Quantifying absolute protein synthesis rates reveals principles underlying allocation of cellular resources

Abstract

Quantitative views of cellular functions require precise measures of rates of biomolecule production, especially proteins-the direct effectors of biological processes. Here, we present a genome-wide approach, based on ribosome profiling, for measuring absolute protein synthesis rates. The resultant E. coli data set transforms our understanding of the extent to which protein synthesis is precisely controlled to optimize function and efficiency. Members of multiprotein complexes are made in precise proportion to their stoichiometry, whereas components of functional modules are produced differentially according to their hierarchical role. Estimates of absolute protein abundance also reveal principles for optimizing design. These include how the level of different types of transcription factors is optimized for rapid response and how a metabolic pathway (methionine biosynthesis) balances production cost with activity requirements. Our studies reveal how general principles, important both for understanding natural systems and for synthesizing new ones, emerge from quantitative analyses of protein synthesis.

Figure 1. Absolute Quantification of Protein Synthesis Rates

(A) Effect of translational pausing on average ribosome density. Average ribosome density is plotted for the first and second half of each gene. The Pearson correlation for genes with at least 64 reads aligned to both halves (red) is R² = 0.92. The inset shows the distribution of the fold-difference between the second and the first halves (N = 2,870, SD = 1.3 fold). (B) Agreement between published protein copy numbers and absolute synthesis rates. The copy numbers of 62 proteins which have been individually quantified in the literature are plotted against the absolute protein synthesis rates (Pearson correlation R² = 0.96). See also Figure S1, Figure S2, Table S1, and Table S2

Figure 2. Proportional Synthesis of Multi-Protein Complexes

(A) Translation rates reflecting subunit stoichiometry for the ATP operon. Eight subunits of the F₀F₁ ATP synthase are expressed from a polycistronic mRNA, whose level as measured by RNA-seq is shown in blue. Each subunit is associated with different levels of ribosome density (green), and the average density is proportional to the subunit stoichiometry (right). (B) Proportional synthesis for a diverse range of complexes. Synthesis rates are plotted as a function of the subunit stoichiometry for multi-protein complexes whose subunits are encoded in the same operon. Complexes with different subunit stoichiometry or more than two subunits are included here (also see panel (C)). The dashed line indicates the best-fit that crosses the origin. (C) Proportional synthesis for complexes with two equimolar subunits. Each complex is plotted for the synthesis rates of the two subunits, with the earlier (later) gene in the operon on the horizontal (vertical) axis. 28 equimolar and co-transcribed complexes, covering 4 orders of magnitude in expression level, are plotted here. Inset shows the histogram of fold-difference between the synthesis rates of the two subunits. Our experimental results are shown in red, and the predicted values based on a thermodynamic model considering the sequence surrounding translation initiation sites are shown in blue (Salis et al., 2009). The distribution of the differences in translation rates for all other operons is shown in gray. Panels B and C show complexes whose subunits are encoded on a single polycistronic operon. See Fig. S3BC for examples of proportional synthesis involving distinct transcripts. See also Figure S3, Figure S4, Figure S6, Table S3, and Table S4.

Figure 3. Proportional Synthesis for Complexes in Yeast

(A) Proportional synthesis for multi-protein complexes in S. cerevisiae. Synthesis rates are plotted as a function of the subunit stoichiometry for complexes with more than two subunits. For the signal recognition particle, four subunits (Srp14/21/68/72) are synthesized according to their stoichiometry, and the other two are exceptions. (B) Proportional synthesis for heterodimeric complexes in S. cerevisiae. Each complex is plotted for the synthesis rate of the two subunits. (C) Proportional synthesis for complexes with paralogous subunits. For each complex, the subunits that can substitute each other are plotted in the same column.

Figure 4. Hierarchical Expression for Functional Modules

(A) Synthesis rates for toxin-antitoxin (TA) modules. E. coli contains 12 type II TA systems that are each expressed from a polycistronic mRNA. (The order of genes differs among systems.) The anti-toxin protein binds to and inhibits the toxin protein, while repressing its own transcription. The synthesis rates for each system are plotted (bottom). Modules with the toxin gene preceding the antitoxin gene in the operon is marked by asterisk. (B) Synthesis rates for sigma-anti-sigma factors modules. The anti-sigma factor binds to and inhibits the sigma factor, preventing transcription from the promoter driven by the corresponding sigma factor. The synthesis rates for each systems are plotted (bottom). (C) Synthesis rates for two-component signaling systems. Bacterial two-component signaling system consists of a membrane-bound histidine kinase and the cognate response regulator. The synthesis rates for 26 two-component systems in E. coli are plotted (bottom). (D) Synthesis rates for ATP-binding cassette (ABC) transporters. An ABC transporter consists of a core membrane transporter, an ATP-binding domain, and the corresponding periplasmic binding proteins. The synthesis rates for each transporter are plotted (bottom).

Figure 5. Composition of the E. coli Proteome

(A) Break down of the proteome by functions. The mass-fraction of the proteome that is devoted to specific biological functions is plotted as a pie chart. The copy numbers were estimated for E. coli grown in rich defined medium (Methods). (B) Ten proteins with the largest mass-fraction in the proteome. The color used for each protein corresponds to the biological function indicated in A.

Figure 6. Abundance of Transcription Factors (TFs)

(A) Cumulative distribution of abundance for transcriptional activators, repressors, and dual regulators. The cumulative distribution for each class of TF is plotted as a function of the copy number per genome equivalent. (B) Cumulative distribution of abundance for autoregulators. The cumulative distributions for positive- and negative-autoregulators are plotted as a function of the copy number per genome equivalent. (C) Ligand dependence of target binding. Among TFs whose abundance fall into a given range, the fraction that binds to the target site in a ligand-dependent way is shown in blue, and the fraction that binds to the target site independent of ligands is shown in green. The number of transcription factors analyzed is indicated above each bin. See also Table S5.

Figure 7. Quantitative Analysis of the Methionine Biosynthesis Pathway

(A) Maximal reaction rates for the intermediate steps. For each step of the pathway, the maximal reaction rate (V_max), inferred from the enzyme abundance in vivo and the turnover number measured in vitro, is shown as the width of the blue bar, unless no in vitro data were available. The last step that is catalyzed by the enzyme MetE has V_max = 34,000 Met/s/cell, whereas the flux of methionine into protein synthesis is 31,000 Met/s/cell. The scatter plot on the right shows up-regulation of these enzymes in media without methionine. (B) Model predicting the optimal MetE level. In a model that considers the cost and benefit of MetE expression, the maximal growth rate is plotted as a function of the mass fraction of MetE in the proteome. The cost due to competition with new ribosome synthesis is shown in red, and the benefit from increased methionine flux is shown in blue. The maximal growth rate is highest (28 min) when the mass fraction of MetE is ~7%. This prediction agrees with experimental results. See also Figure S5.