The Escherichia coli Peripheral Inner Membrane Proteome

Biological membranes are essential for cell viability. Their functional characteristics strongly depend on their protein content, which consists of transmembrane (integral) and peripherally associated membrane proteins. Both integral and peripheral inner membrane proteins mediate a plethora of biological processes. Whereas transmembrane proteins have characteristic hydrophobic stretches and can be predicted using bioinformatics approaches, peripheral inner membrane proteins are hydrophilic, exist in equilibria with soluble pools, and carry no discernible membrane targeting signals. We experimentally determined the cytoplasmic peripheral inner membrane proteome of the model organism Escherichia coli using a multidisciplinary approach. Initially, we extensively re-annotated the theoretical proteome regarding subcellular localization using literature searches, manual curation, and multi-combinatorial bioinformatics searches of the available databases. Next we used sequential biochemical fractionations coupled to direct identification of individual proteins and protein complexes using high resolution mass spectrometry. We determined that the proposed cytoplasmic peripheral inner membrane proteome occupies a previously unsuspected ∼19% of the basic E. coli BL21(DE3) proteome, and the detected peripheral inner membrane proteome occupies ∼25% of the estimated expressed proteome of this cell grown in LB medium to mid-log phase. This value might increase when fleeting interactions, not studied here, are taken into account. Several proteins previously regarded as exclusively cytoplasmic bind membranes avidly. Many of these proteins are organized in functional or/and structural oligomeric complexes that bind to the membrane with multiple interactions. Identified proteins cover the full spectrum of biological activities, and more than half of them are essential. Our data suggest that the cytoplasmic proteome displays remarkably dynamic and extensive communication with biological membrane surfaces that we are only beginning to decipher.An in-depth understanding of cellular proteomes requires knowledge of protein subcellular topology, assembly in macromolecular complexes, and modification and degradation of poplypeptides. Escherichia coli, a model organism for many such studies, is by far the best studied. The genomes of strain K-12 derivatives MG1655 and W3110 have been sequenced (1, 2), and >75% of their genes have been functionally assigned (3). Almost 90% of the K-12 proteome has been identified experimentally, and >73% of its proteins have known structures (4, 5). Moreover, the genomes of another 38 E. coli strains have been determined (see EcoliWiki for details).In E. coli, like in all Gram-negative bacteria, the bacterial cell envelope comprises the plasma or inner membrane and the outer membrane, which are separated by the periplasmic space. The inner membrane encloses the cytoplasm and is a dynamic substructure. It harbors a wide variety of proteins that function in vital cell processes such as the trafficking of ions, molecules, and macromolecules; cell division; environmental sensing; lipid, polysaccharide, and peptidoglycan biosynthesis; and metabolism. Inner membrane proteins either fully span the lipid bilayer using one or more hydrophobic transmembrane helices (integral) or are bound either directly to phospholipid components or via protein–protein interactions to the surface of the membrane (peripheral) (6) (Fig. 1A). Peripheral inner membrane proteins exist on either side of the membrane and may be recruited in membrane-associated complexes on demand (7). Peripheral inner membrane proteins on the cytoplasmic side constitute a sub-proteome of central importance because of their interaction with the cytoplasmic proteome, the nucleoid, and most of the cell''s metabolism. Thanks to their soluble character and the nature of their interactions with the membrane (mostly electrostatic and moderate hydrophobic interactions (7)), peripheral inner membrane proteins can be extracted using high salt concentrations, extreme pH levels, or chaotropes without disrupting the lipid bilayer (8–11). In contrast, the solubilization of integral proteins requires amphiphilic detergents in order to displace the membrane phospholipids and maintain them as soluble in aqueous solutions (12).Open in a separate windowFig. 1.Bioinformatics and experimental workflow for characterizing peripheral inner membrane proteins. A, schematic representation of the subcellular localization of the E. coli inner membrane peripherome. Protein topology assignment is based on the cellular compartment: A, cytoplasmic; B, integral inner membrane proteins; F1, peripheral inner membrane proteome; r, ribosome. B, schematic diagram for PIM protein annotation. 130 cytoplasmic and PIM E. coli K-12 proteins were downloaded from Uniprot (November 2010) (81) and EchoLOCATION (23). A set of bioinformatics tools was used to predict topologies and features of the unassigned and differently assigned proteins and to further validate existing protein annotations (see supplemental text). For the annotation of additional peripheral membrane proteins, the literature was extensively searched. Additional, other E. coli K-12 databases containing gene ontology annotations (84, 85) and protein homologies through BLAST (44) were employed. Homologues of curated E. coli K-12 proteins were identified in E. coli BL21(DE3) (supplemental Table S1A). C, preparation strategy for detecting the E. coli inner membrane peripherome via nanoLC-MS/MS. Inverted membrane vesicles (IMVs) were isolated and washed extensively with the indicated chemical agents to extract cytoplasmic and PIM proteins (“IMVs washed”), and then their surface was trypsinized (gray arrow). Following digestion, soluble peptides were analyzed via nanoLC-MS/MS. D, protein enrichment at different sample preparation conditions. Top: Relative percentage of proteins detected with the proteolysis approach. Proteins are classified here in three major categories: cytoplasmic (A), ribosomal (r), and peripheral (F1). The bar graphs indicate the percentage of proteins in each category relative to the proteins in other categories at a given sample preparation condition. Bottom: Heat maps showing relative quantities of individual proteins at different sample preparation conditions. Perseus (version 1.2.0.16), a part of the MaxQuant bioinformatics platform, was used for the construction of the heat map (86). A top-three label-free quantitative method was employed (27). Individual protein values across the various treatments are given in supplemental Table S3B.Unlike the cytoplasmic proteome of E. coli, which has been extensively characterized (13), its membrane sub-proteome is still poorly defined. Of 1133 predicted integral inner membrane proteins, only half were experimentally identified through proteomics approaches (14). These figures are constantly being re-evaluated,² but most protein identifications appear robust. In contrast to integral inner membrane proteins, bioinformatics prediction of peripheral inner membrane proteins is currently not possible because they are not known to possess any specific features. Despite the occasional designation of partner proteins identified as peripheral in studies that target inner membrane complexes (15–21), no systematic effort has been undertaken to analyze the peripheral inner membrane proteome.Here we have used a multi-pronged strategy employing bioinformatics, biochemistry, proteomics, and complexomics to systematically determine the peripheral inner membrane proteome of E. coli. We focus exclusively on the peripheral inner membrane proteome that faces the cytoplasm, referred to hereinafter as PIM,¹ and do not analyze peripheral inner membrane proteins residing on the periplasm. Manually curated and re-evaluated topology of the E. coli K-12 proteome was extrapolated to the non-K-12 strain BL21(DE3) (95% proteome homology to K-12) (22). By combining various biochemical treatments, we determined experimentally that several cytoplasmic proteins are also novel PIM proteins, and many of them participate in protein complexes associated with the membrane. Collectively, we demonstrate that a significant, previously unsuspected percentage of the expressed polypeptides constitute the PIM proteome.     

