Quantification of mRNA and protein and integration with protein turnover in a bacterium

Abstract

Biological function and cellular responses to environmental perturbations are regulated by a complex interplay of DNA, RNA, proteins and metabolites inside cells. To understand these central processes in living systems at the molecular level, we integrated experimentally determined abundance data for mRNA, proteins, as well as individual protein half-lives from the genome-reduced bacterium Mycoplasma pneumoniae. We provide a fine-grained, quantitative analysis of basic intracellular processes under various external conditions. Proteome composition changes in response to cellular perturbations reveal specific stress response strategies. The regulation of gene expression is largely decoupled from protein dynamics and translation efficiency has a higher regulatory impact on protein abundance than protein turnover. Stochastic simulations using in vivo data show how low translation efficiency and long protein half-lives effectively reduce biological noise in gene expression. Protein abundances are regulated in functional units, such as complexes or pathways, and reflect cellular lifestyles. Our study provides a detailed integrative analysis of average cellular protein abundances and the dynamic interplay of mRNA and proteins, the central biomolecules of a cell.

Figure 1

Proteome composition changes in response to cellular perturbations. (A) COG class dynamics over the course of 4 days growth in batch culture. Functional classes are colour coded. See Supplementary information for COG class nomenclature. (B) Abundance changes of proteins along the growth curve. Blue (MS): mass spectrometry data; red (WB): quantified western blots. (C) Venn diagram for proteins with significantly changed abundance in response to cellular perturbations. Red: heat shock; blue: DNA damage; green: osmotic stress.

Figure 2

mRNA and protein profiles indicate complex post-transcriptional regulatory mechanisms. (A) Examples for abundance dynamics for mRNA and proteins in operons. Red: mRNA; blue: protein. See Supplementary information for additional profiles. (B) Heat-shock response on mRNA and protein levels for ClpB (MPN531), Lon (MPN332) and DnaK (MPN434). Red vertical line: heat shock start; blue vertical line: heat shock end. Red triangles: mRNA; blue triangles: protein. (C) mRNA and protein dynamics along the growth curve. Blue colour: similar patterns of abundance change; orange colour: different patterns of abundance change.

Figure 3

Protein turnover measurements and stochastic simulations. (A) Boxplots showing the variance of measured protein turnover profiles. 13C/12C ratios of metabolically labelled cells were determined by mass spectrometry. (B) Influence of k1 (k_translation) and k2 (k_degradation) on the ratio protein/mRNA. Colour gradient according to the ratio protein/mRNA. (C) Stochastic simulation of transcription–translation in M. pneumoniae using the range of experimental values for mRNA and protein turnover rates found in this study. We considered 200 ribosomes, 130 RNA polymerase complexes and 400 promoters, RNA lifetime of around 3 min (seen after heat-shock induction; Figure 2B) and a rate for protein production of 0.1 molecules of protein per second (see Supplementary information). Red: protein turnover rate of 0.001/s; blue: protein degradation rate of 0.0001/s; green: protein turnover rate of 0.00001/s. Panels show different mRNA–protein combinations.

Figure 4

Protein complex stoichiometries and dynamics reflect cellular functions. (A) Mapping protein abundances on the literature-curated complexes reveal the importance of this organizational principle. Coloured boxes represent complexes with >1% total abundance by mass. (B) Examples for protein complexes and the stoichiometric abundance of the subunits in the proteome. Boxplots represent measured pairwise protein copy number ratios distributions within protein complexes. All different experimental conditions were used to estimate the distributions. Horizontal continuous lines represent expected stoichiometries. Identical colours associate the measured ratios (boxplots) with expected ratios (horizontal lines) from the literature. (C) Comparison of mass spectrometry and western blotting for the quantification of ribosomal proteins. Red circles: cellular abundances determined by quantitative western blotting; blue crosses: cellular abundances determined by quantitative mass spectrometry. Black line: median (190); dashed line: average (255). (D) Western blots against ribosomal proteins on size exclusion fractionated M. pneumoniae lysates (Superose6 PC 3.2/30 column). High abundant proteins L7 and S2 appear also in the low molecular weight fraction, suggesting secondary functions.

Figure 5

Protein abundances reflect cellular lifestyles. (A) Protein abundances for orthologous proteins within same functional classes correlate highly. Relative enrichment of proteins (slope differences of the trend lines) reflect specific cellular lifestyles of M. pneumoniae and L. interrogans. Red squares: proteins in COG class L (replication, recombination and repair); black crosses: proteins in COG class G (carbohydrate transport and metabolism). (B) Differences in metabolism between L. interrogans and M. pneumoniae are mirrored in protein abundance. Red blocks and line: carbon metabolic route of L. interrogans; blue blocks and line: carbon metabolic routes of M. pneumoniae. Size of coloured shapes represents relative abundances.