BETWEEN CF HUMAN AIRWAY AND NORMAL CELLS Institute for Research in Immunology and Cancer, Department of Computer Science and Operation Research, Research Center, CHUM, Université de Montréal, Montréal, QC, Canada. Massé, André Dagenais, Sébastien Lemieux

DIFFERENTIAL EXPRESSION Breathe Project Grégory Voisin, Chantal

EPITHELIAL CELL LINES and Yves Berthiaume

Previous microarray studies in CF have been conducted in :  Primary cells ( Δ F508/ Δ F508) : Zabner, Wright.  Transgenic mouse model (CFTR(-/-)) : Xu, Radzioch.  Human Airway Epithelial (HAE) cell lines (ΔF508/W1282X) : Virella-Lowell. Although all of these models are heterogeneous, these studies come to the same global conclusion : There is a modulation of inflammatory actors in CF cells. So far, no relation has been shown between possible metabolic pathways and modulated genes. 1-Introduction

2-Hypothesis CFTR deficiency in HAE cells triggers specific pathways involved in the inflammatory response.

Goal of the present microarray study:  Carry out a differential expression analysis on a CF (ΔF508/ ΔF508) and a non-CF human airway epithelial cell lines.  Determine the biological processes modulated in CF.  Determine the metabolic pathways modulated in CF. 3-Specific Objectives

2 cell lines immortalized by Dr. Joseph Zabner. The study was carried out on cell lines to reduce biological variability and allows a higher confidence in microarray analysis and interpretation. Nuli cells: Normal Lung, University of Iowa Derived from HAE of normal genotype. Cufi cells: Cystic Fibrosis, University of Iowa derived from HAE of CF genotype (homozygote ∆F508) RNA extraction hybridation EXPERIMENT DESIGN 3 biological replicates 2 experimental conditions: Cufi and Nuli GeneChip® Affymetrix Pangenomic Chips HGU133.plus ,000 probesets 47,000 transcrits ( 38,500 well-known genes) 4-Methology Scanning by bioanalyzer

Data acquisition Normalization by RMA express Statistical analysis with Bioconductor (AffyLM package) Bioconductor version 1.8 Statistical analysis based on a linear model. Differential Expressed Genes (DEGs) ordered by Bayesian statistic, which represents the probability of expression in the context of our experiment. CEL files

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 level Adjusted Pvalues Ontological Analysis: Determine the overexpressed Gene Ontology (GO) in an interest group of DEGs Pathway-express Onto-express

Selection of interesting DEGs Confirmation of expression by qPCR Validation of over/under expression obtained by microarray analysis Confirmation of protein expression Pathway inhibition Confirmation of pathway activation In progress...

5-Results 2335 PROBESETS differentially expressed 1659 annotated differentially expressed GENES 788 DEGs UP-regulated 871 DEGs DOWN-regulated 202 genes NA duplicate gene annotations + Range values: 0.01<Adjusted Pvalues<10℮ <Expression probability< <Expression Ratio <19

Results: Gene ontology and Pathway analysis. Gene Ontology: 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 lipid metabolism: ACOX1, ACOX2 Down-regulation of the transport electron: ALOX5,15B, CLCN4, KCNK5 Pathway analysis: Activation of Toll-Like Receptor pathway

Results: Modulated genes in the Toll-like Signaling Pathway

Results: microarray modulated genes in Toll-like Signaling Pathway.

RATIO qPCR 3,6 EXPRES.PROBAB. MICROARRAY 0,002 RATIO MICROARRAY P-value qPCR 4 >95 % 4,7 0, >95 % 5,4 0,01 17 >95 % 5,5 0,028 9 >95 % 4,9 0,02 18 >50% Results: Confirmation by qPCR of gene expression.

CLCA4: The protein encoded by this gene belongs to the calcium sensitive chloride conductance protein family. The exact function of this protein is not known. STAT1: This protein can be activated by various ligands including interferon-alpha, interferon-gamma, EGF, PDGF and IL6. This protein mediates the expression of a variety of genes, which is thought to be important for cell viability in response to different cell stimuli and pathogens. Results: Other interesting genes modulated in Cufi confirmed by qPCR.

Conclusion In the absence of pathogen agents, we observe an up-regulation of several inflammatory actors in Cufi cells. CFTR deficiency could be responsible for an excessive immune and inflammatory response. The highest regulated genes are implicated in the TLR signaling pathway, therefore there could be a dysregulation of this pathway in CF cells.