Virulence plasmid-associated sensitivity to rifampicin and novobiocin in Escherichia coli

Introduction of the ColV, I-K94 or ColV-K30 plasmid into Escherichia coli K12 resulted in increased sensitivity to growth inhibition and killing by rifampicin and novobiocin. For increased rifampicin sensitivity, both transfer and colicin components had to be present whereas transfer components plus another unidentified ColV component were needed for novobiocin sensitivity. The Co1 factor Co1B-K98 conferred increased rifampicin sensitivity, an effect not dependent on transfer components, but this plasmid had no effect on the novobiocin sensitivity of E. coli. The effects of ColV, I-K94 on rifampicin sensitivity were fully reversed by magnesium ions but Mg²⁺ had only a slight effect on the novobiocin inhibition of the Co1V, I-K94⁺ strain and the rifampicin inhibition of the Co1B-K98⁺ strain. Prior growth at 25C greatly reduced the effects of Co1V, I-K94 on rifampicin and novobiocin sensitivity but had essentially no effect on rifampicin sensitivity of the Co1B-K98⁺ strain.     

致肾盂肾炎大肠杆菌的毒力因子和调控

致肾盂肾炎大肠杆菌引起人的尿路感染,它的毒力因子包括表面毒力因子和分泌毒力因子两大类。表面毒力因子包括菌毛、鞭毛、黏附素和多糖类物质,主要在细菌的侵染过程中起作用。分泌毒力因子主要是溶血素、细胞毒性坏死因子等毒素蛋白,主要对宿主细胞产生毒力作用。本文简要综述致肾盂肾炎大肠杆菌毒力因子分泌所需要的5种分泌机制,并论及毒力因子的宏观调控和影响毒力调控的因素。     

Proteome response of Escherichia coli fed-batch culture to temperature downshift

During fed-batch cultivation of Escherichia coli K-12, the proteomic response to a temperature downshift from 37 to 20°C was quantitatively monitored and analyzed by using two-dimensional electrophoresis. When the temperature of exponentially growing E. coli K-12 culture was downshifted to 20°C, the synthesis level of 57 intracellular proteins showed significant changes for a prolonged period of time, compared to the fed-batch culture controlled at 37°C. Thus, these proteins are regarded as important stress proteins responsive to cold shock, which were analyzed by using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and identified using the E. coli SWISS-2DPAGE database. Most of the identified proteins were shown to be involved in energy metabolism, several cellular molecule biosynthetic pathways and catabolism, cell processes, flagellar biosynthesis and motility, and protein translation and folding. The systematic approach to the monitoring of proteomic responses and the detailed analysis results reported in this article would be useful in understanding the metabolic adaptation to lowered culture temperature and designing efficient fermentation strategies for the production of recombinant proteins and metabolites using E. coli strains.     

