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.

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.

			Action	DRS
	20100829RL_sample65.wiff.-1.mgf	Wiff
	20100829RL_sample66.wiff.-1.mgf	Wiff
	20110128Rachel52.wiff.-1.mgf	Wiff
	20110128Rachel53.wiff.-1.mgf	Wiff
	20110207Rachel55.wiff.-1.mgf	Wiff

Dataset Information

Blue native gel electrophoresis of Syntrophus aciditrophicus proteome

Dataset's files

Publications

Syntrophus aciditrophicus uses the same enzymes in a reversible manner to degrade and synthesize aromatic and alicyclic acids.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets