The impact of growth rate on RNA-protein relationships in Pseudomonas aeruginosa

Our understanding of bacterial physiology during human infection is hampered by limited characterization of bacterial function in the infection site and a research bias towards in vitro, fast-growing bacteria. Recent studies have begun to address these gaps in knowledge by directly quantifying bacterial mRNA levels in human-derived samples using transcriptomics. However, mRNA levels are not always predictive of protein abundances, which are the primary functional components of a cell. Here, we assessed transcriptomes and proteomes of bacterial pathogen Pseudomonas aeruginosa (P. aeruginosa) using chemostats across four growth rates. We found a moderate genome-wide correlation among mRNAs and proteins across all growth rates, with genes essential for P. aeruginosa survival displaying stronger correlations than non-essential genes. We used statistical methods to identify genes whose mRNA abundances poorly predict protein abundance and calculated an RNA-to-protein (RTP) conversion factor to improve mRNA prediction of protein levels across strains and growth conditions. This study provides critical insights into infection microbiology by providing a framework for enhancing the functional interpretation of bacterial transcriptome data acquired from human infections.