The Escherichia coli Proteome: Past, Present, and Future Prospects

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.     

Virulence of Escherichia coli Serotypes for Mice

A study was undertaken to determine whether virulence in mice could be used to assess the pathogenicity of a variety of Escherichia coli serotypes. Sixty-one E. coli strains isolated from animals, poultry, or humans were serotyped to determine their O, K, and H antigens, and were administered to mice via the intraperitoneal route with and without a mucin adjuvant. The ld(50) dose was then determined for each serotype. The results indicated that the source of the serotype may be associated with virulence for mice. Serotypes isolated from nonenteric, systemic sources showed a greater virulence for mice inoculated intraperitoneally than did the enteric and the nonenteric, nonsystemic (localized) isolates. It was observed that not all serotypes belonging to a specific serogroup were virulent for mice and that the presence or absence of a K antigen had no effect on the virulence of strains of one serotype.     

Core and panmetabolism in Escherichia coli

Escherichia coli exhibits a wide range of lifestyles encompassing commensalism and various pathogenic behaviors which its highly dynamic genome contributes to develop. How environmental and host factors shape the genetic structure of E. coli strains remains, however, largely unknown. Following a previous study of E. coli genomic diversity, we investigated its diversity at the metabolic level by building and analyzing the genome-scale metabolic networks of 29 E. coli strains (8 commensal and 21 pathogenic strains, including 6 Shigella strains). Using a tailor-made reconstruction strategy, we significantly improved the completeness and accuracy of the metabolic networks over default automatic reconstruction processes. Among the 1,545 reactions forming E. coli panmetabolism, 885 reactions were common to all strains. This high proportion of core reactions (57%) was found to be in sharp contrast to the low proportion (13%) of core genes in the E. coli pangenome, suggesting less diversity of metabolic functions compared to that of all gene functions. Core reactions were significantly overrepresented among biosynthetic reactions compared to the more variable degradation processes. Differences between metabolic networks were found to follow E. coli phylogeny rather than pathogenic phenotypes, except for Shigella networks, which were significantly more distant from the others. This suggests that most metabolic changes in non-Shigella strains were not driven by their pathogenic phenotypes. Using a supervised method, we were yet able to identify small sets of reactions related to pathogenicity or commensalism. The quality of our reconstructed networks also makes them reliable bases for building metabolic models.     

Pathogenomics of the Virulence Plasmids of Escherichia coli

Summary: Bacterial plasmids are self-replicating, extrachromosomal elements that are key agents of change in microbial populations. They promote the dissemination of a variety of traits, including virulence, enhanced fitness, resistance to antimicrobial agents, and metabolism of rare substances. Escherichia coli, perhaps the most studied of microorganisms, has been found to possess a variety of plasmid types. Included among these are plasmids associated with virulence. Several types of E. coli virulence plasmids exist, including those essential for the virulence of enterotoxigenic E. coli, enteroinvasive E. coli, enteropathogenic E. coli, enterohemorrhagic E. coli, enteroaggregative E. coli, and extraintestinal pathogenic E. coli. Despite their diversity, these plasmids belong to a few plasmid backbones that present themselves in a conserved and syntenic manner. Thanks to some recent research, including sequence analysis of several representative plasmid genomes and molecular pathogenesis studies, the evolution of these virulence plasmids and the implications of their acquisition by E. coli are now better understood and appreciated. Here, work involving each of the E. coli virulence plasmid types is summarized, with the available plasmid genomic sequences for several E. coli pathotypes being compared in an effort to understand the evolution of these plasmid types and define their core and accessory components.     

