A proteomic view of cell physiology and virulence of Staphylococcus aureus

ARTICLE IN PRESS International Journal of Medical Microbiology 300 (2010) 76–87

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Mini Review

A proteomic view of cell physiology and virulence of Staphylococcus aureus ¨ Michael Hecker n, Dorte Becher, Stephan Fuchs, Susanne Engelmann Institut f¨ ur Mikrobiologie, Ernst-Moritz-Arndt-Universit¨ at, F.-L.-Jahn-Straße 15, D-17487 Greifswald, Germany

a r t i c l e in f o

Keywords: Physiological proteomics Entire proteome 2D gel electrophoresis MS-based proteomics Metabolism Stress/starvation responses Virulence factors Secretome

a b s t r a c t Staphylococcus aureus is a human pathogen that has advanced to a main problem in hospital settings since effective treatment options for infections caused by this pathogen are limited. Thus, new strategies to prevent and treat S. aureus infections and rapid diagnostic tools are urgently needed. The course of an S. aureus infection largely depends on successful adaptation to the host environment and a very complex and poorly understood interplay of bacterial virulence factors with each other and with host components. Over the last years, genome sequences of different S. aureus strains have been published permitting a high-throughput proteomic analysis of this pathogen. This review summarizes the impact of 2D gel- and mass spectrometry-based proteomic approaches on a more comprehensive understanding of S. aureus pathophysiology and virulence. We show that only a combination of both techniques allows a proteomic view which adequately considers all subproteomic fractions of a bacterium, i.e. cytosolic, membrane, cell surface-associated, and extracellular proteins. By this means, the majority of proteins expressed in S. aureus can be identified and even quantified. In addition, posttranslational processes such as protein secretion, modification, processing, damages, and degradation can be adequately studied. & 2009 Elsevier GmbH. All rights reserved.

Introduction Deciphering the ‘‘blue-print of life’’ of Haemophilus influenzae (Fleischmann et al., 1995) marked a turning point in life sciences: for the first time, the genome of an organism had been completely sequenced. Since then, the genome sequences of hundreds of organisms including human have been determined. The genome sequence alone, however, merely provides information on the potential inventory of organisms. Functional genomics techniques translate the blueprint of life into cellular functions, and particularly proteomics assumes a privileged position because it deals directly with the proteins, the main players of life processes. Because of their low complexity bacteria are excellent model systems to bring the genome sequence via the proteins to cell function since only a few hundreds of different proteins make a cell viable. The main model system of this study is Staphylococcus aureus, the causative agent of up to 40% of nosocomial infections including severe diseases such as osteomyelitis, endocarditis or sepsis. Because antibiotic resistances are spreading, current treatment strategies of S. aureus infections are becoming more and more ineffective. Because of the great impact of S. aureus as a pathogen, there is a comprehensive literature on infection biology of this bug focusing on the structure and function of virulence

n

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factors, on host-pathogen interaction and on the molecular mechanisms of antibiotic resistance. There is, however, only limited information available on basic cellular physiology as compared to well known model organisms such as Escherichia coli or Bacillus subtilis. It becomes more and more evident that cell physiology is a crucial issue for survival of the pathogen in the host and for pathogenicity in general. Thus, a ‘‘panorama view of proteomics’’ on cell physiology and virulence of S. aureus should help to come to a better and more comprehensive understanding of basal cell physiology and – as a consequence – to a better understanding of the behavior of the pathogen in the host. The post-genomic era of S. aureus began in 2001 when the first genome sequences of two reference strains were published (Kuroda et al., 2001). The S. aureus genome codes for about 2600 to 2700 proteins, including about 1000 proteins with still unknown functions. The genome sequence allowed the prediction of many metabolic reactions and regulators of gene expression, two-component systems, DNA-binding proteins, and two to three alternative sigma factors. In addition to a relatively stable core genome encoding housekeeping functions, but also some virulence factors, there is a more flexible gene pool encoding a different set of virulence factors (Holden and Lindsay, 2008). This variable and probably dispensable genome fraction belongs to plasmids, prophages and related mobile elements frequently organized in pathogenicity islands. In this review article, recent efforts to visualize the entire proteome of S. aureus and their implication on uncovering cell physiology (and virulence) will be summarized. Proteomics

