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A proteomic view on the cell biology of Staphylococcus aureus proteome

ABSTRACT: The genome sequence is the “blue-print of life”, but proteomics is required to relate this blue-print of life to the actual physiology of living cells. Because of their low complexity – only about 2.000 proteins make a Staphylococcus aureus cell viable – bacteria are excellent model systems to identify the entire protein assembly of a living organism. Many of the studies on physiological proteomics of bacteria, however, still rely on 2D gel-based technologies that visualize only a minor part of the proteome. In this study it will be shown that the majority of proteins expressed in the Gram-positive human pathogen S. aureus can be identified and even quantified by a combination of gel-based and gel-free approaches, the latter having undergone formidable improvements over the last 10 years. S. aureus has been the model of choice for our proteomic studies, because it combines high pathogenicity with increasing antibiotic resistance, which calls for new leads in the development of anti-staphylococcal therapy. On the way towards the entire proteome of S. aureus, we analysed four subproteomic fractions: cytosolic proteins, membrane-bound proteins, cell-surface associated and extracellular proteins, the two latter fractions containing most of the virulence factors. To obtain quantitative results, we compared growing cells and non-growing/stationary phase cells using the 15N metabolic labelling approach. With almost 2.000 proteins, 80 % of the expressed proteome was identified and even quantified in growing and non-growing cells. A comprehensive inventory of the entire protein assembly during the two physiologically distinct states is presented, which integrates data ranging from gene expression to subcellular localization. Three major protein classes were distinguished: (i) Proteins induced in the stationary phase only (ii) Vegetative proteins, no longer synthesized but stable in the stationary phase (iii) Vegetative proteins, no longer required in non-growing cells and finally degraded. This quantitative proteomics approach for growing/non-growing cells, which represents a proof-of-principle for whole-cell physiological proteomics, can now be extended to address particular infection-relevant physiological states. Importantly, the present model study represents the highest coverage of a bacterial proteome so far (except low complexity bacteria). It thus paves the way towards a new understanding of cell physiology and pathophysiology of S. aureus and related pathogenic bacteria, opening a new era in infection-related research on this crucial pathogen.

ORGANISM(S): Staphylococcus aureus subsp. aureus N315 Staphylococcus aureus subsp. aureus COL Staphylococcus aureus subsp. aureus Mu50 Staphylococcus aureus subsp. aureus MRSA252 Staphylococcus aureus Staphylococcus aureus subsp. aureus NCTC 8325 Staphylococcus aureus subsp. aureus USA300 Staphylococcus aureus subsp. aureus MW2 Staphylococcus aureus subsp. aureus MSSA476

PROVIDER: GSE15060 | GEO | 2009/11/12

SECONDARY ACCESSION(S): PRJNA114829

REPOSITORIES: GEO

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Project description:The genome sequence is the “blue-print of life”, but proteomics is required to relate this blue-print of life to the actual physiology of living cells. Because of their low complexity – only about 2.000 proteins make a Staphylococcus aureus cell viable – bacteria are excellent model systems to identify the entire protein assembly of a living organism. Many of the studies on physiological proteomics of bacteria, however, still rely on 2D gel-based technologies that visualize only a minor part of the proteome. In this study it will be shown that the majority of proteins expressed in the Gram-positive human pathogen S. aureus can be identified and even quantified by a combination of gel-based and gel-free approaches, the latter having undergone formidable improvements over the last 10 years. S. aureus has been the model of choice for our proteomic studies, because it combines high pathogenicity with increasing antibiotic resistance, which calls for new leads in the development of anti-staphylococcal therapy. On the way towards the entire proteome of S. aureus, we analysed four subproteomic fractions: cytosolic proteins, membrane-bound proteins, cell-surface associated and extracellular proteins, the two latter fractions containing most of the virulence factors. To obtain quantitative results, we compared growing cells and non-growing/stationary phase cells using the 15N metabolic labelling approach. With almost 2.000 proteins, 80 % of the expressed proteome was identified and even quantified in growing and non-growing cells. A comprehensive inventory of the entire protein assembly during the two physiologically distinct states is presented, which integrates data ranging from gene expression to subcellular localization. Three major protein classes were distinguished: (i) Proteins induced in the stationary phase only (ii) Vegetative proteins, no longer synthesized but stable in the stationary phase (iii) Vegetative proteins, no longer required in non-growing cells and finally degraded. This quantitative proteomics approach for growing/non-growing cells, which represents a proof-of-principle for whole-cell physiological proteomics, can now be extended to address particular infection-relevant physiological states. Importantly, the present model study represents the highest coverage of a bacterial proteome so far (except low complexity bacteria). It thus paves the way towards a new understanding of cell physiology and pathophysiology of S. aureus and related pathogenic bacteria, opening a new era in infection-related research on this crucial pathogen. For the DNA-microarray experiment RNA was extracted from two independently grown cultures. One was grown in 14N labelled BioExpress medium and the other in 15N labelled BioExpress medium. Samples of exponentially growing cells (OD600=0.5) and of cell at 5h after entry into stationary phase were harvested from every culture. Equal amounts of all four RNAs were pooled and the pool was used as common reference (Cy3-labeld) for the four sample RNAs (Cy3). Thus, in total four hybridizations, one for each sample versus the common reference, were performed.

