthy primary epithelium, these considerations lead to the conclusion that on the macro level the response in H292 can not be used as a detailed model of the response of primary nasal epithelium to HDM extract.

Even though H292 can not be used as a true model for the reason explained above, our detailed analysis of the similarities between the response in H292 and primary epithelium provided clues towards a better understanding of the HDM response per se and how this response can contribute to allergy. Just 29 genes of the regulatory network we had identified in primary epithelium respond in H292 in an identical fashion. Nearly all (24/29) genes show only 1 of 2 distinct expression patterns that we first had identified in the transcription factors of the primary epithelium network. One class is characterized by high expression in non-HDM exposed allergic samples that does not further increase after HDM exposure, whereas these same transcription factors have low expression in non-HDM exposed healthy samples and are up-regulated by HDM. Genes with this expression pattern are: IL-8, IL-1β, GRO-γ, TNF-αIP3, HBEGF, PTGS-2, BMP-2, PLAUR, PLAU, NFKB2, NFKB1, NPC-1, TICAM-1, EPHA-2, CTGF, and SPRY-1. The second class similar to the first class, but now the expression levels in allergic epithelium are relatively low (and remain unaffected by HDM exposure), while they still go up in healthy epithelium. Genes with this expression pattern are: MIP-3α, GRO-β , AREG,

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LDLR2, JUN, ATF-3, EGR-1, and DUSP-1. The 4 genes (Serpin-B3, PSAT-1, complement factor C3, and IVNS1ABP) that have a shared expression between H292 and primary allergic epithelium can not be clustered in a similar fashion.

Previously we indicated an aberrant interaction between the NF-kappaB and AP-1 transcription families that could be responsible for the activated state in primary epithelium from allergic individuals. Now we show, not only that four of these transcription factors (NFKB2, NFKB1, JUN, and ATF-3) can be found to respond in H292, but also that other genes with this type of response are conserved between H292 and primary epithelium. Some cytokines, chemokines, or growth factors of the core-response have been well described as classical players in allergy or inflammation; IL-1β, IL-8, MIP-3α, GRO-β, GRO-γ, CTGF, HBEGF, AREG 12-19. Also some relative new players in the allergy field are related to the regulation of critical signaling effects; C3, SERPIN-B3, DUSP-1, SPRY-1, EPHA-2, PLAU, and PLAUR 20-

27. Moreover, three genes; TLR-3, TICAM-1, IVNS1ABP 28-30 can be directly linked to the defense against viral infections. This is highly relevant given the above mentioned dual relationship between allergy and viral infection. Early viral infections in life have been linked to the protection against the later development of allergic disease and conversely concurrent viral infections have been linked to the exacerbation of allergic and asthmatic symptoms 31. TICAM-1 (toll-like receptor adaptor molecule 1, also known as TRIF) is an essential part of the TLR-3 mediated signaling cascade that is activated through viral PAMPs, leading to an antiviral response and the production of IFNs 32. IVNS1ABP (Influenza virus NS1A binding protein) is the potential target of the viral protein NS1A through which Influenza virus interferes with the RNA metabolism of the infected cell 30. More debatable could be the link between allergy and obesity through LDLR or NPC-1, given that both these proteins can be linked to lipid metabolism 33;34. Also here more genes that can be linked to nutrition, like the SLC-class of amino acid transporters, are part

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of the larger regulatory network in primary epithelium.Many studies have focused on individual genes or proteins in relation

to allergy or inflammation. Although this approach is valid it does not take into account the complex nature of the immunological response. For some genes (IL-1β or IL-8) it is not clear whether these genes are typical for the allergic response in epithelium. More likely these genes correspond to a strong common response triggered by environmental factors. Therefore one should be careful to draw general conclusions based on relative few outcome parameters. Our analysis of the house dust mite response in epithelia shows that this is also true in a narrowly defined experimental system. Most importantly, the HDM response in three different epithelia reveals the involvement of a limited number of genes. Moreover the activated state of the allergic epithelium itself influences the ability of allergic epithelium to respond to HDM extract and can even result in a reduction in the expression level of genes that are highly expressed at baseline. These genes are involved in the regulation in signaling events inside the cell (through receptors, phosphatases, and adaptor molecules), the regulation of down-stream events (through cytokines, chemokines, and growth factors), and regulation of these factors (through transcription factors). Not only do these data show the active and well defined involvement of airway epithelia in the allergic response, but also identify potent new targets for treatment.

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Supplementary table 1: Genes that are in the Venn diagram and show statistical change in two groups with a similar direction of change. 1A) genes up-regulated both in allergic and H292. 1B) genes that are down-regulated both in allergic and H292. 1C) genes that are up-regulated in healthy and H292. 1D) genes that are down-regulated in healthy and H292. In bold are indicated the genes that are also in the network analysis (39) of allergic and healthy controls.

Supplementary table 1A

ID Symbols Names FC all fc h292

209720_s_at SERPINB3 serpin peptidase inhibitor, clade B, mem 3 1.67 2.47223062_s_at PSAT1 phosphoserine aminotransferase 1 2.12 2.10209719_x_at SERPINB3 serpin peptidase inhibitor, clade B, mem 3 1.62 1.88224209_s_at GDA guanine deaminase 1.95 2.72

209908_s_at TGFB2 transforming growth factor, beta 2 1.47 2.51

242722_at LMO7 LIM domain 7 1.29 2.50

228181_at SLC30A1 solute carrier family 30, member 1 1.29 1.58

207059_at PAX9 paired box gene 9 1.58 1.52

236067_at MBNL2 muscleblind-like 2 (Drosophila) 1.30 1.46

242204_at WFDC5 WAP four-disulfide core domain 5 2.65 1.30

1569206_at TCP11L2 t-complex 11 (mouse) like 2 1.38 1.16

232810_at AIG1 androgen-induced 1 1.29 1.16

225105_at LOC387882 NA 1.51 1.09

Supplementary table 1B

ID Symbols Names FC all fc h292

217767_at C3 complement component 3 -1.62 -1.12201362_at IVNS1ABP influenza virus NS1A binding protein -1.50 -1.40226796_at LOC116236 NA -1.30 -1.09

221534_at Bles03 NA -1.15 -1.20

225240_s_at MSI2 musashi homolog 2 (Drosophila) -1.33 -1.35

205573_s_at SNX7 sorting nexin 7 -1.44 -1.46

244317_at KIAA1324L KIAA1324-like -1.23 -1.50

226910_at COMMD2 COMM domain containing 2 -1.43 -1.53

217853_at TNS3 tensin 3 -1.51 -1.78

219013_at GALNT11 N-acetylgalactosaminyltransferase 11 -1.33 -2.00

212336_at EPB41L1 erythrocyte membrane protein band 4.1-like 1 -1.70 -2.11

205623_at ALDH3A1 aldehyde dehydrogenase 3 family, memberA1 -1.50 -3.98

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Supplementary table 1C

ID Symbols Names fc con fc h292

205476_at CCL20 chemokine (C-C motif) ligand 20 (MIP3alpha) 4.88 90.92202859_x_at IL8 interleukin 8 6.56 61.16209774_x_at CXCL2 chemokine (C-X-C motif) ligand 2 (GRObeta) 4.68 46.03204470_at CXCL1 chemokine (C-X-C motif) ligand 1 (GROalpha) 2.48 23.32205239_at AREG amphiregulin 1.64 20.84202643_s_at TNFAIP3 tumor necrosis factor, α-induced protein 3 3.36 12.28203821_at HBEGF heparin-binding EGF-like growth factor 2.98 9.851557285_at AREG amphiregulin 1.85 7.7538037_at HBEGF heparin-binding EGF-like growth factor 2.61 6.771554997_at PTGS2 prostaglandin-endoperoxide synthase 2 4.09 6.6639402_at IL1B interleukin 1, beta 2.12 6.09205067_at IL1B interleukin 1, beta 2.27 3.25202068_s_at LDLR low density lipoprotein receptor 1.68 2.82210845_s_at PLAUR plasminogen activator, urokinase receptor 8.34 2.81207535_s_at NFKB2 NFKB2 (p100) 2.53 2.72202679_at NPC1 Niemann-Pick disease, type C1 1.30 2.48205289_at BMP2 bone morphogenetic protein 2 2.81 2.46205290_s_at BMP2 bone morphogenetic protein 2 2.57 2.37239876_at NFKB1 NFKB1 (p105) 2.27 2.04211668_s_at PLAU plasminogen activator, urokinase 2.16 1.98213191_at TICAM1 toll-like receptor adaptor molecule 1 1.95 1.97203499_at EPHA2 EPH receptor A2 1.61 1.88217584_at NPC1 Niemann-Pick disease, type C1 1.62 1.80201464_x_at JUN v-jun sarcoma virus 17 oncogene homolog 1.52 1.73209101_at CTGF connective tissue growth factor 2.81 1.51201044_x_at DUSP1 dual specificity phosphatase 1 1.71 1.45205479_s_at PLAU plasminogen activator, urokinase 1.91 1.42212558_at SPRY1 sprouty homolog 1, antagonist of FGF 1.55 1.321554980_at ATF3 activating transcription factor 3 3.80 1.28211506_s_at IL8 interleukin 8 7.23 26.66227404_s_at EGR1 early growth response 1 3.23 2.23201694_s_at EGR1 early growth response 1 2.71 1.57221841_s_at KLF4 Kruppel-like factor 4 (gut) 1.52 11.12

204622_x_at NR4A2 nuclear receptor subfamily 4, group A, member 2 1.81 4.29

36711_at MAFF v-maf oncogene homolog F 2.02 4.08

217997_at PHLDA1 pleckstrin homology-like domain, fam A, mem 1 1.44 3.97

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209457_at DUSP5 dual specificity phosphatase 5 2.68 3.32

204621_s_at NR4A2 nuclear receptor subfamily 4, group A, member 2 1.94 3.07

239331_at NA NA 2.05 2.99

229242_at NA NA 1.43 2.86

233488_at RNASE7 ribonuclease, RNase A family, 7 1.59 2.66

219496_at C2orf26 chromosome 2 open reading frame 26 1.59 2.52

221765_at UGCG UDP-glucose ceramide glucosyltransferase 1.41 2.40

213506_at F2RL1 coagulation factor II (thrombin) receptor-like 1 2.31 2.29

204011_at SPRY2 sprouty homolog 2 (Drosophila) 1.59 2.29

240861_at NA NA 1.63 2.28

231697_s_at TMEM49 transmembrane protein 49 1.59 2.18

203313_s_at TGIF TGFB-induced factor (TALE family homeobox) 1.92 1.95

225756_at CSNK1E casein kinase 1, epsilon 1.19 1.94

224692_at PPP1R15B protein phosphatase 1, regulatory subunit 15B 1.36 1.91

230503_at NA NA 2.29 1.91

225582_at KIAA1754 KIAA1754 1.49 1.80

242294_at NA NA 1.33 1.79

210260_s_at TNFAIP8 tumor necrosis factor, alpha-induced protein 8 1.81 1.70

225557_at AXUD1 AXIN1 up-regulated 1 1.99 1.68

235165_at PARD6B par-6 partitioning defective 6 homolog beta 2.11 1.65

209493_at PDZD2 PDZ domain containing 2 1.58 1.61

221840_at PTPRE protein tyrosine phosphatase, receptor type, E 1.49 1.60

32069_at N4BP1 NA 1.16 1.57

1566901_at TGIF TGFB-induced factor (TALE family homeobox) 2.69 1.55

240991_at LOC392271 NA 2.05 1.53

1558783_at WTAP Wilms tumor 1 associated protein 2.26 1.53

209535_s_at NA NA 1.40 1.52

1554783_at ARHGEF2 rho/rac guanine nucleotide exchange factor 2 1.23 1.48

1555411_at CCNL1 cyclin L1 1.88 1.47

224606_at KLF6 Kruppel-like factor 6 2.14 1.44

208033_s_at ATBF1 AT-binding transcription factor 1 1.22 1.42

207439_s_at SLC35A2 solute carrier family 35, member A2 1.31 1.41

227099_s_at LOC387763 NA 1.66 1.39

225991_at TMEM41A transmembrane protein 41A 1.47 1.39

203463_s_at EPN2 epsin 2 1.14 1.38

219334_s_at OBFC2A oligonucleotide -binding fold containing 2A 1.33 1.38

1556220_at NA NA 1.50 1.37

211962_s_at ZFP36L1 zinc finger protein 36, C3H type-like 1 2.32 1.36

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1557810_at CCT5 chaperonin containing TCP1, subunit 5 (epsilon) 1.48 1.32

