Impact of Growth Rate on the Protein-mRNA Ratio in Pseudomonas aeruginosa

Abstract

Our understanding of how bacterial pathogens colonize and persist during human infection has been hampered by the limited characterization of bacterial physiology during infection and a research bias toward in vitro, fast-growing bacteria. Recent research has begun to address these gaps in knowledge by directly quantifying bacterial mRNA levels during human infection, with the goal of assessing microbial community function at the infection site. However, mRNA levels are not always predictive of protein levels, which are the primary functional units of a cell. Here, we used carefully controlled chemostat experiments to examine the relationship between mRNA and protein levels across four growth rates in the bacterial pathogen Pseudomonas aeruginosa. We found a genome-wide positive correlation between mRNA and protein abundances across all growth rates, with genes required for P. aeruginosa viability having stronger correlations than nonessential genes. We developed a statistical method to identify genes whose mRNA abundances poorly predict protein abundances and calculated an RNA-to-protein (RTP) conversion factor to improve mRNA predictions of protein levels. The application of the RTP conversion factor to publicly available transcriptome data sets was highly robust, enabling the more accurate prediction of P. aeruginosa protein levels across strains and growth conditions. Finally, the RTP conversion factor was applied to P. aeruginosa human cystic fibrosis (CF) infection transcriptomes to provide greater insights into the functionality of this bacterium in the CF lung. This study addresses a critical problem in infection microbiology by providing a framework for enhancing the functional interpretation of bacterial human infection transcriptome data. IMPORTANCE Our understanding of bacterial physiology during human infection is limited by the difficulty in assessing bacterial function at the infection site. Recent studies have begun to address this question by quantifying bacterial mRNA levels in human-derived samples using transcriptomics. One challenge for these studies is the poor predictivity of mRNA for protein levels for some genes. Here, we addressed this challenge by measuring the transcriptomes and proteomes of P. aeruginosa grown at four growth rates. Our results revealed that the growth rate does not impact the genome-wide correlation of mRNA and protein levels. We used statistical methods to identify the genes for which mRNA and protein were poorly correlated and developed an RNA-to-protein (RTP) conversion factor that improved the predictivity of protein levels across strains and growth conditions. Our results provide new insights into mRNA-protein correlations and tools to enhance our understanding of bacterial physiology from transcriptome data.

Keywords: Pseudomonas aeruginosa; chemostat cultures; cystic fibrosis; protein-mRNA ratios; protein-to-mRNA ratio; proteomics; transcriptomics.

FIG 1

Differentially expressed mRNAs and proteins show modest overlap. Venn diagrams show the overlap between differentially expressed mRNAs and proteins among fast-growing (3-h doubling time) and slow-growing (25-h doubling time) P. aeruginosa cultures. (A) Overlap of differentially produced mRNAs and proteins using a P_adj value of <0.05. (B) Overlap of differentially produced mRNAs and proteins using a more stringent cutoff (RNA fold change of ≥4, protein fold change of ≥2, and P_adj of <0.05).

FIG 2

Identification of P. aeruginosa mRNAs and proteins that change in abundance monotonically with the growth rate. (A and B) Venn diagrams showing the overlap of monotonic increasing (A) and decreasing (B) levels as a function of the growth rate in both the transcriptome and proteome. (C) Representative genes that showed monotonic increasing gene expression as the growth rate increased. Genes listed from top to bottom are PA14_61720 (red), uraA (yellow), murI (purple), upp (orange), moeB (green), and prmC (blue). (D) Representative genes that showed monotonic decreasing gene expression as the growth rate decreased. Genes listed from top to bottom are PA14_66320 (orange), gcvP1 (red), arcD (green), arcC (blue), phaC2 (yellow), and hutH (purple).

FIG 3

mRNA and protein are positively correlated at all growth rates. mRNA-protein relationships across a diverse range of growth rates spanning doubling times from 3 h to 25 h (A-D) are shown. Genes used for these analyses were detected at all growth rates in both the mRNA and protein expression data sets (n = 3,903). (Left) Scatterplots of mRNAs and proteins with associated Spearman rank correlation coefficients (ρ) demonstrating that mRNA and protein levels are positively correlated. (Middle) Binned scatterplots showing the relationship between measured protein abundances and mRNA abundances. The numbers of genes within each correlation are represented by the color scale in the right ordinate, where the number indicates the number of genes at a specific x-y coordinate. (Right) Distributions of mRNA and protein levels show that protein abundances vary to a greater extent than mRNA abundances.

