Genome-wide identification of the subcellular localization of the Escherichia coli B proteome using experimental and computational methods

Subcellular Protein Fractionation in Legionella pneumophila and Preparation of the Derived Sub-proteomes for Analysis by Mass Spectrometry

Classical proteomic techniques are perfectly suited to reflect changes in the metabolism by detection of changed protein synthesis rates and protein abundances in a global protein-centered analysis. Although the proteome of microbes is considered as rather low complex, usually the subcellular fractionation of proteins leads to higher proteome coverage which might be important for the proteome quantification. Additionally, such fractionation provides the possibility to detect changes in the protein localization as well as the protein abundance in single sub-proteomes. Here, a workflow for subcellular fractionation of Legionella pneumophila into cytosolic, periplasmic, membrane, and extracellular proteins for global proteome analyses is provided. The methods included in this workflow can be used to analyze the adaptation of L. pneumophila to different environmental and nutritional situations during infection or during different life cycle stages including planktonic or biofilm phase.

Genomic and transcriptomic landscape of Escherichia coli BL21(DE3)

Escherichia coli BL21(DE3) has long served as a model organism for scientific research, as well as a workhorse for biotechnology. Here we present the most current genome annotation of E. coli BL21(DE3) based on the transcriptome structure of the strain that was determined for the first time. The genome was annotated using multiple automated pipelines and compared to the current genome annotation of the closely related strain, E. coli K-12. High-resolution tiling array data of E. coli BL21(DE3) from several different stages of cell growth in rich and minimal media were analyzed to characterize the transcriptome structure and to provide supporting evidence for open reading frames. This new integrated analysis of the genomic and transcriptomic structure of E. coli BL21(DE3) has led to the correction of translation initiation sites for 88 coding DNA sequences and provided updated information for most genes. Additionally, 37 putative genes and 66 putative non-coding RNAs were also identified. The panoramic landscape of the genome and transcriptome of E. coli BL21(DE3) revealed here will allow us to better understand the fundamental biology of the strain and also advance biotechnological applications in industry.

Proteome-wide identification of predominant subcellular protein localizations in a bacterial model organism

Proteomics data provide unique insights into biological systems, including the predominant subcellular localization (SCL) of proteins, which can reveal important clues about their functions. Here we analyzed data of a complete prokaryotic proteome expressed under two conditions mimicking interaction of the emerging pathogen Bartonella henselae with its mammalian host. Normalized spectral count data from cytoplasmic, total membrane, inner and outer membrane fractions allowed us to identify the predominant SCL for 82% of the identified proteins. The spectral count proportion of total membrane versus cytoplasmic fractions indicated the propensity of cytoplasmic proteins to co-fractionate with the inner membrane, and enabled us to distinguish cytoplasmic, peripheral inner membrane and bona fide inner membrane proteins. Principal component analysis and k-nearest neighbor classification training on selected marker proteins or predominantly localized proteins, allowed us to determine an extensive catalog of at least 74 expressed outer membrane proteins, and to extend the SCL assignment to 94% of the identified proteins, including 18% where in silico methods gave no prediction. Suitable experimental proteomics data combined with straightforward computational approaches can thus identify the predominant SCL on a proteome-wide scale. Finally, we present a conceptual approach to identify proteins potentially changing their SCL in a condition-dependent fashion.

BIOLOGICAL SIGNIFICANCE

The work presented here describes the first prokaryotic proteome-wide subcellular localization (SCL) dataset for the emerging pathogen B. henselae (Bhen). The study indicates that suitable subcellular fractionation experiments combined with straight-forward computational analysis approaches assessing the proportion of spectral counts observed in different subcellular fractions are powerful for determining the predominant SCL of a large percentage of the experimentally observed proteins. This includes numerous cases where in silico prediction methods do not provide any prediction. Avoiding a treatment with harsh conditions, cytoplasmic proteins tend to co-fractionate with proteins of the inner membrane fraction, indicative of close functional interactions. The spectral count proportion (SCP) of total membrane versus cytoplasmic fractions allowed us to obtain a good indication about the relative proximity of individual protein complex members to the inner membrane. Using principal component analysis and k-nearest neighbor approaches, we were able to extend the percentage of proteins with a predominant experimental localization to over 90% of all expressed proteins and identified a set of at least 74 outer membrane (OM) proteins. In general, OM proteins represent a rich source of candidates for the development of urgently needed new therapeutics in combat of resurgence of infectious disease and multi-drug resistant bacteria. Finally, by comparing the data from two infection biology relevant conditions, we conceptually explore methods to identify and visualize potential candidates that may partially change their SCL in these different conditions. The data are made available to researchers as a SCL compendium for Bhen and as an assistance in further improving in silico SCL prediction algorithms.

Comparison of the large-scale periplasmic proteomes of the Escherichia coli K-12 and B strains

Escherichia coli typically secretes many proteins into the periplasmic space, and the periplasmic proteins have been used for the secretory production of various proteins by the biotechnology industry. However, the identity of all of the E. coli periplasmic proteins remains unknown. Here, high-resolution periplasmic proteome reference maps of the E. coli K-12 and B strains were constructed and compared. Of the 145 proteins identified by tandem mass spectrometry, 61 proteins were conserved in the two strains, whereas 11 and 12 strain-specific proteins were identified for the E. coli K-12 and B strains, respectively. In addition, 27 proteins exhibited differences in intensities greater than 2-fold between the K-12 and B strains. The periplasmic proteins MalE and OppA were the most abundant proteins in the two E. coli strains. Distinctive differences between the two strains included several proteins that were caused by genetic variations, such as CybC, FliC, FliY, KpsD, MglB, ModA, and Ybl119, hydrolytic enzymes, particularly phosphatases, glycosylases, and proteases, and many uncharacterized proteins. Compared to previous studies, the localization of many proteins, including 30 proteins for the K-12 strain and 53 proteins for the B strain, was newly identified as periplasmic. This study identifies the largest number of proteins in the E. coli periplasm as well as the dynamics of these proteins. Additionally, these findings are summarized as reference proteome maps that will be useful for studying protein secretion and may provide new strategies for the enhanced secretory production of recombinant proteins.

Exoproteomics: exploring the world around biological systems

The term 'exoproteome' describes the protein content that can be found in the extracellular proximity of a given biological system. These proteins arise from cellular secretion, other protein export mechanisms or cell lysis, but only the most stable proteins in this environment will remain in abundance. It has been shown that these proteins reflect the physiological state of the cells in a given condition and are indicators of how living systems interact with their environments. High-throughput proteomic approaches based on a shotgun strategy, and high-resolution mass spectrometers, have modified the authors' view of exoproteomes. In the present review, the authors describe how these new approaches should be exploited to obtain the maximum useful information from a sample, whatever its origin. The methodologies used for studying secretion from model cell lines derived from eukaryotic, multicellular organisms, virulence determinants of pathogens and environmental bacteria and their relationships with their habitats are illustrated with several examples. The implication of such data, in terms of proteogenomics and the discovery of novel protein functions, is discussed.