214629_x_at RTN4 reticulon 4 1.13 1.29

203068_at KLHL21 kelch-like 21 (Drosophila) 1.53 1.28

225648_at STK35 serine/threonine kinase 35 1.20 1.27

229396_at OVOL1 ovo-like 1(Drosophila) 3.69 1.26

235037_at TMEM41A transmembrane protein 41A 1.39 1.25

203002_at AMOTL2 angiomotin like 2 1.92 1.25

215528_at NA NA 1.76 1.23

227489_at CCDC45 coiled-coil domain containing 45 1.36 1.20

203739_at ZNF217 zinc finger protein 217 1.61 1.19

201328_at ETS2 erythroblastosis virus oncogene homolog 2 1.44 1.14

1557394_at DLGAP4 discs, large homolog-associated protein 4 1.35 1.12

224624_at LRRC8A leucine rich repeat containing 8 family, member A 1.34 1.11

Supplementary table 1D

ID Symbols Names fc con fc h292

206271_at TLR3 toll-like receptor 3 -1.51 -1.75226243_at LOC391356 NA -1.27 -1.14

223213_s_at ZHX1 zinc fingers and homeoboxes 1 -1.27 -1.14

204784_s_at MLF1 myeloid leukemia factor 1 -1.37 -1.20

226151_x_at CRYZL1 crystallin, zeta (quinone reductase)-like 1 -1.16 -1.23

218048_at COMMD3 COMM domain containing 3 -1.19 -1.23

229353_s_at NUCKS1 nuclear cyclin-dependent kinase substrate 1 -1.31 -1.29

227925_at FLJ39051 NA -1.49 -1.29

223442_at NICN1 nicolin 1 -1.18 -1.34

203714_s_at TBCE tubulin-specific chaperone e -1.21 -1.39

202735_at EBP emopamil binding protein (sterol isomerase) -1.25 -1.40

219962_at ACE2 angiotensin I converting enzyme 2 -1.60 -1.52

215016_x_at DST dystonin -1.41 -1.81

242093_at NA NA -1.35 -2.02

202436_s_at CYP1B1 cytochrome P450, fam 1, subfam B, polypep 1 -1.89 -2.14

211361_s_at SERPINB13 serpin peptidase inhibitor, clade B member 13 -1.23 -2.27

221521_s_at Pfs2 NA -1.39 -3.28

209917_s_at TP53AP1 TP53 activated protein 1 -1.25 -3.60

204734_at KRT15 keratin 15 -1.18 -3.79

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5Epithelial cells show a

pleiotrope mediator response as a consequence of cell-cell

contact disruption.

Aram B. Vroling, Dirk Duinsbergen, Wytske J. Fokkens, Cornelis M. van Drunen

Department of Otorhinolaryngology, Academic Medical Center, University of Amsterdam , Amsterdam, The Netherlands

submitted

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Abstract

Airway epithelial cells are active participants in the reaction to environmental factors by producing signalling molecules, which influence the micro environment. In this research we investigated the epithelial response to disruption of cell-cell contact, in the presence or absence of trypsin. Airway epithelial cells were dislodged from the cultureplate using a non-enzymatic method or using trypsin. Dissociation alone causes H292 cells to produce a variety of mediators (EGF, G-CSF, GRO, IGF-BP3, IL-1β, IL-6, IL-8, IFN-γ, LIF, TGF-β2, TIMP-1, TIMP-2, and TNF-α). When this dissociation takes place in the presence of trypsin these mediators are produced in higher amounts. In addition cells produce MIP1-β, TGF-β1, IP10, FGF4, and FGF-9. For 18 out of 23 genes we could confirm mRNA expression. Although these mediators were known to be produced by epithelial cells under various conditions, we show for the first time a combined expression of these mediators in a single model and that the response of epithelial cells is broader when disruption of cell cell contacts takes place in the presence of trypsin. This data highlights the role of epithelial cells in the mucosal defense system.

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Introduction

Airway epithelial cells form the layer that separates the underlying mucosal tissue from the environment. Their function, besides being a physical barrier, lies in production of mediators that have antimicrobial function, and also in production of cytokines or chemokines that activate and attract immune competent cells 1. Epithelial cells do this in response to a wide variety of environmental factors, such as exposure to chemical components like cigarette smoke or diesel exhaust particles, exposure to biological components like viruses, allergens, but also to physical changes such as wounding 2-7.

One of the mechanisms by which epithelial cells can be activated is via protease activated receptor (PAR). This innate receptor, which plays a role in blood clotting, can be activated by endogenous proteases, as well as by proteases that are found in many allergens 8-10. One of the results of the exposure to these proteases is that the tight junctions between epithelial cells are cleaved, causing the cells to detach from their culture substrate in vitro, and disruption of epithelial integrity and even epithelial shedding in vivo 11.

Disruption of cell cell contacts leads to a proinflammatory response characterized by the production of IL-1β, IL-6, IL-8, and GM-CSF 3;12 and recruitment (IL-8, RANTES, eotaxin, and TARC) 13-16, or activation (IL-6 and GM-CSF) 12;17. Through these factors epithelial cells can contribute to disease. For instance, the postulated interaction between epithelial cells and the mesenchymal unit is thought to actively contribute to progression of asthma 17.

In previous experiments with house dust mite exposure to primary airway epithelial cells we observed both mediator induction as well as up-regulation of genes involved in tight junctions formation 18. These findings gave rise to the question whether these processes occur in parallel or whether these

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processes are related? With the experiments described in this manuscript, we aimed to get a comprehensive overview of the airway epithelial response on the disruption of the epithelial integrity, and to try to discriminate between the relative contribution of the intrinsic activation as a consequence of the disruption os cell cell contacts and the contribution through the protease induced activation. To address these questions, we determined the protein production of over 80 distinct mediators by airway epithelium, in response to disruption of cell cell contacts and PAR activation. Additionally, we investigated 23 mediators on mRNA level to confirm their protein expression data.

Materials and methods

Cell culture.NCI-H292 human airway epithelial cells (American Type Culture Collection,

Manassas, VA, USA) were cultured in RPMI 1640 medium (Invitrogen, Breda, The Netherlands) supplemented with 1.25 mM L-glutamine, 100 U/mL penicillin, 100 µg/mL streptomycin and 10% (v/v) fetal bovine serum (HyClone, Logan, USA). Cells were grown in fully humidified air containing 5% CO2 at 37°C and were subcultured weekly.

Dissociation of cells.Cells were dislodged from fully confluent flasks by the following

procedure; cell were washed three times with PBS, to remove culture media and subsequently, cells were incubated with cell dissociation solution (CDS) (Sigma C-5914, Zwijndrecht, The Netherlands) or with trypsin (Gibco 15400-054, Invitrogen, Breda, The Netherlands) for 15 min at 37°C. Cells were dislodged by tapping the flask. Upon dislodging the cell suspension was examined under a microscope to ensure all cells had detached from the flask and were in a single cell suspension. To remove all trypsin or cell dissociation solution, cells were washed two times with PBS and centrifuged between washings. After washing, cells were resuspended in RPMI1640

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basal medium, and seeded in 0.5 mL at 50,000 cells per well of a 24 wells-plate.

Luminex Bio-Plex assay.Dissociated cells were seeded in a 24 wells plate in serum-free culture

medium; supernatant was removed every 24 hours for four days, to determine the 24 hour production of cytokines. Cytokine levels in supernatant of cells were determined using a Bio-Plex human cytokine 17 plex panel kit (Bio-Rad, Veenendaal, The Netherlands) which was analyzed on the Bio-Plex workstation (Bio-Rad). All standards were diluted in the same serum free culture medium where the cells were seeded in after dissociation. Concentrations were calculated from a dilution series of standards using the Luminex software, and the stable expression level at 96 hours was taken as baseline.

Human cytokine array.Human protein cytokines were shown using the cytokine-array kit

from RayBiotech, Inc. (Norcross, GA, USA) according to manufacturer’s protocol. Briefly, the membranes were blocked with a blocking buffer; 1 mL of supernatant from cells collected 24 hours (pooled from the three triplicates) after dissociation with either CDS or trypsin was added and incubated at room temperature for 2 hours. The membranes were washed; 1 mL of primary biotin-conjugated antibody was added and incubated at room temperature for 2 hours. After washing, membranes were incubated with 2 mL of horseradish peroxidase-conjugated streptavidin at room temperature for 30 minutes. The membranes were developed by using enhanced chemiluminescence-type solution, and digital exposures were made using the Fujifilm LAS3000 luminescent image analyzer.

PCR analysis.To investigate the baseline expression of mRNA, H292 cells were cultured

to confluence and washed with phosphate buffered saline. Directly upon

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washing, cells were lysed and mRNA was isolated using the nucleospin RNA II (Machery-Nagel, Düren, Germany), subsequently cDNA was synthesized using the MBI Fermentas first strand cDNA synthesis kit (Fermentas GmbH, St. Leon-Rot, Germany). Primer sequences for all investigated genes ave been described before 19-36, and were obtained from Sigma-Aldrich (Haverhill, UK). PCR was performed on iCycler machine (Bio-Rad, Veenendaal, The Netherlands) and PCR products were run on 1.7% agarose gel for 1 hour at 80 volts.

Statistical analysis.Experiment was performed in triplicate, and mediator levels are given as

mean (± standard deviation) unless stated differently. Statistical significance (p<0.05) between means was determined using ANOVA and the Student’s t -test using GraphPad Prism 4.0 for Windows.

Results

Baseline expression of cytokines by H292 cells.The human airway epithelial cell line NCI-H292 cells constitutively express

cytokines. Using the Bio-Plex Human Cytokine 17-Plex Panel, significant detectable amounts of IL-6, IL-8, G-CSF, IL-7, and IL-13 were measured in the baseline sample (see table 1). Expression of the other cytokines and chemokines was below the detection level of the assay.

Trypsin treatment of H292 causes an increase in secreted cytokines and chemokines.

In the 24 hours following subculturing with trypsin, the cells increase their cytokine production. IL-6, IL-8, and G-CSF production goes up drastically (35-, 7-, and 6-fold respectively). This effect diminishes with time; the per 24 hour production at 48 hours is near baseline level, and at 72 hours baseline production levels are reached. Trypsin did not effect the expression of IL-7 and IL-13; cells secrete an average of 84.9 (±1.9) and 12.0 (±3.8) pg/mL

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respectively over all 24 hour periods (see table 1).IL-1β, IFN-γ, TNF-α, IL-2, and IL-4 could not be detected at baseline, but

were produced in the 24 hours after subculturing The production of these cytokines is transient, since none of these cytokines could be detected at later timepoints (see table 1). Some cytokines were not detectable at baseline or after trypsin treatment (IL-5, IL-10, IL-12, GM-CSF, MCP-1, and MIP-1β).

Mediator Baseline 24h-48h 48h-72h 72h-96hIL-1β (2) n.d. 4.7 (0.23)** n.d. n.d.IFN-γ (8) n.d. 35.9 (2.9)** n.d. n.d.TNF-α (2) n.d. 11.7 (0.8)** n.d. n.d.IL-2 (8) n.d. 17.5 (15.1) n.d. n.d.IL-4 (2) n.d. 8.1 (5.9)* n.d. n.d.IL-5 (8) n.d. n.d. n.d. n.d.MCP-1 (30) n.d. n.d. n.d. n.d.MIP-1β (8) n.d. n.d. n.d. n.d.IL-6 (8) 33.6 (12.5) 1167.7 (104.0)** 96.7 (7.4) 26.6 (19)IL-8 (2) 355.4 (80.1) 2604.4 (132.2)** 304.0 (14.9) 261.7 (48.2)G-CSF (30) 85.3 (13.1) 530.0 (45.0)** 138.2 (15.6) 62.8 (34.0)IL-7 (8) 84.0 (17.0) 87.6 (2.0) 84.8 (5.5) 83.1 (6.1)IL-13 (2) 11.7 (0.0) 17.4 (5.6) 8.4 (5.6) 10.5 (8.0)IL-10 (2) n.d. n.d. n.d. n.d.IL-12 (8) n.d. n.d. n.d. n.d.IL-17 (2) n.d. 6.2 (9.6) n.d. n.d.GM-CSF (2) n.d. 4.0 (7.0) n.d. n.d.

Table 1: Cytokine production per 24 hours after dissociation with trypsin. Cytokine concen-trations are presented as average (SD) of triplicate experiment in pg/mL. Lower detection limits are indicated behind respective mediator (pg/mL). n.d. = not detectable; below detec-tion level. Statistical significance of samples compared to baseline sample is indicated by * (P<0.05) or ** (P<0.01).

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Reduced induction of cytokines by cell dissociation solution (CDS).The observed increase described above can be caused by the disruption

of cell-cell contacts or by trypsin mediated PAR-2 activation. To determine the relative contribution, we used a non-enzymatic method to disrupt cell cell contacts while avoiding PAR-2 activation.

When subcultured with CDS, H292 cells produce increased levels of a number of cytokines compared to baseline. IL-6, IL-8, and G-CSF are significantly increased compared to baseline (24-, 3-, and 6-fold respectively) (table 2). However, this increase is not as high as when cells are treated with trypsin (P<0.01). IL-1β, IFN-γ, and TNF-α, were undetectable before CDS treatment but were produced in the 24 hours after. However the production after CDS treatment is significantly lower than after trypsin treatment. IL-1β: 2.2(±0.14) vs. 4.7(±0.23) pg/mL (P<0.05), IFN-γ 20.9(±5.8) vs. 35.9(±2.9) pg/mL (P<0.01) and TNF-α 4.9(±1.3) vs. 11.7(±0.8) pg/mL (P<0.001) (see table 1 and 2). In contrast to the trypsin treated cells, IL-2 and IL-4 were not produced by H292 cells after CDS treatment (see table 2).

