Systems biology approaches address the challenge of translating sequence information into function. In this study, we described the Pseudomonas aeruginosa PA14 proteomic landscape and quantified environment-driven changes in protein levels by the use of LC-MS techniques. Previously recorded mRNA data allowed a comparison of RNA to protein ratios for each individual gene and, thus, to explore the relationship between an mRNA being differentially expressed between environmental conditions and the mRNA-protein correlation for that gene. We developed a Random Forest-based predictor for protein levels and found that the mRNA to protein correlation was higher for genes/proteins that undergo dynamic changes. One example of a discrepancy between protein and predicted protein levels was observed for a phage-related gene cluster, which was translated into low protein levels under standard growth conditions. However, under SOS-inducing conditions more protein was produced and the prediction of protein levels based on mRNA abundancy became more accurate. In conclusion, our systems biology approach sheds light on complex mRNA to protein level relationships and uncovered condition-dependent post-transcriptional regulatory events.
© 2018 Society for Applied Microbiology and John Wiley & Sons Ltd.