Virulence genotypes of Escherichia coli strains from bacteremia in relation to phylogeny

Bacteremia is the principal way of dissemination of local infections to distant organs. Escherichia coli bacteremia is almost always clinically significant, suggesting an increased risk of developing sepsis syndrome. Fifty-one E. coli bloodstream human isolates were analyzed using PCR technique for several molecular markers associated with extraintestinal virulence, and their phylogenetic group assignment, taking into account the link between the phylogenetic background and the intrinsic virulence of this species. Sixteen virulence genotypes have been identified, the majority of the blood isolates carrying the association of two genes. The genes encoding type 1 fimbria and aerobactin had the highest prevalence. As a confirmation of other studies, the strains assigned to E. coli phylogenetic group B2 exhibited the highest concentration of virulence genes, and represented almost half of the clinical blood isolates. The multifactorial virulence of E. coli strains isolated from invasive infections reflects a phylogenetic inheritance, and supports the concept of ExPEC pathotype as a subset of E. coli population involved in human infectious diseases. The surveillance of geographical variation of E. coli pathogenic clones is useful for epidemiological analysis.     

Pathways of disulfide bond formation in Escherichia coli

Disulfide bond formation is required for the correct folding of many secreted proteins. Cells possess protein-folding catalysts to ensure that the correct pairs of cysteine residues are joined during the folding process. These enzymatic systems are located in the endoplasmic reticulum of eukaryotes or in the periplasm of Gram-negative bacteria. This review focuses on the pathways of disulfide bond formation and isomerization in bacteria, taking Escherichia coli as a model.     

Virulence characteristics and antimicrobial susceptibility of uropathogenic Escherichia coli strains

Eight virulence factors associated with uropathogenic Escherichia coli (UPEC) were investigated in 204 clinical isolates of E. coli recovered from urine cultures at counts ≥10(5). The bacteria were classified into two groups according to the number of leukocytes in urine samples from which they were isolated: group I ≤8 leukocytes/hpf, 104 strains; group II >8 leukocytes/hpf, 100 strains. Two multiplex PCR systems were used to detect genes encoding adhesin P (pap), adhesin S (sfa), afimbrial adhesin I (afa), siderophore aerobactin (aer), alpha-hemolysin (hly), cytotoxic necrotizing factor type 1 (cnf1), and traT associated with serum resistance. The PAI marker for the virulence island identified in strains CFT072 and CVD432, a marker of enteroaggregative E. coli, was also investigated using PCR. The susceptibility profile of E. coli strains was determined by disk diffusion method. Ninety percent UPEC showed at least one of the virulence genes, the prevalence being traT (76%), aer (41%), PAI (32%), sfa (26%), pap (25%), cnf1 (18%), afa (6%), and hly (5%). There was no significant difference in the distribution of virulence genes between groups I and II. A significantly higher degree of virulence was detected in UPEC group II. The CVD432 gene was not detected in any of the UPECs. Fifty-nine percent of the strains were resistant to at least one of the antimicrobials that we tested; the most common being resistance to ampicillin (51%) and trimethoprim-sulfamethoxazole (44%).     

Escherichia coli Proteome Microarrays Identified the Substrates of ClpYQ Protease

Proteolysis is a vital mechanism to regulate the cellular proteome in all kingdoms of life, and ATP-dependent proteases play a crucial role within this process. In Escherichia coli, ClpYQ is one of the primary ATP-dependent proteases. In addition to function with removals of abnormal peptides in the cells, ClpYQ degrades regulatory proteins if necessary and thus let cells adjust to various environmental conditions. In E. coli, SulA, RcsA, RpoH and TraJ as well as RNase R, have been identified as natural protein substrates of ClpYQ. ClpYQ contains ClpY and ClpQ. The ATPase ClpY is responsible for protein recognition, unfolding, and translocation into the catalytic core of ClpQ. In this study, we use an indirect identification strategy to screen possible ClpY targets with E. coli K12 proteome chips. The chip assay results showed that YbaB strongly bound to ClpY. We used yeast two-hybrid assay to confirm the interactions between ClpY and YbaB protein and determined the K_d between ClpY and YbaB by quartz crystal microbalance. Furthermore, we validated that YbaB was successfully degraded by ClpYQ protease activity using ClpYQ in vitro and in vivo degradation assay. These findings demonstrated the YbaB is a novel substrate of ClpYQ protease. This work also successfully demonstrated that with the use of recognition element of a protease can successfully screen its substrates by indirect proteome chip screening assay.     

Escherichia coli Genes and Pathways Involved in Surviving Extreme Exposure to Ionizing Radiation

To further an improved understanding of the mechanisms used by bacterial cells to survive extreme exposure to ionizing radiation (IR), we broadly screened nonessential Escherichia coli genes for those involved in IR resistance by using transposon-directed insertion sequencing (TraDIS). Forty-six genes were identified, most of which become essential upon heavy IR exposure. Most of these were subjected to direct validation. The results reinforced the notion that survival after high doses of ionizing radiation does not depend on a single mechanism or process, but instead is multifaceted. Many identified genes affect either DNA repair or the cellular response to oxidative damage. However, contributions by genes involved in cell wall structure/function, cell division, and intermediary metabolism were also evident. About half of the identified genes have not previously been associated with IR resistance or recovery from IR exposure, including eight genes of unknown function.     