IL-7 and IL-13 expression was not affected after CDS treatment compared to baseline, similar to what could be observed after trypsin treatment. IL-5, IL-10, IL-12, IL-17, GM-CSF, MCP-1, and MIP-1β were not produced in detectable amounts at baseline in this experiment. Similar to trypsin treatment, CDS treatment does not lead to production of these cytokines.

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Mediator Baseline 24h-48h 48h-72h 72h-96hIL-1β (2) n.d. 2.2 (0.14)** n.d. n.d.IFN-γ (8) n.d. 20.9 (5.8)** n.d. n.d.TNF-α (2) n.d. 4.9 (1.3)** n.d. n.d.IL-2 (8) n.d. n.d. n.d. n.d.IL-4 (2) n.d. n.d. n.d. n.d.IL-5 (8) n.d. n.d. n.d. n.d.MCP-1 (30) n.d. n.d. n.d. n.d.MIP-1β (8) n.d. n.d. n.d. n.d.IL-6 (8) 31.7 (8.8) 771.3 (58.4)** 127.5 (8.1)* 32.7 (4.9)IL-8 (2) 352.4 (111.5) 1200.8 (204.9)** 264.1 (2.7) 221.4 (7.4)G-CSF (30) 58.6 (47.4) 368.0 (32.6)** 141.4 (17.6)* 61.3 (5.6)IL-7 (8) 79.6 (14.2) 67.4 (4.0) 83.9 (11.9) 90.9 (11.1)IL-13 (2) 5.2 (5.6) 15.7 (3.5)* 11.7 (0.0) 11.7 (0.0)IL-10 (2) n.d. n.d. n.d. n.d.IL-12 (8) n.d. n.d. n.d. n.d.IL-17 (2) n.d. n.d. n.d. n.d.GM-CSF (2) n.d. n.d. n.d. n.d.

Table 2: Cytokine production per 24 hours after dissociation with cell dissociation solution. Cytokine concentrations are presented as average (SD) of triplicate experiment in pg/mL. Lower detection limits are indicated behind respective mediator (pg/mL). n.d. = not detecta-ble; below detection level. Statistical significance of samples compared to baseline sample is indicated by * (P<0.05) or ** (P<0.01).

Additional signaling molecules produced by H292 cells after treatment with trypsin.

In the previous experiment here we have shown that H292 human airway epithelial cells are capable of producing a broad range of cytokines and chemokines. 14 out of 17 cytokines of the bio-plex assay were shown to be produced, either at baseline or after disruption of cell cell contacts. When these cells are treated with CDS, the production of these cytokines increases; moreover, with trypsin, this increase in production is even greater. A more comprehensive overview of produced cytokines was acquired by using a cytokine antibody array. Supernatant of cells, which had been treated with

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trypsin or CDS, were analyzed for the presence of 79 signaling molecules. With the antibody array, we could confirm and extend our previous findings.

IL-1β, IL-2, IL-8, G-CSF, and MIP-1β protein was again shown in the supernatant after trypsin treatment; IL-8 and G-CSF were also shown to be produced after CDS treatment, but in a lower amount (see figure 1). An additional number of cytokines (Growth-Related Oncogene (GRO), Epidermal Growth Factor (EGF), IGF-Binding Protein 3 (IGF-BP3), Leukemia Inhibitory Factor (LIF), TGF-β2, Tissue Inhibitor of Metalloproteinase (TIMP)-1, and TIMP-2) could be detected 24 hours after CDS or trypsin treatment (see fig. 1).

Figure 1: Cytokine expression after disruption of cell-cell contact with Cell Dissociation solu-tion or trypsin. Chemoluminescence photograph of raybio® human cytokine antibody array V incubated with supernatants of cells 24 hours after dislodging with (A) Cell Dissociation solu-tion or (B) trypsin. Pooled supernatant of triplicate was used. (C) layout of 79 antibody spots for cytokines on the human cytokine antibody array V (IL-12 = p40p70).

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Just like in our previous data, the signal on the array for samples taken after trypsin treatment was stronger than that after CDS treatment. For IFN-γ-inducible protein 10 (IP-10), TGF-β1, Vascular Endothelial Growth Factor (VEGF), Fibroblast Growth Factor (FGF)-4, -9, and Glial Cell Derived Neurotrophic Factor (GDNF) the difference in expression level between trypsin and CDS was such that they could only be detected after trypsin treatment (see figure 1). Possibly due to the detection limit in the RayBiotech human cytokine antibody array, we could not confirm production of any other cytokines (such as IL-1β, IL-6, IL-13, IFN-γ, and TNF-α) from the bio-plex human cytokine 17 plex panel.

mRNA expression of mediators produced by epithelium.Using RT-PCR we investigated expression of 23 genes by H292 cells

that have shown expression at baseline or after disruption of cell cell contact. The genes we investigated were EGF, FGF-4, FGF-9, G-CSF, GDNF, GM-CSF, GRO-α, IGF-BP3, IL-2, IL-4, IL-6, IL-7, IL-8, IL-13, IFN-γ, IP-10, LIF, TGF-β1, TGF-β2, TIMP-1, TIMP-2, TNF-α, and VEGF. Most of these genes were expressed at baseline (figure 2), but we could not show any mRNA expression of FGF-4, FGF-9, GDNF, GM-CSF, and IL-2 at baseline or at different timepoints after disruption of cell cell contacts.Discussion

Figure 2: PCR products for genes expressed by H292 cells at baseline. M=100 bp marker (band sizes are indicated on left hand side of picture in bp), lane 1: β-2-microglobulin, lane 2: EGF , lane 3: G-CSF, lane 4: GRO-α, lane 5: IFG-BP3, lane 6: IL-4, lane 7: IL-6, lane 8: IL-7, lane 9: IL-8, lane 10: IL-13, lane 11: INF-γ, lane 12: IP-10, lane 13: LIF, lane 14: TGF-β1, lane 15: TGF-β2, lane 16: TIMP-1, lane 17: TIMP-2, lane 18: VEGF and lane 19: TNF-α.

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In previous experiments we have investigated the response of airway epithelial cells to allergen exposure 18;37. We showed that exposure to proteolytic active allergen causes up-regulation of genes involved in repair of the structural integrity, in addition to genes involved in downstream immunological signaling, such as cytokines and chemokines. We set out to get a comprehensive overview of the epithelial response on the disruption of the epithelial cell contacts and to try and discriminate between the relative contribution of the intrinsic activation as a consequence of the disruption and the effects of the presence of a protease which can signal through protease activated receptors. We were able to show that disruption of cell-cell contacts in the absence of protease, by cell dissociation solution, already leads to an extensive induction of mediators. Out of the 79 mediators tested nine could be detected. This response is even broader and stronger when trypsin is used, a known PAR-2 activator. Dissociation in the presence of trypsin showed 18 out of 79 evaluated mediators induced, and relative to the mediators induced by mere disruption of cell cell contacts, the expression level is about 50% higher. As expected in our model, we found mediators that are associated with both tissue repair and modulation of inflammation.

The repair of tissue after epithelial damage has been described extensively and is comprised of the deposition of extracellular matrix proteins by fibroblasts, followed by proliferation and migration of epithelial cells from the edge of the lesion to the centre 38. In our experiments we could detect mediators (IL-1β, TGF-β, FGF-4, FGF-9, EGF, VEGF, GDNF, IGF-BP3, TIMP-1, and TIMP-2) that have been reported to have an influence on these processes. When we used PCR to verify mRNA for these mediators to independently confirm their expression, we could detect mRNA for TGF-β, EGF, VEGF, IGF-BP3, TIMP-1, and TIMP-2. However, we could not detect any mRNA for FGF and GDNF at baseline or at later timepoints after either CDS or trypsin treatment. Whether this discrepancy between protein and RNA data is due to the sensitivity of the PCR or due to specificity of the

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cytokine array remains unclear. Although we cannot independently confirm the expression of FGF and GDNF in our system, others have reported that epithelial cells to produce these mediators under specific conditions 39-

41. IL-1β and TGF-β are known factors that stimulate fibroblast growth in several models of repair of epithelial damage 17;42. Besides mediators that are involved in the growth of fibroblasts we also find proteins that are involved in growth of other components of the mucosal layer, such as IGF-BP3, EGF, and VEGF, which respectively stimulate growth in general, of epithelial cells, and vascular endothelial cells 43-45. Besides producing factors that are actively involved in repair of the tissue, epithelial cells also produce TIMP-1 and TIMP-2, two molecules that inhibit matrix metalloproteinases (MMP’s). MMP-9 has been linked to asthma 46. These proteases are reported to be involved in epithelial shedding in asthma and in maintaining the homeostasis of the extracellular matrix 11;47. This indicates that epithelial cells are also directly active in keeping the balance in protease regulated processes.

An other role for epithelial cells is in the recruitment and activation of inflammatory cells to the mucosa through secretion of chemokines and cytokines. After loss of cell-cell contacts, we observe an increased production of IL-6, IL-8 and G-CSF, and a de novo production of IL-1β, IFN-γ and TNF-α. Using RT-PCR, we could show that H292 cells also express mRNA for IL-6, IL-8, G-CSF, IFN-γ and TNF-α. Moreover, when epithelial cells are dissociated in the presence of trypsin, a known PAR-2 activator 1;48, they produce higher amounts of these mediators, and additionally produce IL-4, IP-10, LIF, and GRO. Expression of mRNA for these mediators was confirmed by PCR at baseline. These factors are reported to be involved in the recruitment or activation of inflammatory cells, such as T-cell chemoattraction (IP-10) 49;50, activation and proliferation (IL-6) 51, macrophage activation (IFN-γ) 52, B-cell activation (IL-4) 53, and neutrophil chemoattraction (IL-8, G-CSF) 54.

What can be observed in the time-course experiment is that the production of IL-7 and IL-13 remains generally stable. IL-13 is known to be produced by

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Th2 cells and is known to induce goblet cell formation and stimulate mucus production. IL-7 is involved in the development of immature B-cells and has not been described to be produced by epithelial cells before. The function of these proteins in this model remains unresolved 55;56.

Our data show the expression of a wide spectrum of mediators in a single model. This model of disruption of cell cell contacts mimics a common theme seen in a wide and diverse set of processes that involves the epithelium.

In our research we also find epithelial expression of these mediators, and using RT-PCR we show that these epithelial cells are also capable of expressing the mRNA for these proteins (IFN-γ, TNF-α, LIF). The described role for these mediators is by and on T-cells in the context of infection and asthma. IFN-γ and TNF-α are pro-inflammatory cytokines produced by Th1 cells during infection 57;58. LIF is involved in recruitment of neutrophils 59.

The production of IL-2 and IL-17 can be debated, since protein concentrations were only just above the detection limit of the assay and their expression could not be confirmed by PCR.

In conclusion, we have investigated the response of epithelium to the disruption of cell cell contact by treating airway epithelial cells with cell dissociation solution or trypsin. In response to disruption, epithelial cells are able to produce a wide range of signal molecules, and when this disruption is accompanied by presence of a protease this response is even stronger and its range is wider. Our data show a broad and general response by epithelial cells that extends far beyond the commonly reported IL-1, IL-6, IL-8, GM-CSF, and TGF-β response. Through this broad range of mediators, epithelial cells may participate in both the innate and acquired immune response. These findings emphasize the potential for epithelial cells in mediating and initiating responses to environmental changes, and their role in diseases that affect the structural integrity of mucosal surfaces. As such, we propose epithelial cells should be considered an active constituent part of the mucosal defense system.

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6A strongly reduced synergistic

response to TNF-α and IL-17 detected for the Th1 cytokine

INF-γ in primary nasal epithelial cells from allergic individuals.

Aram B. Vrolinga, Annemieke Snoekb, Silvia Luitena, Wytske J. Fokkensa, Rene Lutterb, Cornelis M. van Drunena

a Department of Otorhinolaryngology, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlandsb Department of Respiratory Medicine, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands

Submitted

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Abstract

Airway epithelial cells are known to respond to TNF-α by producing a variety of other cytokines in response. For some cytokines this response is enhanced by addition of IL-17. To investigate if the described synergistic effect in lung epithelial cells by simultaneous stimulation with TNF-α and IL-17 can also be observed in primary nasal epithelial cells and to see if there are differences between nasal epithelial cells of allergic and healthy individuals. We cultured primary nasal epithelial cells from healthy and allergic individuals. Upon stimulation we measured cytokine production of 27 mediators. We found synergistic effects on production of IL-6, IL-8 G-CSF and INF-γ. For INF-γ however hits synergistic effect was strongest, but only in epithelial cells of healthy controls. This deregulated response of IFN-γ production in allergic individuals could contribute to disease symptoms, and could play a role in reduced antiviral response observed in allergic individuals.