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started in 1975 when O’Farrell published the famous twodimensional gel electrophoresis separation technique of proteins that allows the separation of thousands of proteins (O’Farrell, 1975). A few years later, Ruth Van Bogelen and Fred Neidhardt, the pioneers of physiological proteomics, used this technique to address crucial issues of cell physiology of E. coli (Neidhardt et al., 1980). Twenty years after O’Farrells milestone publication, the sequencing of entire genomes provided the basis for protein identification by sensitive mass spectrometry (MS) techniques at a large scale. In parallel, the development of mild ionization methods for peptides enabled high-throughput MS-based protein identification techniques. The combination of both, very sensitive and highly reproducible 2D protein separation techniques (Weiss ¨ and Gorg, 2009) and MS analysis of peptides, had a great impact on the establishment of proteomics as a new field of life sciences with many implications for bacterial physiology. Nowadays, physiological reactions can be followed using large-scale proteomics thus visualizing cellular events with unprecedented sensitivity. Novel insights in bacterial physiology such as the responses of bacterial cells to stress and starvation are now possible (VanBogelen, 2003; VanBogelen and Molloy, 2003; Hecker and ¨ Volker, 2004; Hecker et al., 2008). After the initial enthusiasm for gel-based proteomics in the 1990s, the limitations of the technique became evident: only a part of the bacterial proteomes can be visualized by this gel-based procedure. Many proteins, among them membrane proteins and low-abundance proteins – as the most prominent classes – escape detection by gel-based proteomics. To identify these still missing proteins on the way towards the elucidation of entire proteomes, novel proteomics techniques are required, which avoid the limitations of 2D gels. Especially the MS-based techniques have undergone dramatic developments during the last 15 years (Han et al., 2008). Nevertheless, gel-based proteomics is still a powerful tool to address many physiological issues because (i) most of the metabolic pathways and the most obvious stress/starvation responses can be visualized on 2D gels, (ii) very rapid stress/ starvation responses can be directly followed because gel-based proteomics can discriminate between protein synthesis (visualized by [35S]methionine pulse labeling) and protein level

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accumulated in the cells, and (iii) protein modifications such as protein processing, protein phosphorylation or protein damage, and even proteolysis at a proteome-wide scale can be visualized. MS-based procedures, on the other hand, are absolutely required to cover the entire proteome including membrane proteins or low-abundance proteins. In this review article, we will illustrate that a combination of gel- and MS-based approaches is the state of the art to visualize the entire proteome of bacteria in order to address physiological questions.

Gel-based proteomics, cell physiology and virulence of S. aureus Understanding general metabolism of S. aureus by using proteomics Gel-based proteomics provides an overview on the metabolism in growing or non-growing cells. Within the main proteomic windows of pI 4-7 and 6-11, almost 700 proteins of growing and non-growing S. aureus cells were identified by gel-based procedures (Fig. 1). In the pI 4-7 region, the most abundant proteins are metabolic enzymes, translational factors, or chaperones. In the alkaline region, the most abundant proteins are ribosomal proteins. Most of the metabolic pathways are covered by gelbased proteomics offering the possibility to reconstruct the active metabolism at a proteome-wide scale and to analyze the regulation of entire metabolic pathways. In a pioneering study, we visualized the strong activation of glycolysis and fermentation as well as a strong repression of the TCA cycle following shift to anaerobic conditions (Fig. 2) (Fuchs et al., 2007). Detailed kinetic data provide a ‘‘movie of anaerobic life of S. aureus’’ (Fig. 2A). Extending this initial analysis, we are currently analyzing changes in S. aureus proteome patterns upon the shift from aerobiosis to anaerobiosis using the AQUA (absolute quantification) technology to enable S. aureus systems biology. The AQUA technology will enable us to measure the number of protein molecules in an S. aureus cell. This quantitative data will be established for up to 300 key proteins involved in S. aureus metabolism. Thus, detailed views of the metabolism of the pathogen become available – a

Fig. 1. Cytoplasmic protein patterns of exponentially growing and stationary S. aureus COL cells false colored in green and red, respectively. Proteins less accumulated in the stationary phase appear in green and induced proteins appear in red. Most of the enzymes of the main metabolic pathways can be identified in the main window (pI 4 - 7) of gel-based proteomics. In the more alkaline region (pI 6 - 11), additional proteins can be found, e.g. many ribosomal proteins. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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Fig. 2. Changes in the cytoplasmic protein synthesis pattern of S. aureus COL in response to oxygen limitation. Changes in the protein synthesis pattern are visualized by gel-based proteomics. Newly synthesized proteins were labeled by [35S]methionine incorporation before and different times after oxygen restriction. The protein synthesis pattern of unstressed cells (immediately before oxygen limitation) is colored in green and that of cells grown in the absence of oxygen in red. All proteins induced after oxygen limitation appear in red and those repressed appear in green. The comparison of the protein synthesis patterns at different times (10, 20, 30, and 60 min) under anaerobic conditions provides expression profiles for all proteins spots detected on the gels (A). Synthesis patterns of selected proteins representing different branches of cellular metabolism: glycolysis, fermentation, pyruvate dehydrogenase, and miscellaneous. The protein synthesis pattern under aerobic conditions (0 min shown in green) was compared with the protein synthesis pattern at different times after shift to fermentation conditions (10, 20, 30, 60 min shown in red) and with that of an aerobically grown culture. The autoradiographs were normalized by using total normalization (B). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

prerequisite for a global understanding of S. aureus physiology and – ultimately – virulence. Initial studies using the ‘‘panorama view of proteomics‘‘ already demonstrated that in addition to established effects of oxygen starvation, such as activation of fermentation, novel

effects can be observed: oxygen starvation is encompassed by a strong induction of an ATPase/chaperon ClpL (Fuchs et al., 2007). Whereas the function and the induction mechanism of ClpL under anaerobic conditions remains to be determined, this result illustrates the power of proteomics in identifying novel key

