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Multifaceted Stoichiometry Control of Bacterial Operons Revealed by Deep Proteome Quantification

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Multifaceted Stoichiometry Control of Bacterial Operons Revealed by Deep Proteome Quantification

Jing Zhao et al. Front Genet.

Abstract

More than half of the protein-coding genes in bacteria are organized in polycistronic operons composed of two or more genes. It remains under debate whether the operon organization maintains the stoichiometric expression of the genes within an operon. In this study, we performed a label-free data-independent acquisition hyper reaction monitoring mass-spectrometry (HRM-MS) experiment to quantify the Escherichia coli proteome in exponential phase and quantified 93.6% of the cytosolic proteins, covering 67.9% and 56.0% of the translating polycistronic operons in BW25113 and MG1655 strains, respectively. We found that the translational regulation contributes largely to the proteome complexity: the shorter operons tend to be more tightly controlled for stoichiometry than longer operons; the operons which mainly code for complexes is more tightly controlled for stoichiometry than the operons which mainly code for metabolic pathways. The gene interval (distance between adjacent genes in one operon) may serve as a regulatory factor for stoichiometry. The catalytic efficiency might be a driving force for differential expression of enzymes encoded in one operon. These results illustrated the multifaceted nature of the operon regulation: the operon unified transcriptional level and gene-specific translational level. This multi-level regulation benefits the host by optimizing the efficiency of the productivity of metabolic pathways and maintenance of different types of protein complexes.

Keywords: DIA; HRM-MS; mass-spectrometry; multifaceted stoichiometry control; operons; proteome quantification; translation.

Figures

FIGURE 1
FIGURE 1
Comparison of protein abundances obtained by different label-free quantification methods. (A) Quantified protein numbers in two biological replications of HRM-MS method of two strains. (B,C) The quantification reproducibility of HRM-MS method in relaxed and stringent criteria of two E. coli strains, respectively. R is the Pearson correlation coefficient. The protein abundance range was divided into low (<2.0 log scale), mid (2.0–3.5 log scale) and high (>3.5 log scale) sections. r_low, r_mid and r_high are the Pearson correlation of the proteins in these sections. (D,E) The reproducibility of DDA MS experiment of the two strains. Proteins were quantified using iBAQ method. (F) The peptide coverage of the HRM-MS-identified proteins in two E. coli strains, respectively.
FIGURE 2
FIGURE 2
Protein coefficient of variation (CV) within operons of measured data and randomized negative control.
FIGURE 3
FIGURE 3
Functional-dependence of operon stoichiometry control. (A) Protein CVs within operons, categorized according to the number of genes in operon. RD, randomized data. Blue dashed line represents the median of the randomized data. (B,C) Gene ontology (GO) overrepresentation of the operons with lower CV than the randomized median (L2–L5) and higher CV than the randomized median (H2–H5). The number refer to the number of genes in operon. GO terms with P > 0.001 were considered insignificant and marked as gray. BP, biological process; CC, cell component; MF, molecular function. (B) BW25113 and (C) MG1655 strain.
FIGURE 4
FIGURE 4
Length-dependence of operon stoichiometry control. (A) Linear regression analysis of gene CVs at RNA and protein levels within operon that encodes proteins forming complexes or involving in the metabolic pathways in BW25113 strain. P-values of the regression are indicated in the plots. P < 0.05 are considered significant. RD, randomized control. (B) The RNA–protein scatter plots of the gene expression levels of the “Complex” and “Pathway” operons in two strains, respectively. (C) The correlation coefficient R2 of the RNA–protein correlation shown in (B) panel. (D) The mutual P-value (Mann–Whitney U test) matrix of the protein CV within “Pathway” operons and “Complex” operons, respectively. P < 0.05; ∗∗P < 0.01.
FIGURE 5
FIGURE 5
Examples of “Complex” and “Pathway” operons. (A) The gene expression at RNA and protein levels in E. coli K-12 MG1655 strain. The detailed expression level are marked on the data points. <1 means the expression level is too low to be confidently quantified. (B) Illustrations of the “branch-free” and “branched” pathways. E1 and E2 represent the quantified proteins in the same operon. (C) All nine operons which encodes enzymes that exist in branch-free pathways. Their RNA abundance, protein abundance and KM values were plotted. KM values are plotted in orange bars. Detailed KM values are listed in Table 1. Detailed pathways are illustrated in Supplementary Figure S6.
FIGURE 6
FIGURE 6
Gene intervals in the “Complex” and “Pathway” operons. (A) Illustration of the gene interval within one operon. (B) Distribution of the gene intervals of “Complex” and “Pathway” operons.

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