Characterization of the E. coli proteome and its modifications during growth and ethanol stress

Abstract

We set out to provide a resource to the microbiology community especially with respect to systems biology based endeavors. To this end, we generated a comprehensive dataset monitoring the changes in protein expression, copy number, and post translational modifications in a systematic fashion during growth and ethanol stress in E. coli. We utilized high-resolution mass spectrometry (MS) combined with the Super-SILAC approach. In a single experiment, we have identified over 2300 proteins, which represent approximately 88% of the estimated expressed proteome of E. coli and estimated protein copy numbers using the Intensity Based Absolute Quantitation (iBAQ). The dynamic range of protein expression spanned up to six orders of magnitude, with the highest protein copy per cell estimated at approximately 300,000. We focused on the proteome dynamics involved during stationary phase growth. A global up-regulation of proteins related to stress response was detected in later stages of growth. We observed the down-regulation of the methyl directed mismatch repair system containing MutS and MutL of E. coli growing in long term growth cultures, confirming that higher incidence of mutations presents an important mechanism in the increase in genetic diversity and stationary phase survival in E. coli. During ethanol stress, known markers such as alcohol dehydrogenase and aldehyde dehydrogenase were induced, further validating the dataset. Finally, we performed unbiased protein modification detection and revealed changes of many known and unknown protein modifications in both experimental conditions. Data are available via ProteomeXchange with identifier PXD001648.

Keywords: E. coli; Super-SILAC; absolute quantitation; post translational modifications; quantitative proteomics; stress response.

Figure 1

Protein copy number estimates of E. coli. (A) Dynamic range of protein copy number estimates which span across less than six orders of magnitude from approximately 1–300,000 protein copies per cell. (B) Biological reproducibility of Copy number estimates between time points T3 and T5 as indicated through Bland-Altman statistical analysis, indicate a good level of correlation. Outliers between copy number estimates are usually associated with those proteins that are of a general low abundance.

Figure 2

Relative proteome dynamics during growth in E. coli. (A) Hierarchical clustering analysis of fluctuating proteins reveals distinct growth stage specific changes, resulting in five distinct clusters. Examples of representative classes of proteins based on GO enrichments are depicted below each cluster. (B) Relative (solid line) and absolute dynamics (dashed line) of detected universal stress response proteins reveals an increased level of expression during later stages of stationary phase (TP7) (C) Relative (solid line) and absolute dynamics (dashed line) of detected mismatch repair proteins mutS and mutL reveals a decreased level of expression during later stages of growth.

Figure 3

Relative proteome dynamics during ethanol stress in E. coli. (A) Hierarchical clustering analysis of fluctuating proteins reveals distinct specific changes at each time point, resulting in three distinct clusters. Examples of representative classes of proteins based on GO enrichments are depicted below each cluster. (B) Relative (solid line) and absolute dynamics (dashed line) of detected alcohol dehydrogenases confirms that upon ethanol stress, the alcohol dehydrogenase of E. coli (yqhD) has the highest level of expression compared to other alcohol dehydrogenases. (C) Relative (solid line) and absolute dynamics (dashed line) of detected proteins associated with the binding and transport of maltose.

Figure 4

Unbiased detection of protein modifications during ethanol stress. Comparison of protein modification that occur in pre-stress vs. 10 min ethanol stress reveals the presence of many post translational modifications associated with sample preparation, but also the presence of an increased amount of acetylation likely due to the production of acetate associated with ethanol metabolism. The Y axis represents the difference between the normalized counts (absolute counts divided by the number of sequenced precursors) of each modification. The legend lists the absolute difference and fold change (FC).