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Introduction

Previously we have shown that the in vitro expression profile in nasal epithelial cells of house dust mite allergic individuals is markedly different from the expression profile in healthy controls 1. At baseline, in the absence of house dust mite allergen, the differences between allergic and healthy nasal epithelium are best described as a sustained activated state in allergic individuals. Nasal epithelial cells from healthy individuals only reach a similar expression level after in vitro exposure to the allergen. Evidently, the allergic status of an individual leaves an imprint on the nasal epithelium which is maintained even after in vitro culture in the absence of house dust mite extract. We have hypothesized that a deregulated expression of a group of transcription factors (NF-κB, AP1, ATF3, and EGR1) underlies this activated state and that this state could be maintained by an autocrine loop involving TNF-α. The latter idea was based on the observation that many of the genes that are up-regulated in nasal epithelial after allergen exposure have been reported to be under the transcriptional control of TNF-α 2. Furthermore, TNF-α has been reported to be a principle downstream target of NF-κB 3.

Here we analyze the potential role of TNF-α on the activation of nasal epithelial cells in greater detail. Van den Berg et al showed that TNF-α-induced IL-6 and IL-8 responses are potentiated by the further addition of IL-17 4. IL-17 acts as proinflammatory mediator and there is clinical and experimental proof that implicates it in various inflammatory diseases 5. Increased levels of IL-17 have been reported in allergic asthma 6;7, reumathoid arthritis 8;9 and inflammatory bowel disease 10. Where TNF-α has a direct effect on the de novo synthesis of the mRNAs for these genes, it was suggested that the potentiating effect of IL-17 is mainly through the inhibition of the breakdown of these mRNAs 11;12. In this manuscript we analyse the effect of the simultaneous addition of TNF-α and IL-17 to primary nasal epithelial cells from healthy and allergic individuals. For 27 soluble mediators we compare

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the effect of the single exposure to either TNF-α or IL-17 to the effect on the combined exposure, and show a strongly reduced synergistic response in nasal epithelium from allergic individuals compared the healthy controls for the Th1 cytokine IFN-γ.

Material and methods

Patient characteristics.This study was reviewed and approved by the medical ethical committee

of the Amsterdam Medical Center and all participants read and signed an informed consent. Five allergic volunteers and five healthy non smoking volunteers were selected based on skin prick test for house dust mite (HDM) and other common allergens, and a nasal allergen provocation to asses their response. Only monotypically HDM allergic and non allergic volunteers were included. Allergic individuals had refrained from using any medication for their allergy in the four weeks prior to the visit when biopsies were taken. Biopsies were taken from the lower edge of the inferior turbinate, 1 and 2 cm from the anterior end, using Fokkens’ forceps with a cup diameter of 2.5 mm. Local anaesthesia was achieved by application of adrenalin and cocaine under the inferior turbinate without touching the biopsy site, during 10 minutes.

Primary epithelial cell culture.Primary cells were obtained by digesting nasal biopsies of volunteers with

0.5 mg/mL collagenase 4 (Worthington Biochemical Corp., Lakewood, NJ) for 1 hour in Hanks’ balanced salt solution (HBSS; Sigma-Aldrich, Zwijndrecht, The Netherlands). Subsequently cells were washed with HBSS and resuspended in bronchial epithelial growth medium (Lonza Clonetics, Breda, The Netherlands) and seeded in a T25 flask and grown in fully humidified air containing 5% CO2 at 37°C, cells were used between passages 3 and 5 until they reached approximately 80% confluency. Stimulation experiment

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Cells were seeded at an initial density of 2 x 104 in 48-well plates and used between passage 3 and 5 for the 24 hours induction experiment. To asses if simultaneous addition of 5 ng/mL TNF-α and 10 ng/mL IL-17 to nasal epithelial cells has a synergistic effect over addition of either stimulus alone, we calculated the ratio of the expression level of 27 soluble mediators after the simultaneous addition of TNF-α and IL-17, over the sum of the expression level for TNF-α and IL-17 alone (Synergy Factor = [mediator]TNF+IL17 / [mediator]TNF + [mediator]IL17).

Luminex assay.Supernatants of cytokine stimulated cells were stored at -20°C until

analysis. Cytokine levels in supernatant of cells were determined using the xMAP technology (Luminex Corporation, Austin, TX, USA). A Bio-Plex Human Cytokine 27-Plex Panel kit (Bio-Rad, Veenendaal, The Netherlands) was used and analyzed on the Bio-Plex workstation (Bio-Rad). All standards were diluted in the same serum free culture medium where the cells were put in during treatment. Concentrations were calculated from a dilution series of standards using the Luminex software. Each experiment was performed in triplicate and concentrations of mediators are given as pg/mL. When a mediator is expressed below the detection level, the detection level for that mediator is used as the missing value with the “<” indication.

Statistical analysis.Statistical significances (p<0.05) between expression levels of healthy

and allergic individuals were determined using unpaired Mann-Whitney rank sum test using GraphPad Prism 4.0 for Windows.

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Results

Baseline and induced expression of mediators in nasal epithelial cells.In this experiment we confirmed that nasal epithelial cells can produce

cytokines and chemokines at low to moderate concentrations at baseline levels (Table 1). Out of a large panel of cytokines and chemokines we could show expression above the detection level for IL-1RA, IL-4, IL-6, IL-8, G-CSF, IL-10, FGF, GM-CSF, IFN-γ, IP-10, and VEGF. Although the expression for these mediators is generally higher in allergic patients than in healthy controls, the expression is clearly highly variable in individual participants. This pattern of expression reflects our previous data, where we saw higher baseline expression of a multitude of genes in allergic compared to healthy individuals1.

SecretedMediator

Healthy Controls Allergic Individuals p-value1 2 3 4 5 1 2 3 4 5

IL-1RA 553 611 1481 383 1108 1243 2779 843 43879 1767 0.002

IL-4 <0.37 <0.37 1.1 <0.37 <0.37 0.61 0.415 2.035 0.94 1.025 0.062

IL-6 12.1 138.6 49.2 <0.58 <0.58 97.3 308.6 1204 <0.58 385.1 0.047

IL-8 47.4 602.7 1477 <0.62 63.2 969.9 799.5 971.2 13.3 1870 0.246

IL-10 2.1 5.7 15.0 <0.93 <0.93 19.7 15.9 27.3 <0.93 23.3 0.036

FGF 9.1 16.9 25.2 21.1 21.1 26.9 29.3 32.5 33.5 32.4 <0.001

G-CSF <1.5 <1.5 35.1 <1.5 <1.5 134.8 12.9 224.5 <1.5 35.7 0.014

GM-CSF 1.6 1.9 31.2 24.1 24.8 41.1 28.7 37.1 26.6 30.5 0.003

IFN-γ <1.33 <1.33 <1.33 <1.33 <1.33 2.5 2.3 12.0 2.2 5.6 0.008

IP-10 <15.1 <15.1 32.9 <15.1 <15.1 108.7 <15.1 653.8 <15.1 72.3 0.021

VEGF 137.9 317.8 1008 <1.59 10.2 1733 1549 2057 34.5 2000 0.011

Table 1: Variable baseline expression in individual participants. Concentration of secreted epithelial mediators is presented in pg/mL. If a cytokine could not be picked up in a sample then it is indicated that the expression is less than the detection limit. P-values are calculated for the difference in expression between healthy and allergic individuals.

Exposure to TNF-α or IL-17 induced expression to a different extent for the different mediators. Table 2 shows that most mediators are induced by both TNF-α or IL-17 albeit to a different extent, while a few mediators (IL-

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1RA, IL-10, VEGF) are not induced. For G-CSF, GM-CSF, IFN-γ, IP-10 the induction factor was more pronounced in healthy individuals than in allergic individuals. Interestingly, the opposite was true for IL-6 that showed a much stronger inducing capacity in allergic individuals (TNF-α: 3.62 fold and IL-17: 2.37 fold) than in healthy individuals (TNF-α: 2.27 fold and IL-17: 1.41 fold).

Mediator Allergic status TNF-α IL-17 TNF-α + IL-17

IL-1RAHealthy 1.28 1.40 1.35Allergic 1.01 0.94 0.88

IL-4Healthy >3.83 >4.16 >11.4Allergic 4.73 5.67 14.0

IL-6Healthy 2.27 1.41 4.03Allergic 3.62 2.37 10.64

IL-8Healthy 6.65 4.19 53.79Allergic 7.67 2.82 29.37

IL-9Healthy >29.2 >68.6 >122Allergic >13.5 >85.6 >88.7

IL-10Healthy 1.50 1.71 2.62Allergic 1.26 1.19 1.69

FGFHealthy 1.37 1.64 1.83Allergic 1.13 1.16 1.32

G-CSFHealthy >5.0 >158 >699Allergic >3.46 >41.9 >57.9

GM-CSFHealthy 3.40 4.22 6.23Allergic 1.35 1.32 2.32

IFN-γHealthy >15.6 >14.8 >975Allergic 7.67 9.32 27.4

IP-10Healthy >9.98 >4.84 >10.6Allergic >5.32 >1.74 >2.99

VEGFHealthy 1.45 1.46 2.20Allergic 1.45 1.08 1.81

Table 2: Induction of cytokine expression. Upon stimulation with TNF-α, IL-17, or combined TNF-α/IL-17, epithelial cells produce cytokines. We calculated the induction by dividing pro-duction after stimulation by production in medium control.

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Synergistic effect of TNF-α or IL-17 on mediator expression in nasal epithelial cells.

To study a potential synergistic effect of TNF-α and IL-17 it is important to show that TNF-α did not affect IL-17 expression levels and conversely that IL-17 did not affect TNF-α expression. Indeed this was not the case (data not shown). Table 2 shows that the simultaneous addition of TNF-α and IL-17 has a different effect for the various cytokines. For some cytokines (IL-4, IL-9, IL-10, FGF, GMCSF, IP10, and VEGF), the inducing effects of TNF-α and IL-17 seems additive, with the expression level after the simultaneous addition merely being the sum of the expression of each factor individually. For IL-1RA and FGF, the simultaneous addition of TNF-α and IL-17 leads to an expression level that is not different from when either of the factors is added alone. However, what stands out most from the comparison is the synergistic effect between TNF-α and IL-17 on the expression of IL-6, IL-8, GCSF, and IFN-γ. Figure 1 shows a moderate synergistic effect for IL-6, a clear synergistic effect for both IL-8 and GCSF, and a very strong synergistic effect for IFN-γ.

IL1ra IL4 IL6 IL8 IL9 IL1

0FGF

GCSF

GMCSFIP

10IFNg

VEGF0

2

4

6

8

10controlallergic

4050607080

syne

rgy

FIgure 1. Synergy factors. Synergistic effect of com-bined TNF-α and IL-17 stimulation compared to stimulation with the factors alone. Values represented are average (± SD) calcu-lated between the 5 indivi-duals.

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Interestingly, there is a difference in the synergistic effect between healthy and allergic individuals. For IL-8, GCSF, and IFN-γ the synergistic effect in allergic individuals is smaller than in healthy individuals. For IL-8 the synergistic effect goes down from 4.8 to 2.5, for GCSF from 3.54 to 1.53 and, most remarkably, for IFN-γ the synergistic effect goes down from 49.0 in healthy individuals to just 1.5 in allergic individuals. Only IL-6 seems to behave in an opposite fashion, with a moderate synergistic effect in allergic individuals (1.6) that seems to be absent (1.1) in healthy individuals.

Discussion

Our previous data using a microarray approach on primary nasal epithelial cells did not only show that the cells from allergic individuals are different at baseline from epithelial cells of healthy individuals, but also that they respond differently to the same trigger. In this manuscript we further add to this concept and show that allergic epithelium differs in INF-γ response after stimulation with IL-17 and TNF-α.

The synergistic effect of the simultaneous addition of TNF-α and IL-17 has been reported before 13-15, but we could only partly replicate these findings. Bronchial epithelial cells and fibroblast show an increased expression of IL-6 and IL-8 in response to the simultaneous addition of TNF-α and IL-17 4. In our experiments this is also true for IL-8, although the extent of the reported synergistic effect somewhat larger in bronchial epithelial cells (7.6 times) than in nasal epithelial cells (4.8 times). However, for IL-6 we only observed a moderate synergistic effect in allergic epithelium and not in healthy epithelium. The most striking difference between the bronchial and nasal data is that in our experiments IL-17 alone already induces IL-6 and IL-8, whereas IL-17 was not able to induce these genes in bronchial epithelial cells 4. A difference in response for bronchial and nasal epithelial cells to an identical trigger has been observed before with bronchial epithelial cells

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responding strongly to the addition of lipopolysaccharide (LPS), while nasal epithelial cells are unresponsive towards LPS (data not shown). Why these nasal responses would be different from the data in bronchial epithelial cells is not clear. Possibly this is related to the constant exposure to environmental stimuli in the nose, with the bronchial epithelial cells being rather shielded from these stimuli. A similar difference in the response to LPS has also been observed for gut and kidney epithelial cells, with the gut epithelial cells being refractory for LPS and the kidney epithelium responding strongly to LPS 16;17. The absence of a clear response to ubiquitous signals (LPS) in the nasal mucosa could perhaps be compensated by a stronger response to inflammatory signals like TNF-α and IL-17.

