Gene Expression Dynamics Induced by Ciprofloxacin and Loss of LexA Function in Pseudomonas aeruginosa PAO1 Using Data Mining and Network Analysis

Abstract

Pseudomonas aeruginosa is an opportunistic pathogen that causes a variety of infections in humans and frequently develops mechanisms of resistance to antibiotics, which makes its treatment difficult. In this study we applied gene expression analysis using data mining techniques and network analysis to evaluate the temporal effects of exposure to ciprofloxacin and the changes caused by the loss of function of LexA, a regulator of the SOS response to the cellular stress. Initially, global differential expression profiles using clustering algorithms suggested that the effects of antibiotic exposure were determined primarily by time and not by loss of LexA function. This was verified by performing attribute selection and differential expression analysis among conditions, where less than 3.3% of maximum difference between strains but up to 21% of differences were observed over time. Together with network analysis, a significant increase in topological metrics was determined when evaluating temporal changes. Functional annotation showed metabolic pathways enriched over time but not when comparing strains. Overall, the results obtained revealed that the response to ciprofloxacin tends to be exacerbated over time and that it remains stable in the face of the loss of function of LexA activity.