This document presents the results of a study examining the transcriptional profiling of Pseudomonas aeruginosa lineages that evolved during infection of cystic fibrosis patients. The study aimed to identify potential adaptive expression traits in P. aeruginosa through isolating bacteria from patients, performing genetic and expression analyses, and microarray experiments. Key results included hierarchical clustering of microarray data identifying differentially expressed genes between and within clonal groups over time. Future work could involve altering current therapeutics based on expression profiles and better understanding genes of unknown function that were differentially expressed.

1. Presented by-
Neha Gupta
Priyanka Bhatt

2. Introduction and Background
• About CF
• Previous studies

3. Hypothesis
Transcriptional profiling would identify
potential adaptive expression traits in
P. aeruginosa lineages that evolved in
vivo

4. Methods
• Isolate collection and genetic analyses
• P. aeruginosa annotation
• Expression profiling
• Microarray analyses

5. Isolate collection and genetic analyses
Fig 1
• Genetic typing - RAPD

6. P. aeruginosa annotation
• All analysis were based in PAO1 annotation
• Genes used to develop probe set were selected by
reciprocal best-hit BLAST among strains PAO1, PA14,
PA7 and LESB58
• Probe set selection
• 5,391 coding genes were selected for Microarray

7. Expression profiling
All Isolated from CF individuals + PAO1 & PA14
RNA isolation
cDNA preparation
cDNA added to Affymetrix GeneChip
Washed, Stained and scanned

8. Microarray analyses
• RMA method used for Hierarchical Clustering
• Gene expression analysis
– DE genes between clonal groups
– DE genes over time within clonal groups

9. Results – Hierarchical clustering of Microarray Data

13. Discussion

14. Future Work
• The current therapeutics can be altered based on the
gene expression profiles.
• As the number of traits undergoing parallel evolution
may be underestimated, sampling P.aeruginosa RNA
directly from sputum samples is very challenging
• Many of these 24 genes encode proteins of unknown
function . Understanding the roles of these genes
could provide insight into selective pressures in CF
lung.

15. Thank You!
Questions?