Antibody Profiling of Bipolar Disorder Using Escherichia coli Proteome Microarrays

To profile plasma antibodies of patients with bipolar disorder (BD), an E. coli proteome microarray comprising ca. 4200 proteins was used to analyze antibody differences between BD patients and mentally healthy controls (HCs). The plasmas of HCs and patients aged 18–45 years with bipolar I disorder (DSM-IV) in acute mania (BD-A) along with remission (BD-R) were collected. The initial samples consisting of 19 BD-A, 20 BD-R, and 20 HCs were probed with the microarrays. After selecting protein hits that recognized the antibody differences between BD and HC, the proteins were purified to construct BD focus arrays for training diagnosis committees and validation. Additional six BD-A, six BD-R, six HCs, and nine schizophrenic disorder (SZ, as another psychiatric control) samples were individually probed with the BD focus arrays. The trained diagnosis committee in BD-A versus HC combined top six proteins, including rpoA, thrA, flhB, yfcI, ycdU, and ydjL. However, the optimized committees in BD-R versus HC and BD-A versus BD-R were of low accuracy (< 0.6). In the single blind test using another four BD-A, four HC, and four SZ samples, the committee of BD-A versus HC was able to classify BD-A versus HC and SZ with 75% sensitivity and 80% specificity that both HC and SZ were regarded as negative controls. The consensus motif of the six proteins, which form the committee of BD-A versus HC, is [KE]DIL[AG]L[LV]I[NL][IC][SVKH]G[LV][VN][LV] by Gapped Local Alignment of Motifs. We demonstrated that the E. coli proteome microarray is capable of screening BD plasma antibody differences and the selected proteins committee was successfully used for BD diagnosis with 79% accuracy.The etiology and genetic contributions of bipolar disorder (BD)¹ largely remain unknown (1). Because of the presumed high level of etiologic heterogeneity and the overlap of dimensions across mood disorders and schizophrenia (2), the main difficulty in making an exact diagnosis for psychiatric disorder is the lack of pathological biochemical index (3). However, several lines of evidence support that various immunomodulatory factors, such as cytokine and soluble cytokine receptor, play an integral role in the pathophysiology of bipolar disorder (4–7). For example, several studies have reported that cell-mediated immunity cytokine abundance is correlated with mood state (8, 9). Our early works also found that higher levels of soluble interleukin-2 receptor (sIL-2R) (5, 10) and interleukin 1 receptor antagonist (IL-1Ra) (5, 11) are accompanied with bipolar mania. Furthermore, the abnormalities of total immunoglobulins levels in body fluid are observed in BD patients (12, 13).The possibility of biomarkers for assisting BD diagnosis has been recently highlighted (14–16). Tumor necrosis factor alpha (TNF-α), 3-nytrotrosine, interleukin-6, interleukin-10, and brain-derived neurotrophic factor in body fluids are potentially useful for classifying stages of BD (15). Nevertheless, they are not specific for distinguishing from other psychiatric diseases (17). Chronic inflammation exists in medicated bipolar patients displaying varied correlations with leptin, insulin, soluble TNF receptor-1 (sTNF-R1), and IL-1Ra (11). Notwithstanding, controversy exists as to whether these phenomena are state-dependent (5), normalize in remission (18), or represent trait markers exacerbated by the affective episodes (19). These discrepancies may be explained by heterogeneity in mood state, methodological differences, and not controlling for known confounds, such as obesity (6). In addition to inflammatory markers, increasing production of antibodies (20–22) and immunoglobulins (23, 24) may be implicated with BD.In recent years, proteomic technologies based on mass spectrometry have been increasingly used, especially in the search for diagnostic and prognostic biomarkers in neuropsychiatric disorders (25). Protein microarrays have been demonstrated as an effective high throughput platform for analysis of aberrant immune responses in diseases (26–29). It is hypothesized that the trait or state-dependent biomarkers of bipolar disorder may exist. We attempted to identify a committee of proteins for the diagnosis of BD through employing the ca. 4200 E. coli proteins in a microarray format. The two-phase strategy for identification and validation protein hits (30) was used in this study. Although the antigens on the microarray may not be directly associated with BD, this microarray provided hundreds of thousands of epitopes for analyzing antibody profiles of plasma samples in a high throughput fashion.