The use of a broad panel of cytokines in the readout of the synergistic effect of TNF-α and IL-17 allowed us to discover that in addition to IL-6 and IL-8, also GCSF and INF-γ show a synergistic response. The most relevant observation of this work must be that the synergistic effects differ between healthy epithelium and allergic epithelium. Although for most mediators these differences are small, the difference for INF-γ is very large.

As a result, the concentration of INF-γ produced by epithelial cells after the combined exposure to TNF-α and IL-17 is 62-fold higher than after TNF-α exposure and 65-fold higher than after IL-17 exposure (1297 pg/mL versus 20.7 pg/mL, respectively 19.7 pg/mL) in healthy individuals. In allergic individuals we can find a completely different pattern, with the combined stimulation at most 3.9 times higher than either of the stimuli alone. As a consequence, the synergistic effect of combined stimulation is much lower in allergic individuals, despite that the baseline expression of INF-γ is not that different between allergic and healthy individuals.

Given the importance of INF-γ in the Th1 response in general and the antiviral response in particular, this defective INF-γ response in epithelial cells can have important consequences for both the Th1/Th2 balance in the nasal mucosa and/or the effectiveness of an antiviral response. It has

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been shown that allergic individuals are more susceptible to viral infections, and that these viral infections are more prolonged 18-23. How this process works and what role the epithelial lining in the airway mucosa and the IL-17 producing T-cells may play in this is yet to be exposed.

The modulating capacity of IL-17 on TNF-α-induced gene expression has been described before and has been attributed to both an increase in the transcription rate of down-stream targets by TNF-α and an inhibition of mRNA degradation by IL-17. Although the precise mechanisms still needs to be elucidated, mRNA-specific degradation involves defined sequences in the 3’-untranslated region of mRNAs that are targeted by small regulatory RNAs called microRNAs or miRNAs 24. Over 700 of these 20-25 base pair long miRNAs have been identified up to now and although not all of these miRNAs are thought to be involved in mRNA degradation 25, the numbers of these miRNA are sufficiently large to be able to co-regulate a substantial number of genes. In this respect it would be interesting to see if IL-17 affects specific miRNAs and whether these miRNAs target TNF-α-induced mRNAs.

The cellular source of IL-17 in the nasal mucosa is unclear. A likely source could be the recently identified Th-17 cell, a new subtype of T helper lymphocyte for which the exact function is still elusive, but which is uniquely marked by its high level of IL-17 expression 26. In chronic bowel disease a marked up-regulation of IL-17 has been reported, indicating IL-17 could function as an enhancer of inflammatory processes 10.

In our previous experiments with primary nasal epithelial cells the expression profiling showed that the allergic state of an individual leaves an imprint on the epithelial cells. This imprint itself is not necessarily responsible for the allergic phenotype of patients, but it does result in a different response to allergens in allergic individuals compared to healthy individuals. Now we show that this imprint can also have a direct impact on the Th1/Th2 balance, as in allergic individuals the impaired INF-γ response will affect the ratio between IL-4 (the hallmark cytokine of the Th2 response) and INF-γ (the

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hallmark cytokine of the Th1 response) 27. As an additional consequence this deregulation of INF-γ may also be responsible for a reduced antiviral response observed in allergic individuals.

Reference List

Vroling, A. B., M. J. Jonker, S. Luiten, T. M. Breit, W. J. Fokkens, and C. M. van Drunen. 1. 2008. Primary nasal epithelium exposed to house dust mite extract shows activated expression in allergic individuals. Am.J Respir.Cell Mol Biol. 38:293-299.Vroling, A. B., D. Duinsbergen, W. J. Fokkens, and C. M. van Drunen. 2007. Allergen 2. induced gene expression of airway epithelial cells shows a possible role for TNF-alpha. Allergy 62:1310-1319.Paik, J., J. Y. Lee, and D. Hwang. 2002. Signaling pathways for TNFa-induced COX-3. 2 expression: mediation through MAP kinases and NFkB, and inhibition by certain nonsteroidal anti-inflammatory drugs. Adv.Exp.Med.Biol. 507:503-508.van den, B. A., M. Kuiper, M. Snoek, W. Timens, D. S. Postma, H. M. Jansen, and 4. R. Lutter. 2005. Interleukin-17 induces hyperresponsive interleukin-8 and interleukin-6 production to tumor necrosis factor-alpha in structural lung cells. Am.J.Respir.Cell Mol.Biol. 33:97-104.Kolls, J. K. and A. Linden. 2004. Interleukin-17 family members and inflammation. 5. Immunity 21:467-476.Molet, S., Q. Hamid, F. Davoine, E. Nutku, R. Taha, N. Page, R. Olivenstein, J. Elias, and 6. J. Chakir. 2001. IL-17 is increased in asthmatic airways and induces human bronchial fibroblasts to produce cytokines. J Allergy Clin.Immunol 108:430-438.Wong, C. K., C. Y. Ho, F. W. Ko, C. H. Chan, A. S. Ho, D. S. Hui, and C. W. Lam. 2001. 7. Proinflammatory cytokines (IL-17, IL-6, IL-18 and IL-12) and Th cytokines (IFN-gamma, IL-4, IL-10 and IL-13) in patients with allergic asthma. Clin.Exp.Immunol 125:177-183.Kotake, S., N. Udagawa, N. Takahashi, K. Matsuzaki, K. Itoh, S. Ishiyama, S. Saito, K. 8. Inoue, N. Kamatani, M. T. Gillespie, T. J. Martin, and T. Suda. 1999. IL-17 in synovial fluids from patients with rheumatoid arthritis is a potent stimulator of osteoclastogenesis. J Clin.Invest 103:1345-1352.Chabaud, M., J. M. Durand, N. Buchs, F. Fossiez, G. Page, L. Frappart, and P. Miossec. 9. 1999. Human interleukin-17: A T cell-derived proinflammatory cytokine produced by the rheumatoid synovium. Arthritis Rheum. 42:963-970. Fujino, S., A. Andoh, S. Bamba, A. Ogawa, K. Hata, Y. Araki, T. Bamba, and Y. Fujiyama. 10. 2003. Increased expression of interleukin 17 in inflammatory bowel disease. Gut 52:65-70. Roger, T., T. A. Out, H. M. Jansen, and R. Lutter. 1998. Superinduction of interleukin-6 11. mRNA in lung epithelial H292 cells depends on transiently increased C/EBP activity and durable increased mRNA stability. Biochim.Biophys.Acta 1398:275-284. Roger, T., T. Out, N. Mukaida, K. Matsushima, H. Jansen, and R. Lutter. 1998. 12. Enhanced AP-1 and NF-kappaB activities and stability of interleukin 8 (IL-8) transcripts are implicated in IL-8 mRNA superinduction in lung epithelial H292 cells. Biochem.J 330 ( Pt 1):429-435. Andoh, A., H. Takaya, J. Makino, H. Sato, S. Bamba, Y. Araki, K. Hata, M. Shimada, T. 13. Okuno, Y. Fujiyama, and T. Bamba. 2001. Cooperation of interleukin-17 and interferon-gamma on chemokine secretion in human fetal intestinal epithelial cells. Clin.Exp.Immunol 125:56-63. Henness, S., C. K. Johnson, Q. Ge, C. L. Armour, J. M. Hughes, and A. J. Ammit. 14. 2004. IL-17A augments TNF-alpha-induced IL-6 expression in airway smooth muscle

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by enhancing mRNA stability. J Allergy Clin.Immunol 114:958-964. Katz, Y., O. Nadiv, and Y. Beer. 2001. Interleukin-17 enhances tumor necrosis factor 15. alpha-induced synthesis of interleukins 1,6, and 8 in skin and synovial fibroblasts: a possible role as a “fine-tuning cytokine” in inflammation processes. Arthritis Rheum. 44:2176-2184. Chowdhury, P., S. H. Sacks, and N. S. Sheerin. 2006. Toll-like receptors TLR2 and 16. TLR4 initiate the innate immune response of the renal tubular epithelium to bacterial products. Clin.Exp.Immunol. 145:346-356. Naik, S., E. J. Kelly, L. Meijer, S. Pettersson, and I. R. Sanderson. 2001. Absence of Toll-17. like receptor 4 explains endotoxin hyporesponsiveness in human intestinal epithelium. J.Pediatr.Gastroenterol.Nutr. 32:449-453. Hassantoufighi, A., M. Oglesbee, B. W. Richter, G. A. Prince, V. Hemming, S. Niewiesk, 18. and M. C. Eichelberger. 2007. Respiratory syncytial virus replication is prolonged by a concomitant allergic response. Clin.Exp.Immunol 148:218-229. Papadopoulos, N. G., P. Xepapadaki, P. Mallia, G. Brusselle, J. B. Watelet, M. Xatzipsalti, 19. G. Foteinos, C. M. van Drunen, W. J. Fokkens, C. D’Ambrosio, S. Bonini, A. Bossios, J. Lotvall, P. van Cauwenberge, S. T. Holgate, G. W. Canonica, A. Szczeklik, G. Rohde, J. Kimpen, A. Pitkaranta, M. Makela, P. Chanez, J. Ring, and S. L. Johnston. 2007. Mechanisms of virus-induced asthma exacerbations: state-of-the-art. A GA2LEN and InterAirways document. Allergy 62:457-470. Wark, P. A., S. L. Johnston, F. Bucchieri, R. Powell, S. Puddicombe, V. Laza-Stanca, S. 20. T. Holgate, and D. E. Davies. 2005. Asthmatic bronchial epithelial cells have a deficient innate immune response to infection with rhinovirus. J Exp.Med 201:937-947. Zambrano, J. C., H. T. Carper, G. P. Rakes, J. Patrie, D. D. Murphy, T. A. Platts-Mills, F. 21. G. Hayden, J. M. Gwaltney, Jr., T. K. Hatley, A. M. Owens, and P. W. Heymann. 2003. Experimental rhinovirus challenges in adults with mild asthma: response to infection in relation to IgE. J Allergy Clin.Immunol 111:1008-1016. van Benten, I. J., C. M. van Drunen, J. L. Koevoet, L. P. Koopman, W. C. Hop, A. 22. D. Osterhaus, H. J. Neijens, and W. J. Fokkens. 2005. Reduced nasal IL-10 and enhanced TNFalpha responses during rhinovirus and RSV-induced upper respiratory tract infection in atopic and non-atopic infants. J Med Virol. 75:348-357. de Waal, L., L. P. Koopman, I. J. van Benten, A. H. Brandenburg, P. G. Mulder, R. L. de 23. Swart, W. J. Fokkens, H. J. Neijens, and A. D. Osterhaus. 2003. Moderate local and systemic respiratory syncytial virus-specific T-cell responses upon mild or subclinical RSV infection. J Med Virol. 70:309-318. Guarnieri, D. J. and R. J. DiLeone. 2008. MicroRNAs: a new class of gene regulators. 24. Ann.Med. 40:197-208. Liu, J. 2008. Control of protein synthesis and mRNA degradation by microRNAs. 25. Curr.Opin.Cell Biol. 20:214-221. Yao, Z., S. L. Painter, W. C. Fanslow, D. Ulrich, B. M. Macduff, M. K. Spriggs, and 26. R. J. Armitage. 1995. Human IL-17: a novel cytokine derived from T cells. J Immunol 155:5483-5486. Monick, M. M., T. O. Yarovinsky, L. S. Powers, N. S. Butler, A. B. Carter, G. Gudmundsson, 27. and G. W. Hunninghake. 2003. Respiratory syncytial virus up-regulates TLR4 and sensitizes airway epithelial cells to endotoxin. J Biol.Chem. 278:53035-53044.

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7General discussion

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Concepts in allergy research.

The cells of the immune system fulfill their roles in the defense against potential threats to the organism in the context of a local tissue that is exposed to the environment. This applies to both the initiation of an immune response where the potential threat is first detected and the elicitation of an immune response where immune effector cells try to eliminate this threat. Most of the research in the field of immunology has focused on the immune cells themselves, their specific contribution to the immune response, and how their function is affected by signals originating from the external environment. Many experimental techniques make use of ex vivo isolated or in vitro generated immune cells, leaving out many of the tissue factors that influence the response. In such an experimental design the potential influence from local tissue via the mediators they produce can not be studied. How different immune cells interact in the allergic cascade with each other and the local tissue is illustrated in figure 1.

