BioProject

Global expression profiling of airway epithelial cells infected with Pseudomonas aeruginosa and the rsmA mutant. Recent work in our laboratory investigated the impact of RsmA on a number of airway epithelial cell phenotypes including actin depolymerization, cytotoxicity and invasion. These cellular phenotypes were influenced by the positive effect of RsmA on the expression of the TTSS in P. aeruginosa (Mulcahy et al. 2006. Infect. Immun.). Pseudomonas aeruginosa is an important opportunistic pathogen which is capable of causing both acute and chronic infections in immunocompromised patients. Successful adaptation of the bacterium to its host environment relies on the ability of the organism to tightly regulate gene expression. RsmA, a small RNA-binding protein, controls the expression of a large number of virulence-related genes in P. aeruginosa, including those encoding the type III secretion system and associated effector proteins, with important consequences for epithelial cell morphology and cytotoxicity. In order to examine the influence of RsmA-regulated functions in the pathogen on gene expression in the host, we compared global expression profiles of airway epithelial cells in response to infection with P. aeruginosa PAO1 and an rsmA mutant. The RsmA-dependent response of host cells was characterized by significant changes in the global transcriptional pattern, including the increased expression of two Kruppel-like factors, KLF2 and KLF6. This increased expression was mediated by specific type III effector proteins. ExoS was required for the enhanced expression of KLF2, whereas both ExoS and ExoY were required for the enhanced expression of KLF6. Neither ExoT nor ExoU influenced the expression of the transcription factors. Additionally, the increased gene expression of KLF2 and KLF6 was associated with ExoS-mediated cytotoxicity. Therefore, this study identifies for the first time the human transcription factors KLF2 and KLF6 as targets of the P. aeruginosa type III exoenzymes S and Y, with potential importance in host cell death. Keywords: Expression profiling, Pseudomonas aeruginosa, microbial infection, airway epithelial cells, cystic fibrosis, exoenzymes, ExoS, ExoT, ExoU, ExoY, Kruppel-like factors, KLF2, KLF6 Overall design: RNA isolation and microarray sample preparation Biological samples from two independent infection experiments were used in two independent microarray experiments as outlined below. Total RNA was isolated from epithelial cells using a RNeasy Kit (Qiagen) according to manufacturer's instructions. Genomic DNA was removed using DNA-free (Ambion) and confirmed by PCR to be free of DNA, prior to cDNA synthesis. Isolated RNA was used in the preparation of fragmented cRNA for microarray hybridization according to the manufacturer's protocol (Affymetrix). Briefly, 8.5ug of RNA was converted to double stranded cDNA by reverse transcription, using a Superscript cDNA synthesis kit (Invitrogen), with an oligo dT primer containing a T7 RNA polymerase site added 3' of the poly (T) (Affymetrix). After second strand synthesis, cDNA was converted to biotin-labelled cRNA by in vitro transcription (Enzo). The labelled cRNA was purified using Genechip clean up module (Qiagen). Total cRNA quality and yield was measured using a GeneQuant spectrophotometer and 20ug of cRNA was fragmented at 94ºC for 35 mins in fragmentation buffer (40 mM tris acetate, pH 8.1, 100 mM potassium acetate, 30 mM magnesium acetate). Full length and fragmented cRNA sample integrity was assessed by electrophoresis on a 1% agarose-TBE gel visualized by staining with SyBr green. Fragmented cRNA was sent to MRC geneservice (Cambridge, UK) for assessment of RNA quality using a Bioanalyzer (Agilent) before hybridization and scanning. The arrays were washed and stained in a fluidics station and scanned according to the manufacturer's protocols (Affymetrix). Microarray data analysis Raw image analysis was performed using Microarray Suite v5.0 software (Affymetrix). Downstream data analysis was performed using GeneSight software version 2.7 (Biodiscovery). For all analyses, the signal intensities from two independent infection and microarray experiments were used. Statistically significant changes in gene expression were identified by firstly performing analysis of variance (ANOVA), for comparison between multiple groups, with a P value of < 0.05. The list of transcripts generated using ANOVA was then subjected to a Student's t-test, for comparison between two groups, with a P value of < 0.05. To identify different levels of gene expression compared to baseline, a fold change cut-off of greater than or equal to 1.5 was applied to this data.