DIFFERENTIAL EXPRESSION BETWEEN CF HUMAN AIRWAY EPITHELIAL CELL LINES AND NORMAL CELLS Grégory Voisin, Chantal Massé, André Dagenais, Sébastien Lemieux.

DIFFERENTIAL EXPRESSION BETWEEN CF HUMAN AIRWAY EPITHELIAL CELL LINES AND

NORMAL CELLSGrégory Voisin, Chantal Massé, André Dagenais, Sébastien Lemieux and Yves Berthiaume,

Institute for Research in Immunology and Cancer, Department of Computer Science and Operation Research , Research Center, CHUM, Université de Montréal, Montréal QC, Canada.

Breathe Project

Notre ProblématiqueQuelles sont les gènes différentiellement

exprimés entre des cellules CUFI (CF) et NULI (Non CF)?

Découvrir les processus biologiques engagés dans une cellule CF.

Découvrir le disfonctionnement du pathway impliqué dans la reponse inflammatoire.

• Several microarray studies using primary cells or model transgenic mouse have been carried out to understand the disregulation in CF cells.

• To date, no such studies have been performed with cell lines of Human Alveolar Epithelial(HAE).

• Using cell lines we reduce the biological variation, an important element in interpretation of microarray analysis.

• Althought there are many heterogen cell models, all studies come to the same global conclusion: modulation of inflammatory actors in CF cells.

• In this work, we present several characteristics of an excessive inflammatory response.

Introduction

Abstract

The relationship between the basic defect and the presence of chronic lung infection and inflammation in CF lungs remains unclear. Although it has been suggested that the basic defect leads to an enhanced secretion of inflammatory mediators by epithelial cells, the mechanisms leading to this enhanced inflammatory response has not been identified. One possible hypothesis is that the basic defect modulates the expression of inflammatory mediators in human airway epithelial (HAE) cells.

To study this question, a gene profiling study was performed on two HAE cell lines: Nuli cells with wild-type CFTR and Cufi cells homozygous for DF508 mutation. A microarray experiment was conducted employing Affymetrix pangenomic HGU133 plus 2.0 chip, which contains more than 54,000 probe sets. Total RNA were isolated from 3 different plates of Nuli and Cufi cells cultured at air-liquid interface for 4 weeks. Gene profiling of the two cell lines was compared using a linear model in Bioconductor (version 1.7). Based on the expression probability issued by Bayesian statistics, 2,335 mRNAs were differently expressed between Cufi and Nuli cells. Although there was up-regulation in Cufi cells of two alternative Cl- channels, such as CLCA2 (4X) and CLCA4 (6X), 30% of the top 77 upregulated genes were involved in the inflammatory response or immune response, such as IL-8, CXCL11 and IL-6 that increased by 5-, 18- and 22-fold changes respectively. These results clearly show a basic upregulation of genes involved in the inflammatory response in Cufi cells. Ontology and pathway analyses of modulated genes between the two cell lines confirmed activation of the inflammatory response in Cufi cells. Two signalling pathways that might be involved in the inflammatory response in CF were identified by these analyses: the toll-like receptor cascade which is involved in the signalling response of many inflammatory cytokines, and the Jak/Stat cascade, one of the signal transduction pathways for IL-6. These results are similar to those reported in CFTR knock-out mice (Xu et al., JBC 278 7674, 2003) for the expression of pro-inflammatory genes, but reveal some differences from similar analysis conducted on primary cultures of CF and non-CF HAE cells (Zabner J. et al. , AJPL 289 , 2005).

In conclusion, gene profiling is an interesting tool to identify the regulatory mechanisms of the inflammatory response and provides a better understanding of CF pathophysiology.

GENE EXPRESSION PROFILING OF CF HUMAN AIRWAY EPITHELIAL CELL LINES SUGGESTS THAT CF IS ASSOCIATED WITH AN INTRINSIC INFLAMMATORY RESPONSE.

2 cell lines

2 cell lines immortalized by Dr. Joseph Zabner (ref 1)

Nuli cells:Normal Lung, University of Iowa Derived from HAE of normal genotype.

Cufi cells: Cystic Fibrosis, University of Iowaderived from HAE of CF genotype (homozygote ∆F508)

RNA extraction

hybridation

EXPERIMENT DESIGN

3 biological replicates2 experimental conditions: Cufi and Nuli

GeneChip® Affymetrix

Pangenomic Chips HGU133.plus.2.054,000 probesets47,000 transcrits ( 38,500 well-known genes)

METHODOLOGY

Scanning by bioanalyzer

Data acquisition

CEL files

Normalizationby RMA express

Statistical analysiswith Bioconductor

package AffyLM(ref 3)

Bioconductor version 1.8Statistical analysis based on a linear model. DEGs ordered by Bayesian Statistic, whichRepresents the probability of expression inthe context of our experiment.