Related to these design issues, the immune response as seen in allergy is often defined as an influx of immune competent cells into local tissues. However, little attention is paid to potential differences in immune competent cells or differences in local tissue cells between healthy and allergic individuals. When one considers allergy as a disease this can easily be understood from a clinical perspective where the allergic individual responds to a given trigger, while a healthy individual does not respond to the same trigger. From a mechanistic point of view however it should be highlighted that a healthy individual may have a cellular response to the same trigger as an allergic individual, but that this cellular response would be different from the response in an allergic individual so that in the healthy individual there is no expression of clinical symptoms.

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blood vessel

lymph vessel

lymph node

mast cell

epithelial cell layer

eosinophil

IgE

mediators

neutrophil

B cell

dendritic cell

Treg

Th2

Th1

Th0

(proteolytic)Allergen

(proteolytic)Allergen

Epithelial damageActivated epithelial cells

Figure 1: Immune response to allergens. Schematic drawing of the processes that take place during sensitization and elicitation of an immune response. When an allergen comes into contact with the mucosa, the epithelial cells will activate and start producing cytokines and chemokines, which will attract dendritic cells which take up the allergen. These dendritic cells will then migrate to the lymph nodes where they present the allergen to T-cells that will skew towards T helper 2 phenotype. These Th2 cells will in turn instruct B-cells to produce specific antibodies that are loaded onto mast cells and eosinophils. When the allergens is encounte-red for the second time the epithelial cells will again produce cytokines and chemokines, that attract the now primed immune cells and allergic inflammation follows.

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In this thesis the focus is on nasal airway epithelial cells, how they respond to an environmental trigger (house dust mite allergen), and on identifying potential differences in the response to this trigger depending on the allergic status of the person these nasal epithelial cells were obtained from. The rationale of this approach is that epithelial cells could affect the local tissue environment in which immune competent cells are active, and that the epithelium can regulate an ongoing immune response through these immune competent cells. Three criteria need to be fulfilled in this concept. Firstly, nasal epithelial cells need to be able to detect changes in their environment and should be able to respond to these changes. Secondly, the response of nasal epithelial cells should to be able to affect the functions of immune competent cells. Thirdly, the epithelial responses in healthy and allergic individuals should either be different per se, or the tissue resident immune competent cells in healthy and allergic individuals should be differently affected by this epithelial response.

Detecting changes in the environment.

The first criterion is clearly fulfilled as airway epithelial cells have many mechanisms to detect changes in their environment. Through a collection of distinct receptors the epithelium is able to respond to structural components of micro-organisms like bacteria, viruses, and helminths, these are called Pattern Recognition Receptors (e.g. Toll Like and NOD Like Receptors), or to the enzymatic active components of potential allergens (Protease Activated Receptors) 1-3. Although epithelial cells are able to respond to all these factors, research into the response to an environmental factor has necessarily focused on exposure to a single trigger. This does not reflect the normal situation where simultaneous exposure to a mixture of factors is more common. Moreover, although all the receptors have distinct signaling pathways they almost always seem to lead to activation of one particular

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transcription factor (NF-κB), which is odd since it would seem to lead to a loss of the specificity achieved by the multitude of the receptors on airway epithelial cells. The data on the activation of epithelial cells by house dust mite allergen in this thesis clearly shows that NF-κB is not the only transcription factor affected, but that it is just one of a substantial number of transcription factors. It is highly relevant to note that a large portion of these transcription factors are able to physically interact with each other (NF-κB and AP-1 family members, together with ATF-3 and EGR-1) 4-6 to form hybrid transcription factors that can have opposite effects than their normal counterparts.

The discovery of these transcription factors as downstream targets of the detection of house dust mite allergen exposure should perhaps not come as a surprise. The microarray approach followed throughout this thesis has allowed such an outcome, as such experiments are inherently unbiased.

At the effector side a similar picture emerges, with many cytokines, chemokines, and growth factors being secreted by nasal epithelial cells through which many downstream processes can be influenced 7. The microarray approach should also serve as a warning concerning generalizations of outcomes in experiments. When common outcome parameters like IL-6 and IL-8 would have been used, the responses of the bronchial cell line H292 and healthy primary epithelium look very similar. The microarray experiment shows that this similarity is only true for a limited number of mediators that represent only a small fraction of the overall response. However, this is not all bad as such common factors in the response between different cells, as limited as they may seem, could point to a conserved and thereby important mechanism that defines such a response. In the detailed analysis of the comparison of the responses in the different epithelial cells a conserved set of genes has been identified that could be seen to define the allergic response.

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Epithelial cells affecting the immune response.

In this thesis we have not tried to show that epithelial cells can affect downstream processes, but available data in literature and our own expression profiling clearly identifies many processes they could influence. One of the drawbacks of a traditional microarray experiment is that so many genes change and that it would be easy to pick a few genes of interest and show that any given process could be affected by epithelial cells. However, if we would focus on the data in chapter 4, where a core response of epithelial cells to the exposure to house dust mite has been defined, we can attribute a role for all secreted mediators in controlling certain aspects of the immune response. When we briefly summarize our observations of chapter 2 we see that epithelial cells are actively involved in the recruitment of neutrophils (GRO-α, GRO-β, GRO-γ, and IL-8) 8;9 and dendritic cells (MIP-3α) 10. Moreover, epithelial cells produce mediators that can both inhibit activation of dendritic cells (IL-6) 11 and stimulate their migration of out of the tissue (IL-1β) 12. The epithelial expression of IL-6 could be related to our unpublished observations that epithelium culture supernatant can suppress the spontaneous maturation of monocyte-derived dendritic cells.

Epithelial cells and the allergic condition.

The allergic condition is often defined as an influx of inflammatory cells into local tissues. At the effector side of the immune response such a definition would be fine as these cells are only present in an ongoing immune response. However, in the same overview one would also include dendritic cells, not only when actively surveying local tissues prior to the detection of a potential threat, but also when contributing to on ongoing response after they have been recruited to a site of ongoing inflammation. This might represent a generalization and could introduce a potential blind spot as it

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does not consider that the diseased state itself may affect the function of these cells. Some activities of dendritic cells have indeed been reported to be affected by the different immunological states found in healthy or allergic individuals. However, it remains unexplored whether the differences between the dendritic cells in healthy and allergic individuals are a consequence of the allergic condition, if they contribute to the allergic condition, or even if they are the cause of this condition.

The issue of a disease state affecting cells is particularly relevant for cells making up local tissues as they will always be present independent of the disease state. The data presented in this thesis show that the allergic condition has a profound effect on nasal epithelial cells. When epithelial cells are isolated from healthy or allergic individuals and cultured for two weeks, the expression profiles of these epithelia are very different between healthy and allergic. A substantial part of the differences between the epithelial cells from healthy and allergic individuals can best be described as an activated state at baseline in allergic individuals that is only induced in epithelial cells of healthy individuals after in vitro exposure to house dust mite. If one considers that the epithelial cells have been exposed in both healthy and allergic individuals when the cells were still in situ, one must conclude that the effect of this in situ exposure is still visible after two weeks in culture. This indicates a mechanism of imprint of an epigenetic or autocrine origin. The exact mechanism has not been explored, but could involve TNF-α, as network analysis has identified this player as a linking factor in many of the genes that are affected in the allergic condition. Epithelial cells are able to respond to exogenously added TNF-α, but when considering the role of autocrine TNF-α we should note that TNF-α is initially produced in a membrane associated form that does not only serve a precursor for soluble TNF-α, but that it is able to activate an independent TNF-α-receptor too 13. It is interesting that TNF-α has received a lot of attention in autoimmunity and that effective treatment regimes in rheumatoid arthritis have been developed that target TNF-α

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signaling. The role of TNF-α in PAR mediated allergy was also confirmed in a study in which it was shown that activation of PAR-2 with an activating peptide leads to immune activation to simultaneous administered OVA, and that this process could be inhibited by blocking TNF-α signaling 14.

In our results we also observed changes in TLR-3 (a pattern recognition receptor specific for viruses 16), and TIRAP (an important downstream signaling molecule for TLR-3 17) indicating that there could also be interaction between the signaling cascade that is initiated upon allergen exposure and viral infection. In a recent review on viral exacerbations in allergic disease 15 it was remarked that even at the epithelial mediator level it can be difficult to discriminate a viral infection from allergy, with many of the mediators induced by viruses after infection of epithelial cells also being reported to be elevated in allergy.

Future opportunities for research.

This thesis has taught us the extent of the response of airway epithelial cells to house dust mite extract and how this response differs between the nasal epithelial cells from healthy and allergic individuals. The identification of a viral detection signaling cascade could indicate similarities between the two responses. Moreover, part of the identified response could be a normal response triggered by stress of some description in airway epithelia and that this response would be shared by many environmental triggers. To resolve these questions it would be important to compare more epithelial responses to different triggers.

A first choice would be to study whether different allergens trigger a similar response to house dust mite in nasal epithelial cells. Both similarities and differences in the response would help us to understand the allergic response better. The similarities would point to a common mechanism by which epithelial cells could contribute to the allergic response, while the

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differences might help us to understand why some individuals have become allergic to, for instance, house dust mite and others to grass or tree pollen. Given that viral infections can trigger asthma exacerbations in allergic individuals independent of what allergen they are sensitized for, it could perhaps be expected that viral and allergen induced signaling both include similar genes.

With the allergic condition defined it would be relevant to understand how this allergic condition state is maintained and what can be done to convert the allergic response in epithelium back to a normal response. A detailed analysis for the transcription factors in the allergic model and the way their interactions affect gene transcription is necessary. In particular it would be important to understand if the different expression clusters we have identified in chapter 3 are related to the function of transcription factors. It has been shown that NF-κB transcription can be inhibited by AP-1 5. Given that our data show that AP-1 is down-regulated in allergic individuals, while it is up-regulated in healthy individuals. This could partly explain why the house dust mite response is maintained in allergic individuals. Another factor that is known to inhibit NF-κB transcription is EGR-1 4 of which we see up-regulation in healthy individuals and down-regulation in allergic individuals. Further research on these interactions can provide further indications towards understanding how the allergic condition is maintained.

When we investigate the clusters in more detail we start to see aspects of concomitant regulation of genes related to a similar process. For instance cell-cell contact which is maintained by the tight junctions located between adjacent epithelial cells. Maintenance of these junctions is vital to the organism, and since house dust mite extract is capable of cleaving tight junctions, exposure to such allergen should result in a response where tight junctions are reinstated. Such a response can be observed and includes genes like Claudin-1, Claudin-4, Tight Junction Protein-1, Tight Junction Protein-2, and Occludin, which are regulated in an identical fashion and all

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can be found in the same cluster 18-20. However, it would go to far to assume that all genes within one expression cluster are involved in the same function or process. Conversely, not all genes corresponding to the same process should be expected to belong to the same cluster. Rab-7, for instance, is required to bring Claudin molecules to the cell surface, yet belongs to a different cluster than the other proteins related to the tight junctions belong to. although these are two different clusters they both share the “activated in allergic” phenotype described in chapter 3. What is important is that clusters contain genes with similar behavior and could thereby be a target for treatment. When considering target genes in the allergic condition for treatment it could be important to influence sufficient genes that contribute to this state as it is unlikely, that a single factor can be held responsible for allergy or that targeting a single factor would resolve the disease. Moreover down-regulating genes without knowing if their expression is orchestrated by the allergic condition could present problems with other cellular defenses or autoimmunity. Although the field of microRNAs is relative unexplored in allergy it could be very relevant to study deregulation of these small regulatory RNAs as they have been shown to play an important role in maintaining messenger RNA stability and inducing degradation. Through binding to the 3’-end of messenger RNA they are able to regulate gene expression, just as binding sites for specific transcription factors can do in the promoter regions of genes.

An exciting application of the knowledge we have gathered and the analysis tools we have used could be in the diagnosis of allergic disease in young children. The diagnosis in children under the age of two is inherently difficult. Skin prick testing and serum levels of IgE are not yet reliable objective outcome measures and the interpretation of clinical symptoms like rhinorrhea and sneezing for allergic rhinitis or cough and wheeze for allergic asthma are hard to tell apart from similar symptoms caused by common viral and bacterial respiratory tract infections that are so prevalent

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in young children. Since principal component analysis in our experiments was capable of separating allergic and healthy individuals, we could also use such an approach for young children, where we now have to depend on clinical symptoms which are sometimes hard to distinguish. A major advantage of principal component analysis is that every individual is a single data point in multidimensional space so that a conclusion can be reached for each individual, this in contrast to more common analysis methods that require some characteristic of a group of patients making it hard to draw conclusions for a single individual. Some issues need to be resolved before this application can be used as a diagnostic tool. As already indicated the allergic model does include signaling pathways for viral infections so that we need to determine which genes are specific for the allergen exposure and which are shared with viral infections. If we were able to define established allergy in children we would still need to define the allergic condition in children as the immune system of children under the age of three years is still under development and may therefore yield a different outcome. Although a definition for allergic rhinitis would be a great step forward in the early detection and treatment, it would be an added bonus if a similar approach could be used to define allergic asthma in young children. In adults it could be conceived that similar analysis methods like the ones described in this thesis could also be used for primary bronchial epithelial cells, however, in young children bronchial epithelial cells are not easily obtainable due to invasive nature of a bronchoscopy. However, there is some tentative data suggesting that lower airway disease may also be reflected in the upper airways. Data by our group has shown that in nasal samples taken during a rhinosyncytial virus infection, IL-18 can only be detected when the infection is not restricted to the upper airways but also includes the lower airways. It would be interesting to see if similar observations can be made for allergic asthma, and if some aspects of the allergic condition in the lower airways are reflected by the nasal epithelium. A final application of expression profiling

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could be in following a cohort of children at risk for the development of allergic disease in order to find an early predictor for children that will develop allergic disease and that will proceed to develop asthma later in life.

