DIFFERENTIAL EXPRESSION BETWEEN CF HUMAN AIRWAY EPITHELIAL CELL LINES AND NORMAL CELLS Grégory 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

Découvrir les processus biologiques engagés dans une cellule CF. Notre Problématique Quelles 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.

Introduction 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.

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

METHODOLOGY 2 cell lines Nuli cells: RNA extraction hybridation GeneChip® Affymetrix Nuli cells: Normal Lung, University of Iowa Derived from HAE of normal genotype. Pangenomic Chips HGU133.plus.2.0 54,000 probesets 47,000 transcrits ( 38,500 well-known genes) RNA extraction hybridation Cufi cells: Cystic Fibrosis, University of Iowa derived from HAE of CF genotype (homozygote ∆F508) Scanning by bioanalyzer EXPERIMENT DESIGN 3 biological replicates 2 experimental conditions: Cufi and Nuli 2 cell lines immortalized by Dr. Joseph Zabner (ref 1)

Ontological Analysis: Global observation: Number of DEGs UP and DOWN Confidence level Adjusted Pvalues Data acquisition Pathway-express (ref 4) Pathway Analysis: Determine the overexpressed Signaling Pathway in an interest group of DEGs CEL files Onto-express (ref 4) Ontological Analysis: Determine the overexpressed Gene Ontology (GO) in an interest group of DEGs Normalization by RMA express List of DEGs Bioconductor version 1.8 Statistical analysis based on a linear model. DEGs ordered by Bayesian Statistic, which Represents the probability of expression in the context of our experiment. Statistical analysis with Bioconductor package AffyLM (ref 3)

Selection of interesting DEGs Confirmation of expression by Q-PCR Validation of over/under expression obtained by microarray analysis Confirmation of protein expression In Progress... Confirmation of pathway activation Pathway inhibition

Results 2335 PROBESETS differentially expressed Ontological analysis: 0.01<Adjusted Pvalues<10-13 0.5<Expression probability<1 0.006<Expression Ratio <19 2335 PROBESETS differentially expressed 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) 1659 annotated differentially expressed GENES 788 DEGs UP-regulated 871 DEGs DOWN-regulated + 202 genes NA + 474 duplicate gene annotations 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

Les paramètres de l’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

... 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

1296 probesets down-regules Analyse ontologique…. ...sur l’ensemble des 1296 probesets down-regules ...sur l’ensemble des probesets modulés ...sur l’ensemble 1039 probesets up-regules METABOLISME: lipid metabolism protein biosynthesis MODIFICATION DE PROTEINE: protein amino acid phosphorylation Proteine byosynthese TRANSCRIPTION: TRANSPORT electron transport MECANISME DE DEFENSE: immune response inflammatory response COMMUNICATION CELLULAIRE: cell-cell signaling chemotaxis. cell adhesion METABOLISME: lipid metabolism protein biosynthesis VOIE DE SIGNALISATION TRANSPORT electron transport MECANISME DE DEFENSE: immune response inflammatory response COMMUNICATION CELLULAIRE: cell-cell signaling chemotaxis cell adhesion METABOLISME: lipid metabolism SIGNAL DE TRANSDUCTION: Cell surface receptor linked signal transduction Positive regulation of I-kappa b Intracellular signaling cascade.

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.

Les paramètres de l’analyse 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

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 action d’un ligand exogène (endogène?) sur un récepteur spécifique. amplification du signal cellulaire spécifique et sensible. production de molécules spécifiques: interleukines et cytokines, CD, interférons. 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 analysis in Toll-Like Receptor Signaling Pathway NULI CUFI

RATIO PCR 3,6 4,7 5,4 5,5 4,9 0,002 0,0004 0,01 0,028 0,02 P-value PCR RATIO MICROARRAY 4 2 17 9 18 EXPRES. PROBAB. MICROARRAY >95 % >95 % >95 % >95 % >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 nuli: cellule contrôle cufi: cellule CF

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