The Escherichia coli proteome: past, present, and future prospects

Abstract

Proteomics has emerged as an indispensable methodology for large-scale protein analysis in functional genomics. The Escherichia coli proteome has been extensively studied and is well defined in terms of biochemical, biological, and biotechnological data. Even before the entire E. coli proteome was fully elucidated, the largest available data set had been integrated to decipher regulatory circuits and metabolic pathways, providing valuable insights into global cellular physiology and the development of metabolic and cellular engineering strategies. With the recent advent of advanced proteomic technologies, the E. coli proteome has been used for the validation of new technologies and methodologies such as sample prefractionation, protein enrichment, two-dimensional gel electrophoresis, protein detection, mass spectrometry (MS), combinatorial assays with n-dimensional chromatographies and MS, and image analysis software. These important technologies will not only provide a great amount of additional information on the E. coli proteome but also synergistically contribute to other proteomic studies. Here, we review the past development and current status of E. coli proteome research in terms of its biological, biotechnological, and methodological significance and suggest future prospects.

FIG. 1.

Major developments in the history of proteomics. Since the beginning of proteome studies in 1975, proteomics and the associated technologies have evolved dramatically, resulting in almost exponential increases in the number of resolved proteins and their identification and greatly enhancing our understanding of complex biological processes in a variety of organisms.

FIG. 2.

General steps for proteomic analysis and tips for success. Once the project objective is set, E. coli cells are cultured and sampled for proteome profiling. During this process, protein samples can be prefractionated or labeled differentially for better comparison of the results. Proteome profiles can be obtained by gel-based and/or non-gel-based approaches. Also, predictive proteomic studies can be performed to analyze a priori the characteristics of proteins in the proteome. Gel-based approaches and non-gel-based approaches are complementary and should be combined if possible to maximize the total number of proteins detected and identified. sHsps IbpA and IbpB were from E. coli and Hsp26 was from Saccharomyces cerevisiae (96). SDS-PAGE, sodium dodecyl sulfate-polyacrylamide gel electrophoresis; AEBSF, aminoethyl benzylsufonyl fluoride or Pefabloc SC; BCA, bicinchoninic acid; delta Cn, correlation value (difference between the first hit and the second hit); DTE, dithioerythritol; DTT, dithiothreitol; iTRAQ, a multiplexed set of isobaric reagents that yield amine-derivatized peptides (iTRAQ reagents; Applied Biosystems, CA) (253); PMSF, phenylmethylsulfonyl fluoride; RSp, rank preliminary score; SELDI-TOF-MS, surface-enhanced laser desorption ionization-time of flight mass spectrometry; TCA, trichloroacetic acid; Xcorr, cross-correlation (measures how close the spectrum fits to the ideal spectrum).

FIG. 3.

Distribution of E. coli proteins identified by gel-based and non-gel-based approaches. These figures plot the theoretical pI versus the theoretical MW (Mw) of the open reading frame products in E. coli. Shown are images of E. coli proteins identified by gel-based approaches (a) and non-gel-based approaches (b) and the virtual 2-D image of 4,237 E. coli K-12 ORF entries predicted by a predictive proteomic tool (c). Each crossbar represents a protein spot. The numbers of proteins found by gel-based and/or non-gel-based approaches and by predictive proteomic tools are compared in panel d. The total number of E. coli proteins nonredundantly identified by experiments is 1,627 (∼38% of 4,237 ORF entries). For alkaline proteins (pI, >8.0), only 253 proteins (∼19%) out of 1,356 ORF entries were identified so far. For the names and the exact locations of all these protein spots, see Fig. S1 in the supplemental material. The theoretical pI/MW ratios were calculated using the Compute pI/Mw tool (http://www.kr.expasy.org/tools/pi_tool.html).

FIG. 4.

The cascade-like regulation observed with various stimulons and/or regulons in a complex regulatory network. The circles indicate regulons, while the rectangles indicate stimulons. Stimulons in which proteins are induced by stimuli such as stationary phase, temperature shock, pH variation, oxidative stress, and starvation are shown in the respectively labeled panels. Regulons shown in large circles are accompanied by small circles which represent major regulators for the corresponding stimuli. One signal activates or represses many regulators, as shown in small circles, to control the transcription and translation of various genes, leading to complex interactions in the cell. For example, E. coli cells enter the stationary phase in response to complex stresses such as cell growth, increased cell density, the presence of byproducts or toxic substances, and inappropriate conditions (restriction of oxygen, low/high temperature and pH, and limitation of nutrients). This complex response is mediated by a variety of specific regulators in addition to the master regulator, RpoS, which is controlled by itself or by other proteins (see the text for a detailed explanation). Abbreviations: HNS, nucleoid-associated protein; IHF, integration host factor; Lrp, leucine-responsive protein; Fis, factor for inversion stimulation.