Allergic disease will be with us for some years to come, but the data described in this thesis sheds some new light on established allergic rhinitis, identifying potential mechanisms that could be targeted for treatment, and opening avenues for the development of new diagnostic tools in the allergy field.

Reference List

Dong, Z., Z. Yang, and C. Wang. 2005. Expression of TLR2 and TLR4 messenger RNA 1. in the epithelial cells of the nasal airway. Am.J Rhinol. 19:236-239.Uehara, A., Y. Fujimoto, K. Fukase, and H. Takada. 2007. Various human epithelial 2. cells express functional Toll-like receptors, NOD1 and NOD2 to produce anti-microbial peptides, but not proinflammatory cytokines. Molecular Immunology 44:3100-3111.Asokananthan, N., P. T. Graham, D. J. Stewart, A. J. Bakker, K. A. Eidne, P. J. Thompson, 3. and G. A. Stewart. 2002. House Dust Mite Allergens Induce Proinflammatory Cytokines from Respiratory Epithelial Cells: The Cysteine Protease Allergen, Der p 1, Activates Protease-Activated Receptor (PAR)-2 and Inactivates PAR-1. J Immunol 169:4572-4578.Chapman, N. R. and N. D. Perkins. 2000. Inhibition of the RelA(p65) NF-kappaB subunit 4. by Egr-1. J.Biol.Chem. 275:4719-4725.Kim, T., J. Yoon, H. Cho, W. B. Lee, J. Kim, Y. H. Song, S. N. Kim, J. H. Yoon, J. Kim-Ha, 5. and Y. J. Kim. 2005. Downregulation of lipopolysaccharide response in Drosophila by negative crosstalk between the AP1 and NF-kappaB signaling modules. Nat.Immunol 6:211-218.Nilsson, M., J. Ford, S. Bohm, and R. Toftgard. 1997. Characterization of a nuclear 6. factor that binds juxtaposed with ATF3/Jun on a composite response element specifically mediating induced transcription in response to an epidermal growth factor/Ras/Raf signaling pathway. Cell Growth Differ. 8:913-920.Asokananthan, N., P. T. Graham, J. Fink, D. A. Knight, A. J. Bakker, A. S. McWilliam, 7. P. J. Thompson, and G. A. Stewart. 2002. Activation of Protease-Activated Receptor (PAR)-1, PAR-2, and PAR-4 Stimulates IL-6, IL-8, and Prostaglandin E2 Release from Human Respiratory Epithelial Cells. J Immunol 168:3577-3585.Smith, D. F., E. Galkina, K. Ley, and Y. Huo. 2005. GRO family chemokines are specialized 8. for monocyte arrest from flow. Am.J.Physiol Heart Circ.Physiol 289:H1976-H1984.Kobayashi, Y. 2008. The role of chemokines in neutrophil biology. 9. Front Biosci. 13:2400-2407. Le Borgne, M., N. Etchart, A. Goubier, S. A. Lira, J. C. Sirard, N. van Rooijen, C. Caux, S. 10. Ait-Yahia, A. Vicari, D. Kaiserlian, and B. Dubois. 2006. Dendritic cells rapidly recruited into epithelial tissues via CCR6/CCL20 are responsible for CD8+ T cell crosspriming in vivo. Immunity. 24:191-201. Santiago-Schwarz, F., J. Tucci, and S. E. Carsons. 1996. Endogenously produced 11. interleukin 6 is an accessory cytokine for dendritic cell hematopoiesis. Stem Cells 14:225-231.

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Cumberbatch, M., R. J. Dearman, and I. Kimber. 1997. Langerhans cells require 12. signals from both tumour necrosis factor-alpha and interleukin-1 beta for migration. Immunology 92:388-395. Janes, K. A., S. Gaudet, J. G. Albeck, U. B. Nielsen, D. A. Lauffenburger, and P. K. 13. Sorger. 2006. The response of human epithelial cells to TNF involves an inducible autocrine cascade. Cell 124:1225-1239. Ebeling, C., T. Lam, J. R. Gordon, M. D. Hollenberg, and H. Vliagoftis. 2007. Proteinase-14. Activated Receptor-2 Promotes Allergic Sensitization to an Inhaled Antigen through a TNF-Mediated Pathway. J Immunol 179:2910-2917. Vercammen, E., J. Staal, and R. Beyaert. 2008. Sensing of viral infection and activation 15. of innate immunity by toll-like receptor 3. Clin.Microbiol.Rev. 21:13-25. Brikos, C. and L. A. O’Neill. 2008. Signalling of toll-like receptors. 16. Handb.Exp.Pharmacol.21-50.Papadopoulos, N. G., P. Xepapadaki, P. Mallia, G. Brusselle, J. B. Watelet, M. Xatzipsalti, 17. G. Foteinos, C. M. van Drunen, W. J. Fokkens, C. D’Ambrosio, S. Bonini, A. Bossios, J. Lotvall, P. van Cauwenberge, S. T. Holgate, G. W. Canonica, A. Szczeklik, G. Rohde, J. Kimpen, A. Pitkaranta, M. Makela, P. Chanez, J. Ring, and S. L. Johnston. 2007. Mechanisms of virus-induced asthma exacerbations: state-of-the-art. A GA2LEN and InterAirways document. Allergy 62:457-470. Koizumi, J., T. Kojima, R. Kamekura, M. Kurose, A. Harimaya, M. Murata, M. Osanai, 18. H. Chiba, T. Himi, and N. Sawada. 2007. Changes of gap and tight junctions during differentiation of human nasal epithelial cells using primary human nasal epithelial cells and primary human nasal fibroblast cells in a noncontact coculture system. J.Membr.Biol. 218:1-7. Mazzon, E. and S. Cuzzocrea. 2008. Role of TNF-alpha in ileum tight junction 19. alteration in mouse model of restraint stress. Am.J.Physiol Gastrointest.Liver Physiol 294:G1268-G1280. Vandenbroucke, E., D. Mehta, R. Minshall, and A. B. Malik. 2008. Regulation of 20. endothelial junctional permeability. Ann.N.Y.Acad.Sci. 1123:134-145.

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Summary

Allergic diseases are very prevalent in the western population, even more than 20% of the general population in the U.S.A. Patients suffering from allergic rhinitis have symptoms like runny nose, blocked nose, itching of the nose and/or eyes, sneezing, impaired smelling, and impaired hearing. Although these symptoms are not lethal they do affect the patients’ quality of life and performance on the job. The costs that are involved in this disease are enormous; it has been estimated that absenteeism and low productivity due to allergies has cost U.S. companies more than $250 million in 1998 which is only a part of the costs since the estimated direct healthcare costs of allergic rhinitis, were more than $6 billion in 1996.

While most other research on allergy has focused on the cells of the acquired immune system our focus in this thesis is on the epithelial cells. The epithelial cells are a part of the nasal mucosa, and being at the surface they play an important role as a barrier, shielding the underlying tissues from the hazardous influences from outside. However they are more than a physical barrier, since they are themselves capable of responding to for instance allergens and pathogens. When they respond they produce cytokines and chemokines, which are important signals for cells in general, but also for cells of the acquired immune system. In our research we set out to see if epihelial cells are involved in allergic reactions, and if they are what their role in this reaction is. By investigating differences between epithelial cells of healthy and allergic individuals we hope to learn more about this. If the function of epithelial cells influences the immune system this would open up new options for the development of treatment.

In chapter 1 we describe three families of receptors that are present on epithelial cells and that play a role in the response against pathogens and allergens. These three main groups are Toll-Like Receptors, NOD-Like Receptors, and Protease Activated Receptors. We also describe the signaling

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pathways that are activated when these receptors are triggered.In chapter 2 we investigated the response of airway epithelial cells to

house dust mite allergen. In an epithelial cell line we find a large number of genes that are up-regulated upon allergen exposure. Most of these genes play a role in cell communication, among which are chemokines and cytokines. We also find genes involved in other processes, such as immunity and defense, receptor activity, and enzyme inhibitor activity. In network analysis we can see what the described interactions are between all genes that are up-regulated after allergen exposure and found a central role for TNF-α.

In chapter 2 we have shown how an airway epithelial cell line responds to allergen exposure, in chapter 3 we have looked into primary nasal epithelial cells, both of healthy and allergic volunteers. Here again we saw up-regulation of genes, but mainly in the healthy control group. When we looked at the gene expression levels of these genes in allergic individuals we saw that these genes were already expressed at a high level and therefore could not increase their expression levels any further. We defined this as the activated state. The genes involved were again involved in cell communication, signal transduction, and transcription factor activity. In a network analysis similar to the epithelial cell line data we saw that the differences in the activated state were also reflected in transcription factors, the regulators of gene expression.

Now that we have established that epithelial cells respond to allergens in a cell line and in primary nasal epithelial cells, we looked into the similarities that can be found between the airway epithelial cell line described in chapter 2 and the primary nasal epithelial cells described in chapter 3. In chapter 4 we have investigated if an epithelial cell line can be used as a model to study epithelial response to allergen. We concluded that the cell line more closely resembles nasal epithelial cell from healthy controls than of allergic individuals, but also that important aspects of the regulated genes are very different. For instance, the number of genes that is affected in the cell line far

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exceeds that in primary cells.The allergen we have used in the experiments described in chapter 2,

3, and 4 contains proteolytic enzymes that cleave the receptor which leads to activation. In chapter 5 we have looked into the response of the airway epithelial cells to a purified proteolytic enzyme, leading to damage of the epithelial layer. We have found that if damage is induced in the presence of such an enzyme the response is much stronger than when similar damage is induced in a non-enzymatic way.

In all our experiments we have found epithelial cells produce cytokines and chemokines in response to proteolytic allergens or enzymes. What is known is that not only these cytokines have an influence on cells of the immune system, but that they can also have an effect on epithelial cells themselves. In chapter 6 we have investigated the response of nasal epithelial cells to the pro-inflammatory cytokines TNF-α and IL-17. Here we found that these two cytokines can have a synergistic effect on the production of some cytokines. Strikingly enough for INF-γ we found that the synergistic effect only occurs in epithelial cells of healthy individuals and not in allergic individuals.

Finally in chapter 7 we discuss how this research has given us more insight into the processes that take place in allergy, and in particular the role the epithelial cells play in this. We have described the differences between epithelial cells of allergic and healthy individuals. These differences point towards different mechanisms of regulation that can influence the allergic response. The insights gained in our research can help to develop new treatments that have an effect on epithelial cells and that influence the immune response via the mucosa. In addition new tools can be developed for the diagnosis of allergy in young children.

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Nederlandse Samenvatting

Allergie is een veelvoorkomende ziekte in de westerse samenleving, in de Verenigde Staten zelfs meer dan 20%. Patienten met allergische ontsteking van het neusslijmvlies (rhinitis) leiden aan symptomen zoals een loopneus of een verstopte neus, jeuk aan de neus of ogen, niezen, en zelfs reuk en gehoorverlies. Ondanks dat deze symptomen niet dodelijk zijn leiden ze er wel toe dat het algehele welbevinden van de patienten verminderd is en dat hun functioneren op het werk er onder kan leiden, de kosten die met deze ziekte gepaard gaan zijn dan ook enorm; geschat is dat afwezigheid en verlaagde productiviteit door allergie in 1998 aan amerikaanse bedrijven 250 miljoen dollar gekost heeft. Dit is nog maar een fractie van de totale kosten, aangezien de kosten voor gezondheidszorg gerelateerd aan allergische rhinitis zelfs meer dan 6 miljard dollar was in 1996.

Waar onderzoek op het gebied van allergie zich altijd gericht heeft op cellen van het adaptieve immuunsysteem zoals witte bloedcellen zijn wij ons in ons onderzoek gaan richten op de epitheelcellen. Epitheelcellen zijn de buitenste laag cellen in het neusslijmvlies, die de binnenkant van de neus bekleden, en als buitenste laag spelen zij een belangrijke rol als barriere, ervoor zorgend dat onderliggende weefsels en cellen afgeschermd worden van kwalijke invloeden van buitenaf. Ze zijn echter meer dan een fysieke barriere, ze kunnen namelijk zelf ook reageren op allergenen en ziekteverwekkers. In reactie hierop produceren ze bijvoorbeeld chemokines en cytokines, dit zijn stoffen waar cellen mee met elkaar kunnen communiceren. De vraag in ons onderzoek was of deze cellen betrokken zijn bij allergische reacties, en als ze dat zijn wat dan hun rol hierin is. Verschillen tussen epitheelcellen van gezonde en allergische mensen kunnen ons hierover veel leren. Als de functie van het epitheel het imuunsysteem beinvloed is dit een aangewezen plek om in te grijpen, bijvoorbeeld door middel van een neusspray.