FIG 4

P. aeruginosa essential genes are highly expressed and have increased mRNA-protein correlations. (A and B) Distributions of mRNA (means ± standard deviations [SD], 8.4 ± 1.8 for essential genes and 6.0 ± 2.0 for nonessential genes) (A) and protein (means ± SD, 22.3 ± 3.5 for essential genes and 18.6 ± 3.4 for nonessential genes) (B) abundances for P. aeruginosa essential and nonessential genes at the 3-h doubling time reveal higher levels of both mRNA and protein for essential genes. (C) Scatterplot with associated Spearman rank correlation coefficient (ρ) for essential genes (red circles) (n_E = 484) and nonessential genes (blue circles) (n_NE = 3,419). (D) Spearman rank correlation coefficients for P. aeruginosa essential and nonessential genes at all growth rates. (E and F) Histograms of the standard deviations in mRNA (means ± SD, 0.48 ± 0.22 for essential genes and 0.62 ± 0.38 for nonessential genes) (E) and protein (means ± SD, 0.28 ± 0.32 for essential genes and 0.42 ± 0.49 for nonessential genes) (F) abundances for essential and nonessential genes demonstrate lower variance for essential genes than for nonessential genes. Histograms were constructed with a bin size of 0.05 using the variation in log₂-normalized mRNA and protein levels from all growth rates. P values were calculated using a Wilcoxon rank sum test.

FIG 5

Identification of genes with poor mRNA-protein correlations. (A) Scatterplot of mRNA and protein abundances noting 160 genes with high (pink) or low (blue) PTRs at the 3-h doubling time. Genes with high and low PTRs were defined as being 2 standard residuals from the predicted correlation. (B) Distribution of genes with high (pink) and low (blue) PTRs at the 3-h doubling time. (C) Venn diagram of genes with poor mRNA-protein correlations across all growth rates. Ninety-four genes are shared at all growth rates. (D) Graph showing the predicted Gibbs free energy of the Shine-Dalgarno sequence binding to the 16S rRNA of the ribosome as a function of the PTR. The predicted binding energy of the Shine-Dalgarno sequence was weakly correlated with the PTR for all genes, including the 160 genes with high (pink) and low (blue) PTRs.

FIG 6

Application of gene-specific RTP conversion factors to P. aeruginosa transcriptomes from other strains, growth conditions, and human CF infections. (A) Prediction of protein levels by the application of the RTP conversion factors to mRNA levels of P. aeruginosa strain PAO1 grown in a synthetic CF sputum medium (SCFM) and strain PA14 grown in chemostats. The application of the RTP conversion factors improved the mRNA-protein Spearman rank correlation coefficients for PAO1 from 0.69 to 0.91 and for PA14 from 0.77 to 0.98. (B) Scatterplot of RNA-protein relationships among measured mRNA and protein levels (black circles) and mRNA corrected by the application of the RTP conversion factors and measured protein levels (red circles). Data are from P. aeruginosa strain PAO1 grown in a synthetic CF sputum medium. The associated Spearman rank correlation coefficients (ρ) among RNA and protein abundances increased from 0.69 to 0.91 after the application of the RTP conversion factors to mRNA levels. (C and D) Central metabolic pathway, with colored arrows showing predicted metabolic fluxes using mRNA levels from P. aeruginosa human CF infection transcriptomes (C) and mRNA levels from P. aeruginosa human CF infection transcriptomes after applying the RTP conversion factor (D). Gray indicates the absence of genes or proteins detected in the data sets. F6P, fructose-6-phosphate; X5P, xylulose-5-phosphate; Ri5P, ribulose-5-phosphate; E4P, erythrose-4-phosphate; S7P, sedoheptulose-7-phosphate; R5P, ribose-5-phosphate; G3P, glyceraldehyde 3-phosphate; BPG, 1,3-bisphosphoglycerate; 3PG, 3-phosphoglycerate; 2PG, 2-phosphoglycerate; ATP, adenosine triphosphate; NADH, nicotinamide adenine dinucleotide; NADPH, nicotinamide adenine dinucleotide phosphate; PEP, phosphoenolpyruvate; FADH₂, reduced flavin adenine dinucleotide.