Pathway Analysis:Determine the overexpressed

Signaling Pathway in an interest group of DEGs

List of DEGs

Global observation:Number of DEGs UP and DOWN

Confidence levelAdjusted Pvalues

Ontological Analysis:Determine the overexpressed

Gene Ontology (GO) in an interest group of DEGs

Onto-express(ref 4)

Pathway-express(ref 4)

Selection ofinteresting DEGs

Confirmation of expression by Q-PCRValidation of over/under expression

obtained by microarray analysis

Confirmation of protein expression

Pathway inhibition

Confirmation of pathway activation

In Progress...

Results

2335 PROBESETS differentially expressed

1659 annotated differentially expressed GENES

788 DEGs UP-regulated

871 DEGs DOWN-regulated

0.01<Adjusted Pvalues<10-130.5<Expression probability<10.006<Expression Ratio <19

202 genes NA

+

474 duplicate gene annotations

+

Ontological analysis:UP-regulation of inflammatory response, immune response, cell adhesion,chemotaxis:(IL6, IL8, SPINK5, CXCL10,CXCL11, 2,3,5,6, IFIT1,3,IL1R2,TNFAIP6,S100A12, MCAM, SRPX, AREG, CD36..)UP-regulation of transport:SLC6A14, CLCA2,4, CYP24A1, KCNE3,VIM

Modulation of protein biosynthesis(EIF1AY, RPS23)) and lipid metabolism: (ACOX1, ACOX2..)

Down-regulation of the transport electron (ALOX5,15B, CLCN4, KCNK5)

Pathway analysis:Activation of Toll-Like Receptor pathway

Que nous apprend les gènes les 100 probesets plus modulés dans les Cufi par rapport au Nuli?

• Première observation: il y a plus d’annotation d’AFFYMETRIX dans les 100 probesets UP-régulés que dans les gènes DOWN-régulés: plus de connaissance dans les gènes UP- régulés.

• Seconde observation: Fonction clairement prédominante dans les gènes UP - régulés : réponse immunitaire:IL6 (X22), IL8 (X4), SPINK5, CXCL10(x12), CXCL1, 2,3,5,6, IFIT1,3,IL1R2,TNFAIP6, S100A12. signal cellulaire: MCAM, SRPX, AREG, CD36. transport:SLC6A14, CLCA2,4, CYP24A1, KCNE3,VIM.

• Troisième observation: Fonction “prédominante” dans les gènes DOWN - régulés : régulation de la transcription: ID4, TFCP2L1, RPS6KA5 réponse au stress: SEPP1, GSTT1, protéine biosynthèse: EIF1AY, RPS23 transport: ALOX5,15B, CLCN4, KCNK5, réponse immune:HLA

Analyse ontologique

• BUT: déterminer les processus biologiques (GO biological function) dominants représentés dans les DEGs (Differential

Expressed Gene)

• OUTIL: ONTO EXPRESS (Sorin Draghici)

• PARAMETRES UTILISÉS: distribution hypergéométrique.

puce de reference :HU133.2.0.plus

Pvalue adjustée par BH.

cut-off: 0.001

au moins 10 membres

Les paramètres de l’analyse ontologique

... sur l’ensemble des probesets modulés

… sur l’ensemble des 1296 probesets down régulés dans les CUFI par rapport au NULI

... sur l’ensemble 1039 probesets up-régulés dans les CUFI par rapport au NULI

Analyse ontologique….

...sur ...sur l’ensemblel’ensemble des probesets des probesets

modulésmodulés

...sur l’ensemble des...sur l’ensemble des1296 probesets down-regules1296 probesets down-regules

...sur l’ensemble...sur l’ensemble 1039 probesets1039 probesets up-regulesup-regules

MECANISME DE DEFENSE:immune responseinflammatory response

COMMUNICATION CELLULAIRE:cell-cell signalingchemotaxiscell adhesionMETABOLISME:lipid metabolism

SIGNAL DE TRANSDUCTION:Cell surface receptor linked signal transduction Positive regulation of I-kappa bIntracellular signaling cascade.

METABOLISME:lipid metabolismprotein biosynthesis

MODIFICATION DE PROTEINE:protein amino acid phosphorylationProteine byosyntheseTRANSCRIPTION:

TRANSPORTelectron transport

MECANISME DE DEFENSE:immune responseinflammatory response

COMMUNICATION CELLULAIRE:cell-cell signalingchemotaxis.cell adhesion

METABOLISME:lipid metabolismprotein biosynthesis

VOIE DE SIGNALISATION

TRANSPORTelectron transport

Quelques conclusions sur l’analyse de l’ontologie.

