From coarse to fine: the absolute Escherichia coli proteome under diverse growth conditions

Abstract

Accurate measurements of cellular protein concentrations are invaluable to quantitative studies of gene expression and physiology in living cells. Here, we developed a versatile mass spectrometric workflow based on data-independent acquisition proteomics (DIA/SWATH) together with a novel protein inference algorithm (xTop). We used this workflow to accurately quantify absolute protein abundances in Escherichia coli for > 2,000 proteins over > 60 growth conditions, including nutrient limitations, non-metabolic stresses, and non-planktonic states. The resulting high-quality dataset of protein mass fractions allowed us to characterize proteome responses from a coarse (groups of related proteins) to a fine (individual) protein level. Hereby, a plethora of novel biological findings could be elucidated, including the generic upregulation of low-abundant proteins under various metabolic limitations, the non-specificity of catabolic enzymes upregulated under carbon limitation, the lack of largescale proteome reallocation under stress compared to nutrient limitations, as well as surprising strain-dependent effects important for biofilm formation. These results present valuable resources for the systems biology community and can be used for future multi-omics studies of gene regulation and metabolic control in E. coli.

Tradeoffs in bacterial physiology determine the efficiency of antibiotic killing

“…2A, blue points). On the sugar line, the well-established resource allocation theory predicts that as more resources are dedicated to carbon catabolism (C sector), fewer resources are dedicated to building ribosomes (R sector), resulting in a correspondingly lower growth rate 29,32,33 . One may assume that under these conditions the death rate increases in direct correlation to growth rate due to increased production of damage in line with previous descriptions 11,15 (e.g., nalidixic acid affects DNA gyrase, which introduces DNA breaks during replication 39 ).…”

“…More detailed yet still coarse-grained models extended the growth laws to predict growth rate as a function of multiple internal proteomic sectors, each representing large groups of genes with similar behavior under corresponding resource limitations [29][30][31] . For example, the "C sector" represents genes which are upregulated under carbon limitation 29,32,33 .…”

First-principles model of optimal translation factors stoichiometry

“…In E. coli, across growth conditions spanning ≈ 20 min doubling time to ≈ 120 min, ⟨ ⟩ changes by about 20%. Specifically, we find ⟨ ⟩ =196, 210, and 240 in respectively MOPS complete (≈ 20 min doubling time(Li et al, 2014)), MOPS minimal (≈ 56 min doubling time(Li et al, 2014)), and NQ1390 forced glucose limitation (≈ 120 min doubling time(Mori et al, 2021)), based on ribosome profiling data. Here for simplicity, we take ⟨ ⟩ ≈ 200 throughout.…”

“…Supplementary File 3: Proteome synthesis fraction (in %) of core mRNA translation factors for species/conditions with slower growth estimated from ribosome profiling. Ribosome profiling data: E. coli (MOPS minimal (Li et al, 2014), M9 glucose (Mori et al, 2021), C. crescentus ((Schrader et al, 2014), with synthesis rates estimated in (Lalanne et al, 2018)).…”

Median-Based Absolute Quantification of Proteins Using Fully Unlabeled Generic Internal Standard (FUGIS)

“…Advances in robust and reproducible LC-MS/MS have led to the notion that generic measures of a protein’s molar abundance could be deduced either from raw intensities or spectral counts of peptide peaks, e.g., emPAI, 27 APEX, 28 SCAMPI. 29 , 30 Methods like Top3/Hi-3, 6 iBAQ, 31 Proteomic Ruler, 32 xTop 33 and Pseudo-IS 34 use averaged XIC intensities of selected or of all peptides matching the protein of interest. Because of limited interlaboratory consistency, they are mostly used for supporting conventional proteomics workflows.…”