In hoofdstuk 1 beschrijven we drie families van receptoren die op

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epitheelcellen aanwezig zijn en die een rol spelen in de reactie tegen allergenen en pathogenen. Deze drie groepen zijn Toll Like Receptoren, NOD Like Receptoren en Protease Geactiveerde Receptoren, ook beschrijven we hoe deze receptoren in de cel signaleren als de receptor wordt geprikkeld.

In hoofdstuk 2 hebben we gekeken naar de reactie van luchtweg epitheel cellen op blootstelling aan huisstofmijt allergeen. In deze cellijn zagen we dat veel genen hun expressie verhogen na allergeen blootstelling. Veel van deze genen spelen een rol in communicatie tussen verschillende cellen, hiertoe behoren ook de chemokines en cytokines. We hebben ook genen gevonden die betrokken zijn bij andere processen zoals immuniteit en verdediging, receptor activiteit, en enzym remmer activiteit. Als we een netwerk analyse doen kunnen we de beschreven interacties tussen alle genen zien waar de expressie van verhoogd is. Hier vonden we een centrale rol voor TNF-α, een cytokine dat een belangrijke rol speelt in de regulatie van witte bloedcellen.

In hoofdstuk 2 hebben we laten zien hoe luchtwegepitheel cellen reageren op allergeen blootstelling, in hoofdstuk 3 hebben we gekeken naar neus epitheelcellen die direct afkomstig zijn van patienten of gezonde vrijwilligers. Ook hier zagen we een verhoging van expressie van genen, echter hoofdzakelijk in de gezonde controles. Als we naar de expressieniveaus van die genen kijken in allergische patienten zien we dat deze al een hoog niveau hebben en mogelijk daardoor hun expressie niet verder kunnen verhogen, dit noemen we de geactiveerde staat. Van de genen die hier veranderd zijn is bekend dat ze een rol spelen in communicatie tussen cellen, signaal transductie en transcriptie factor activiteit. Ook hier hebben we netwerk analyse gedaan waaruit bleek dat de geactiveerde staat van de signaal moleculen ook terug te zien was in transcriptiefactoren, de regulatoren van genexpressie.

Nadat we hebben laten zien dat zowel een luchtweg epitheelcellijn als primaire neusepitheelcellen reageren op allergeen, zijn we gaan kijken naar de overeenkomsten tussen de luchtweg epitheelcellijn beschreven in hoofdstuk

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2 en primaire neusepitheelcellen beschreven in hoofdstuk 3. In hoofdstuk 4 hebben we gekeken of een cellijn gebruikt kan worden als model om de epitheliale respons op allergenen te bestuderen. Uit onze vergelijking bleek dat de cellijn meer overeenkomsten had met het gezonde primaire epitheel dan met dat van allergische individuen. Daarnaast is het aantal genen dat in de cellijn veranderd vele malen groter dan dat in primaire cellen. Voor het bestuderen van de respons na blootstelling aan allergeen kun je dus een cellijn gebruiken, echter zullen de verschillen die door allergie worden veroorzaakt hierin niet terug te vinden zijn.

Het allergeen wat we hebben gebruikt in de experimenten in hoofdstukken 2, 3 en 4 bevat proteolytische enzymen die door het knippen van de receptor deze kunnen activeren. In hoofdstuk 5 hebben we gekeken naar de respons van epitheelcellen op een enkel proteolytisch enzym, wat ertoe leidt dat cellen uit de epitheellaag loslaten. Hier zagen we dat als het loslaten gebeurd in aanwezigheid van een proteolytisch enzym dit tot een veel sterkere respons leidt dan wanneer je dit op een niet enzymatische manier doet.

Een rode draad in de respons van epitheelcellen is dat ze cytokines en chemokines produceren als reactie op blootstelling aan proteolytische allergenen. Van deze stoffen is bekend dat ze niet alleen een effect hebben op cellen van het immuunsysteem maar dat ze ook weer de epitheelcellen kunnen beïnvloeden. In hoofdstuk 6 hebben we gekeken naar de respons van epitheelcellen op de proinflammatoire cytokines TNF-α and IL-17. We zagen dat deze twee cytokines een samenwerkend effect kunnen hebben op de productie van sommige andere cytokines. Erg opvallend was dat het synergistisch effect op IFN-γ alleen te zien was in epitheel van gezonde individuen.

Tot slot bespreken we in hoofdstuk 7 hoe we met dit onderzoek beter inzicht in het proces van allergie hebben gekregen, met name de bijdrage die epitheelcellen daarin hebben. Ook zijn nu de verschillen tussen epitheel van allergische en gezonde allergenen blootgelegd. Deze verschillen wijzen naar

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verschillende mechanismen van regulatie die invloed kunnen hebben op de allergische respons. De verschillen in respons en regulatie daarvan openen nieuwe mogelijkheden voor het ontwikkelen van medicatie die een effect heeft op de epitheelcellen en die via de neusslijmvlieslaag de allergische ontsteking kan onderdrukken. Ook kunnen deze verschillen gebruikt worden voor het ontwikkelen van diagnostische methoden om allergie bij jonge kinderen vast te stellen, aangezien de huidige methoden niet altijd even betrouwbaar zijn.

Met dit onderzoek zijn we een stap dichterbij gekomen bij het begrijpen van allergie, en in het bijzonder de rol die epitheelcellen spelen hierin, dit begrip kan van belang zijn bij het ontwikkelen van nieuwe medicatie voor de behandeling van allergie.

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Bibliography

Vroling AB, Fokkens WJ, van Drunen CM. How epithelial cells detect danger: aiding the immune response. Allergy. 2008 Sep;63(9):1110-23.

Vroling AB, Jonker MJ, Breit TM, Fokkens WJ, van Drunen CM. Comparison of expression profiles induced by dust mite in airway epithelia reveals a common pathway. Allergy. 2008 Apr;63(4):461-7.

Vroling AB, Duinsbergen D, Fokkens WJ, van Drunen CM. Allergen induced gene expression of airway epithelial cells shows a possible role for TNF-alpha. Allergy. 2007 Nov;62(11):1310-9.

Vroling AB, Jonker MJ, Luiten S, Breit TM, Fokkens WJ, van Drunen CM. Primary nasal epithelium exposed to house dust mite extract shows activated expression in allergic individuals. Am J Respir Cell Mol Biol. 2008 Mar;38(3):293-9.

van Drunen CM, Vroling AB, Rinia AB, Fokkens WJ. Considerations on the application of microarray analysis in rhinology. Rhinology 2008 Dec;46(4): 259-66

Vroling AB, Duinsbergen D, Fokkens WJ, van Drunen CM. Epithelial cells show a pleiotrope mediator response as a consequence of cell-cell contact disruption. Submitted.

Vroling AB, Snoek A, Luiten S, Fokkens WJ, Lutter R, van Drunen CM. A strongly reduced synergistic response to TNF-α and IL17 detected for the Th1 cytokine INF-γ in primary nasal epithelial cells from allergic individuals. Submitted.

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Dankwoord

Dit dankwoord zou hier niet hebben gestaan als er niet een hele groep mensen achter me hadden gestaan en me gesteund hadden de afgelopen jaren en dit is de uitgelezen kans om deze mensen te laten weten dat hun steun al die jaren erg fijn gevonden is.

Als eerste natuurlijk een plek voor mijn promotor en mijn co-promotor. Wytske en Kees, zonder jullie had ik deze mogelijkheid niet gehad om dit onderzoek te beginnen, en ook niet geheel onbelangrijk is de rol die jullie hebben gespeeld bij vervolbrengen van dit geheel. Wytske jou gedrevenheid en kees jouw nauwgezetheid hebben een duidelijk stempel achtergelaten op dit werk.

Dirk, samen begonnen wij op het lab van de KNO, en als twee musketiers stonden wij daar alles uit te zoeken en op te zetten, wat ons de bijnaam Jut en Jul opleverde. Al snel werd de groep groter en iedereen die binnenkwam leerde van ons de kneepjes van het vak. Je bent alweer een tijdje ergens anders aan de gang, maar jouw inzet heeft zeker in het begin een hoop mogelijk gemaakt. Laten we dan nu voor eens en altijd afspreken dat ik Jut en jij Jul bent!

Jan dit is het zoveelste dankwoord waar je in genoemd wordt en dat is niet voor niets, als er wat geregeld moest worden dan stond jij klaar, en dat is tot op heden niet anders. Jij was een groot voorstander van ontspanning na(ast) het werk, wat nogal eens resulteerde in een kater op vrijdag. Altijd had jij een luisterend oor, maar jij zorgde er ook voor dat er dan wat gebeurde! Jan je bent een prachtvol mens!

Natuurlijk mijn collega’s van het KNO-clubje uit het heden en verleden, Inge, Rob, Silvia, Danielle, Esther, Erik, Alinda. Met jullie samenwerken was altijd erg prettig, en ik hoop dat met jullie hulp nog vele promovendi hun werk zullen afronden. Ook mijn collega promovendi Fenna, Ward, Bas, Susanne, Barbara en Joost, bedankt voor jullie hulp en steun en alle gezellige avonden op de congressen, ik kijk ernaar uit ook jullie verdediging bij te wonen.

Joke, nu val je weer buiten alle categorieën. Zelden een collega gehad

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met zo’n grote mond en zo’n klein hartje. Hopelijk kom je op het feestje weer met wat ongepaste anekdotes.

Natuurlijk waren we op het KNO lab niet alleen, we mochten de ruimte (en kennis) delen met onze buren, de mensen van cellulaire immunologie, Martien, Esther, Eddy, Astrid, Sonja, Madelon, Esther, Toni, Femke, Robert, Erwin en Joost, jullie hulp is van grote waarde gebleken.

Dit allemaal gebeurde op de afdeling celbiologie en histologie, waar Ron de scepter zwaait, en waar Trees en Irene hem bijstaan. Ook jullie wil ik graag bedanken voor de steun en het vertrouwen dat jullie hebben gehad in de goede afloop.

Dan stappen we over naar de VU, waar ik een nieuwe plek gevonden heb, en met open armen ontvangen ben, en ondanks dat jullie aan de inhoud van het proefschrift geen deelname hebben gehad, hebben jullie wel alle verhalen over de afronding met begrip moeten aanhoren. Georg, Tanja, Reina, Joke, Ramon, Brenda, Ida, Gera, Joost, Jasper, Antonio, Anna, Rosalie, Toon, Ellen, Tom, Serge, Erik, Ronald, en Marlene. Ik hoop nog veel van jullie te leren. Mascha, zonder jou zou dit proefschrift waarschijnlijk nog vol typefouten staan, ik hoop dat er nog veel manuscripten zullen komen waar we samen aan gaan puzzelen.

En dan natuurlijk als laatste maar niet als minste mijn familie en vrienden. Een luisterend oor, even ontspannen, maar ook altijd een zetje op het juiste moment. Mam, Welmoet, Bart, Sonja, Mando en Mirthe, er is geen familie waar ik beter in thuis pas als deze.

Marijn, je hebt bijna tot het eind naast me gestaan tijdens deze promotietijd, en daarvoor wil ik je bedanken. En verder natuurlijk al mijn vrienden en vriendens beminden, Jorn, Judith, Jasper, Gera, Martin, Els, Stephan, Marije, Japio, Paul, Rolf, Kaatje en iedereen die ik nu vergeet in dit lijstje.

Ik denk dat ik nu iedereen wel bedankt heb, maar mocht ik je vergeten zijn dan is dat volledig onopzettelijk, en dan wil ik je bij deze alsnog bedanken. En om af te sluiten zou ik willen zeggen: het zit erop!

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Curriculum vitea

Aram Ben Vroling was born on June 7th 1976 in Alkmaar, The Netherlands. From 1988 to 1994 he attended the HAVO at the “christelijke scholengemeenschap Jan Arentsz”, where he continued his high school education (atheneum) and graduated in 1996. In the same year he started his study biology at the University of Leiden , where he received his master’s degree in 2002. In January 2003 he started as a PhD student at the department of Otorhinolaryngology at the Academic Medical Center of the University of Amsterdam under the supervision of dr. C.M. van Drunen and prof. dr. W.J. Fokkens. During his PhD the role of nasal epithelial cells in allergy was studied, in particular to proteolytic allergens. All results of this study form the content of this thesis. In august 2007 he started in a Postdoctoral research position at the VU University Medical Center under the supervision of prof. dr. G. Kraal where he is currently studying the role of protease inhibitors in immune activation.