• Les1296 probesets down-régulés ne rassemblent pas forcément plus de GO significatifs que le up régulés (1039 probesets).

• Le manque d’annotation implique un biais dans une interprétation biologique (il faut en avoir conscience tout le long de l’analyse).

• Up régulation des mécanismes de la réponse immunitaire au sens large.

• Up régulation de voies de signalisation • Modulation du métabolisme: lipide et proteïne• Modulation du GO TRANSPORT.

Intégration des données d’expression dans des voies métaboliques.

• BUT: déterminer les voies métaboliques significatives

pouvant intégrer nos DEGs .

• OUTIL: PATHWAY EXPRESS(Sorin Draghici)

• PARAMETRES UTILISÉS: puce de reference :HU133.2.0. plus

Pvalue adjustée par BH

cut-off: 0.001

au moins 10 membres

Les paramètres de l’analyse des voies métaboliques

2 voies métaboliques significatives

• Toll-like receptor Pathway (path: hsa04620 ):

Pvalue adjustée: 8.231x10e-3

• Jak/Stat signaling pathway (path: hsa04630 ):

Pvalue adjustée: 3.285x10e-4

L’expression de nos micro-array dans la voie TLR signaling pathway

Toll-like receptor Pathway(selon Kegg)

En décomposant la voie TLR

1. action d’un ligand exogène (endogène?) sur un récepteur spécifique.

2. amplification du signal cellulaire spécifique et sensible.

3. production de molécules spécifiques: interleukines et cytokines, CD, interférons.

4. mise en place d’un mécanisme biologique “orienté”: apoptose, effet pro-inflammatoire, chemotaxisme…

Toll-like receptor Pathway

• Caractéristiques:

Très conservée entre les espèces (on

peut penser qu’intra espèce ce soit la même chose)

• Rôle: fournit un signal intra-cellulaire /inter-cellulaire spécifique et sensible

• Production: de interleukine , cytokine , marqueur cellulaire.

Expression Ratio Q-PCR analysisin Toll-Like Receptor Signaling Pathway

expression de STAT2Pvalue = 0.03 , ratio 1.8

0.0000

0.2000

0.4000

0.6000

0.8000

1.0000

1.2000

1.4000

Nuli-CTL1 Nuli-CTL2 Nuli-CTL3 Cufi-CTL1 Cufi-CTL2 Cufi-CTL6

Expression de IL1b par PCRp=0.0004 ratio = 3,6

0

0.5

1

1.5

2

2.5

3

3.5

4

Nuli-CTL1 Nuli-CTL2 Nuli-CTL3 Cufi-CTL1 Cufi-CTL2 Cufi-CTL6

Expression d'IL6p=0,01 , ratio= 5.4

0

1

2

3

4

5

6

Nuli-CTL1 Nuli-CTL2 Nuli-CTL3 Cufi-CTL1 Cufi-CTL2 Cufi-CTL6

Expression de IL8Pvalue= 0,002

0

0.5

1

1.5

2

2.5

3

Nuli-CTL1 Nuli-CTL2 Nuli-CTL3 Cufi-CTL1 Cufi-CTL2 Cufi-CTL6

EXPRESSION DE CXCL10p=0.028, ratio= 5,5

0.0000

1.0000

2.0000

3.0000

4.0000

5.0000

6.0000

7.0000

Nuli-CTL1 Nuli-CTL2 Nuli-CTL3 Cufi-CTL1 Cufi-CTL2 Cufi-CTL6

expression de CXCL11p=0,01 , ratio = 4,9

0

1

2

3

4

5

6

Nuli-CTL1 Nuli-CTL2 Nuli-CTL3 Cufi-CTL1 Cufi-CTL2 Cufi-CTL6

expression de STAT1Pvalue = 0.006 , ratio= 2.1

0.0000

0.5000

1.0000

1.5000

2.0000

2.5000

3.0000

Nuli_CTL1 Nuli_CTL2 Nuli_CTL3 Cufi_CTL1 Cufi_CTL2 Cufi_CTL6

expression de C-FOSpvalue = 0.63, ratio = 0.9

0.0000

0.2000

0.4000

0.6000

0.8000

1.0000

1.2000

Nuli-CTL1 Nuli-CTL2 Nuli-CTL3 Cufi-CTL1 Cufi-CTL2 Cufi-CTL6

expression de MYD88pvalue= 0,88 , ratio =1,1

0.0000

0.2000

0.4000

0.6000

0.8000

1.0000

1.2000

Nuli-CTL1 Nuli-CTL2 Nuli-CTL3 Cufi-CTL1 Cufi-CTL2 Cufi-CTL6 expression de MAPK8pvalue = 0.6 , ratio = 0.9

