A proteomic view of an important human pathogen--towards the quantification of the entire Staphylococcus aureus proteome

Abstract

The genome sequence is the "blue-print of life," but proteomics provides the link to the actual physiology of living cells. Because of their low complexity bacteria are excellent model systems to identify the entire protein assembly of a living organism. Here we show that the majority of proteins expressed in growing and non-growing cells of the human pathogen Staphylococcus aureus can be identified and even quantified by a metabolic labeling proteomic approach. S. aureus has been selected as model for this proteomic study, because it poses a major risk to our health care system by combining high pathogenicity with an increasing frequency of multiple antibiotic resistance, thus requiring the development of new anti-staphylococcal therapy strategies. Since such strategies will likely have to target extracellular and surface-exposed virulence factors as well as staphylococcal survival and adaptation capabilities, we decided to combine four subproteomic fractions: cytosolic proteins, membrane-bound proteins, cell surface-associated and extracellular proteins, to comprehensively cover the entire proteome of S. aureus. This quantitative proteomics approach integrating data ranging from gene expression to subcellular localization in growing and non-growing cells is a proof of principle for whole-cell physiological proteomics that can now be extended to address physiological questions in infection-relevant settings. Importantly, with more than 1700 identified proteins (and 1450 quantified proteins) corresponding to a coverage of about three-quarters of the expressed proteins, our model study represents the most comprehensive quantification of a bacterial proteome reported to date. It thus paves the way towards a new level in understanding of cell physiology and pathophysiology of S. aureus and related pathogenic bacteria, opening new avenues for infection-related research on this crucial pathogen.

Figure 1. Analysis strategy for investigation of all sub-proteomic fractions of S. aureus.

The mixture of the differentially labeled cells has been carried out on different levels. For analysis of the cytosolic proteins and membrane proteins by LC-MS/MS the samples were mixed on protein level using aliquots with the same protein amount. For 2-D gel analysis identical amounts of protein were separated on individual gel. For the fraction of cell surface-associated proteins the cells were mixed prior to the biotinylation step in equal amounts. Extracellular proteins were mixed after the precipitation step.

Figure 2. Changes of protein expression pattern.

Framed sections are presented separately on right side of the figures. A 2-D gel-based expression analysis by Dual Channel Imaging: The overlay of the false color image of growing cells (green) and non-growing cells (red) is shown. This overlay results in the following color code: I. proteins probably no longer synthesized in non-growing cells but still present and more or less stable (yellow color), II. proteins no longer synthesized and even degraded in non-growing cells (green color) and III. proteins enriched in non-growing cells (red labelled). B GeLC-MS/MS-based expression analysis: Spot position derived from theoretical pI and theoretical molecular weight, spot size derived from spectral counts determined by Census software, spot color derived from log2 ratio of stat/exp, (green<−0.8, red>+0.8, yellow≥−0.8, ≤+0.8), Figure has been created in Microsoft Excel. C Growth curve and RNA content: Sampling points (arrows) are indicated in the growth curve (line graph). The relative decrease of total RNA content with decreasing growth rate is shown in the same graph (line with squares).

Figure 3. Comparative analysis of the cytosolic protein fraction.

A Qualitative analysis: In summary 1180 proteins were identified, including 1095 proteins predicted as cytosolic proteins (but also proteins predicted to be cell wall-associated - 13, membrane proteins – 29, extracellular proteins - 23 and lipoproteins -20). Thus, 61% of the predicted cytosolic proteins were identified. B Quantitative analysis: 1029 proteins have been quantified in summary in both approaches with an overlap of 520 proteins. With the 2-D gel-based approach 610 proteins could be quantified and 265 proteins were found altered in amount (175 proteins - labeled in red - in higher anount and 90 proteins - labeled in green - in lower amount in the stationary phaset). By GeLC-MS 939 proteins could be quantified and 320 proteins were changed in quantity (170 proteins - labeled in red - in higher and 150 proteins - labeled in green - in lower amount in the stationary phase). Therefore, about one third of the cytosolic proteome has to be considered as regulated.

Figure 4. Comparison of identified proteins depending on the expression strength on RNA level.

Up to 90% of the gene products of strongly expressed genes (definition of expressed genes – cf. Text S1) are identified, whereas low abundance proteins are clearly underrepresented among the identified proteins.

Figure 5. Coverage of gene functional categories.

The coverage of gene functional categories was displayed by using the KEGG orthology of S. aureus COL which was mapped in a Voronoi treemap layout . Treemaps display hierarchically organized information by using a space filling approach according to their hierarchy level (A). Smallest blue mosaic tiles correspond to proteins belonging to metabolic pathways (glycolysis, TCA etc.) which depict the metabolic brunches (e.g. carbohydrates, nucleotides) and which belong to metabolism at all. Changes in protein amount caused by starvation are indicated in shade of green (reduced level) and red (increased level). Proteins labeled in grey were not quantified. (B). Homogeneously regulated clusters have been indicated.

Figure 6. Numbers of theoretical and identified integral membrane proteins.

Comparison of identified integral membrane proteins with the numbers of theoretical membrane proteins considering predicted trans-membrane domains (TMDs). This comparison indicates that there was no bias against proteins with a higher number of TMDs.

Figure 7. Membrane proteins which were found in altered amount.

Several of the membrane proteins increased in amount in stationary phase are involved in the transport of amino acids or oligopeptides [from left to right: SACOL2451, SACOL2309, SACOL2452, SACOL2453, SACOL0993 (OppD), SACOL0992 (OppC), SACOL0994 (OppF), SACOL0995, SACOL1963, SACOL2450, SACOL0991 (OppB)], CPS (capsular polysaccharide) biosynthesis [SACOL0151 (Cap5P), SACOL0138 (Cap5C), SACOL0143 (Cap5H), SACOL0137 (Cap5B), SACOL0136 (Cap5A), SACOL0142 (Cap5G), SACOL0150 (Cap5O), SACOL0145 (Cap5J), glycerol utilization [SACOL0805, SACOL1514 (GpsA), SACOL2415 (Gpm), SACOL1320 (GlpK), SACOL1319 (GlpF), SACOL0200, SACOL0407 (GlpT)], or consumption of alternative sugars [SACOL1393 (GlcT), SACOL2663, SACOL0516, SACOL0178, SACOL0224, SACOL2316]. Of the proteins down-regulated in stationary phase numerous could be assigned to the category of iron uptake facilitation [SACOL2010, SACOL2277, SACOL2167, SACOL0799, SACOL0099 (SirA), SACOL0704] or to the class of cell surface proteins which are associated to pathogenicity of S. aureus [SACOL2348, SACOL0699 (Pbp4), SACOL2418 (Sbi), SACOL2291 (SsaA2), SACOL2652 (ClfB)]. Proteins indicated with a log2 ratio of 5 and -5 represent “on”/“off” proteins, respectively.

Figure 8. Summary of identified proteins.

This summary considers the predicted localization in the cell considering the expected gene expression. A: Electron microscopy picture of S. aureus COL with illustrated subcellular protein fractions. B: Identified proteins colored according to the predicted localization in the cell displayed in a theoretical 2-D gel. C: Summary of identified proteins in relation to expected gene expression. Software tools used for the prediction of protein localization are given in the full methods section (Text S1).