Project description:To unravel molecular targets involved in glycopeptide resistance, three isogenic strains of Staphylococcus aureus with different susceptibility levels to vancomycin or teicoplanin were subjected to whole-genome microarray-based transcription and quantitative proteomic profiling. Quantitative proteomics performed on membrane extracts showed exquisite inter-experimental reproducibility permitting the identification and relative quantification of >30% of the predicted S. aureus proteome. In the absence of antibiotic selection pressure, comparison of stable resistant and susceptible strains revealed 94 differentially expressed genes and 178 proteins. As expected, only partial correlation was obtained between transcriptomic and proteomic results during stationary-phase. Application of massively parallel methods identified one third of the complete proteome, a majority of which was only predicted based on genome sequencing, but never identified to date. Several over‑expressed genes represent previously reported targets, while series of genes and proteins possibly involved in the glycopeptide resistance mechanism were discovered here, including regulators, global regulator attenuator, hyper‑mutability factor or hypothetical proteins. Gene expression of these markers was confirmed in a collection of genetically unrelated strains showing altered susceptibility to glycopeptides. Our proteome and transcriptome analyses have been performed during stationary‑phase of growth on isogenic strains showing susceptibility or intermediate level of resistance against glycopeptides. Altered susceptibility had emerged spontaneously after infection with a sensitive parental strain, thus not selected in vitro. This combined analysis allows the identification of hundreds of proteins considered, so far as hypothetical protein. In addition, this study provides not only a global picture of transcription and expression adaptations during a complex antibiotic resistance mechanism but also unravels potential drug targets or markers that are constitutively expressed by resistant strains regardless of their genetic background, amenable to be used as diagnostic targets. Keywords: Molecular markers, antibiotic resistance, glycopeptides, growth-phase

Project description:Specific Lactobacillus strains are marketed as probiotics i.e. health promoting organisms. As previously shown in vivo, L. plantarum WCFS1 modulates immune responses via nuclear factor (NF)kB in a growth phase dependent manner, likely caused by changed microbial cell surface characteristics. Here, we identified the differential cell-surface proteome composition of L. plantarum WCFS1 cells derived from the mid-logarithmic and late stationary growth phase by surface trypsination of intact bacterial cells, followed by a combined 1D- and 2D-LC-MS shotgun proteomics strategy, and growth phase dependent transcriptome analysis. Overall, these analyses led to the identification of 31 surface proteins differentially present in the logarithmic and stationary phase, whereas additional 17 and 33 proteins appeared solely present in stationary and logarithmic phase of growth, respectively. From 75% of predicted surface related genes displaying relatively high expression levels, the corresponding proteins were detected, demonstrating a high correlation between transcriptome and proteome profiles. The impact of cell-surface peptide fractions on cellular host responses was evaluated using NFkB promoter activation assays in intestinal epithelial cells, revealing that only late stationary phase derived peptides are capable of attenuating flagellin and cell envelope induced NFkB activation. Overall our data mechanistically expand earlier observations that revealed growth-phase dependent immunomodulation by L. plantarum WCFS1, leading to a typical adjuvant- or tolerance-like response after exposure to stationary phase harvested bacterial cells. Sub-fractionation of the late stationary phase peptide fraction revealed a sub-set of strong attenuator peptides able to manipulate NFkB related pathways to hereby attenuate pro-inflammatory signaling. Samples were hybridizedin a loop design linking each timepoint to the next as well as the previous timepoint. Additional hybridizations were introduced linking each sample three timepoints forward and three timepoints back. All samples were hybridized unsing at least each of the two dyes once.

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A proteomic view on the cell biology of Staphylococcus aureus proteome

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