0.0000

0.2000

0.4000

0.6000

0.8000

1.0000

1.2000

Nuli-CTL1 Nuli-CTL2 Nuli-CTL3 Cufi-CTL1 Cufi-CTL2 Cufi-CTL6

CUFINULI

Expression of confirmation by Q-PCR

0

1

2

3

4

5

6

7

Nuli Cufi Nuli Cufi Nuli Cufi Nuli Cufi Nuli Cufi

IL8 IL1B IL6 CXCL10 (IP-10) CXCL11(ITAC)

RATIO PCR 3,6

EXPRES. PROBAB.MICROARRAY

0,002

RATIO MICROARRAY

P-value PCR

4

>95 %

4,7

0,0004

2

>95 %

5,4

0,01

17

>95 %

5,5

0,028

9

>95 %

4,9

0,02

18

>50%

L’expression de nos micro-array dans la voie jak-stat signaling pathway

Confirmation par QPCR• Famille des SOC testé: 1,2,3,4,5t1,5t2

Up-regulation de SOC1 :Pvalue= 0,0006,ratio = 5.

Up-regulation de SOC2 :Pvalue= 0,07, ratio = 1,8.

• Famille des PIAS: 1,2,3,4,

UP-régulation de PIAS 4: Pvalue= 0,05, ratio = 1,4.

• Famille des Stat: 1,2

• UP-regulation de Stat2:Pvalue= 0,03 , ratio = 1,8.

Expression de quelques canaux ioniques par QPCR

expression de SCNN1BPvalue = 0.07 ratio = 0.66

0.0000

0.2000

0.4000

0.6000

0.8000

1.0000

1.2000

Nuli-CTL1 Nuli-CTL2 Nuli-CTL3 Cufi-CTL1 Cufi-CTL2 Cufi-CTL6

expression de SCNN1GPvalue = 0.15 , ratio = 0.67

0.0000

0.2000

0.4000

0.6000

0.8000

1.0000

1.2000

Nuli-CTL1 Nuli-CTL2 Nuli-CTL3 Cufi-CTL1 Cufi-CTL2 Cufi-CTL6

Expression de CLCA2pvalue = 0.14 , ratio = 1.3

0.0000

0.2000

0.4000

0.6000

0.8000

1.0000

1.2000

Nuli-CTL1 Nuli-CTL2 Nuli-CTL3 Cufi-CTL1 Cufi-CTL2 Cufi-CTL6

expression de CLCA4pvalue = 0.0001 , ratio = 4,7

0.00000.5000

1.00001.5000

2.00002.50003.0000

3.50004.0000

4.50005.0000

Nuli-CTL1 Nuli-CTL2 Nuli-CTL3 Cufi-CTL1 Cufi-CTL2 Cufi-CTL6

expression de KCNK5pvalue = 0.003 , ratio = 0.48

0.0000

0.2000

0.4000

0.6000

0.8000

1.0000

1.2000

Nuli-CTL1 Nuli-CTL2 Nuli-CTL3 Cufi-CTL1 Cufi-CTL2 Cufi-CTL6

nuli: cellule contrôlecufi: cellule CF

Le winner:GSST1• GSST1:glutathione S-transferase theta 1 • Down regulé dans les microarray et en

QPCR.

expression de GSTT1

0,0000

0,2000

0,4000

0,6000

0,8000

1,0000

1,2000

Nuli-CTL1 Nuli-CTL2 Nuli-CTL3 Cufi-CTL1 Cufi-CTL2 Cufi-CTL6

Glutathione S-transferase (GST) theta 1 (GSTT1) is a member of a superfamily of proteinsthat catalyze the conjugation of reduced glutathione to a variety of electrophilic and hydrophobic compounds

Expériences futures• Activité de NF-kB entre Cufi et Nuli +/-

LPS +/- Myd88

• Expression protéique des acteurs de l’inflammation (proteine array) entre cufi et Nuli +/- LPS et +/- Myd88.

• Phosphorylation de STAT1 et 2 entre cufi et Nuli +/- LPS.

• Translocation de STAT dans le noyau

Conclusion• Up-regulation de nombreux acteurs de

l’inflammation :IL6, IL8,IL1b, CXC10, CXCL11... en absence d’elements pathogenes.

• Activation potentiel d’une voie de signalisation.• Regulation d’un certain nombre de mRNA d’interet:

STAT1,2 ; PIAS 4; SOC1,2: influencent les voies de signalisation.

Modulation de l’expression de quelques canaux ioniques: CLCA4, KCNK5.

Down regulation de GSTT1.