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players of metabolism and in driving hypothesis generation (Fig. 2B). The dynamics of glycolysis, overflow metabolism, and TCA cycle and their involved enzymes were also followed for cells grown under glucose excess and under glucose limitation and starvation (Kohler et al., 2003, 2005; Seidl et al., 2009). An overflow metabolism (Crabtree effect) was postulated for cells grown at high glucose concentrations. Judging by protein abundance as determined by 2D gel-based approaches, measurements of metabolic products were undertaken. Thus, acetate has been found as the dominant overflow product. Moreover, pyruvate and acetoin have been detected to be excreted under these conditions. It is interesting to note that under anaerobic conditions lactate is the main fermentation product followed by acetate. Mutants with defects in the respiration chain (hemB and menD mutants) show a very similar phenotype as found for the wild type under anaerobic conditions indicating that either the redox poise or components of the respiratory chain or both are involved in cellular signaling of anaerobic adaptation (Kohler et al., 2003, 2008). From stress and starvation responses towards a stress/starvation proteomics signature library for S. aureus Stress and starvation are the rule and not the exception in almost all natural ecosystems. Therefore, adaptation to stress and starvation is crucial for bacterial survival in nature. In order to

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face stress and starvation, bacteria have developed a very complicated adaptational network consisting of many different stress and starvation boxes. Proteomics again is a good strategy to put all the proteins induced by the environmental stimuli into the stress/starvation boxes. The initial step in analyzing S. aureus stress/starvation responses is to look for all proteins that are induced by a certain stimulus since all these proteins together handle stress adaptation. The entire set of proteins induced by one stimulus forms a stimulon. For the definition of the stimulon structure, we have been using the dual-channel imaging technique developed by Bernhardt et al. (1999) for more than ten years. This technique allows a rapid allocation of proteins to environmental stimuli simply by looking for red-colored (newly synthesized) and greencolored (repressed) proteins. Two digitized images of 2D gels have to be generated and combined in alternate additive color channels. The first one showing accumulated proteins visualized by staining techniques is false-colored green. The second image showing the proteins radioactively labeled during a 5-min pulse with [35S]methionine is false-colored red. After the imposition of stress or starvation stimuli proteins not previously accumulated in the cell but newly induced (radioactively labeled but not yet stainable) are colored red. Identifying red proteins is a simple and very effective technique for finding all proteins induced by a stimulus and thus defining the stimulon structure (Fig. 3) ¨ (Hochgrafe et al., 2008; Wolf et al., 2008; Fuchs et al., 2007).

Fig. 3. Proteomic signatures of different stress conditions in S. aureus. Comparison of the protein synthesis profile of exponentially growing cells (green) with that of stressed S. aureus cells (red) reveals changes in protein synthesis that are particular for the respective stress stimuli. Cells were cultivated in synthetic medium and exposed to the respective stimulus at an OD500 of 0.5. Protein synthesis was analyzed by [35S]methionine labeling (5 min pulse) under control conditions and 10 min after imposition to stress. All proteins induced by one stimulus belong to a stimulon. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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Using this approach, we are going to establish a stress/starvation proteomics signature library for a set of environmental stimuli – including infection-related ones. This signature library is a useful toolbox for the comprehensive understanding of the physiological state of cells grown in a biofilm (Resch et al., 2006) or under infection-related conditions (Becker et al., 2006). The next step is to dissect stimulons into single regulons consisting of genes that are controlled by one global regulator. By comparing the transcriptome and proteome of a regulator mutant with the wild type under inducing conditions the genes belonging to the regulon can be described. Follow-up studies aiming at the understanding of the physiological role of the regulon as well as of the signal transduction pathways involved in the regulon activation are required for a comprehensive knowledge on the regulon function. The first proteomic view of a regulon of S. aureus was published for the SigB regulon almost 10 years ago (Gertz et al., 2000). It turned out that the function of the SigB regulon including the signal transduction pathway is totally different from that known for B. subtilis (Pane´-Farre´ et al., 2006; Bischoff et al., 2004; Petersohn et al., 1999; Price et al., 2001; Helmann et al., 2001; Hecker et al., 2007). Meanwhile, many other regulons of S. aureus were characterized by transcriptomics or proteomics (Dubrac et al., 2007; Dunman et al., 2001; Fournier et al., 2001; Liang et al., 2005; Rogasch et al., 2006; Said-Salim et al., 2003; Ziebandt et al., 2001, 2004; Yang et al., 2006; Luong et al., 2006). In most cases, regulons are part of a comprehensive gene regulation network because they usually do not exist independently from each other but are tightly connected within the network with many overlapping areas between the single regulons. Many genes are not only under the control of one, but frequently two or even more global regulators forming a network of interacting regulons. Color coding of proteins induced by one, two or even more stimuli is a useful toolbox to uncover such overlapping areas between the regulons (Hecker et al., 2008). This complex regulation of single genes by a set of different environmental stimuli and global regulators leads to a fine tuning of the amount of individual bacterial proteins required under the specific physiological situation. According to the definition, a modulon structure requires a master regulator assisted by secondary regulators leading to a network of interacting regulons controlled by a higher-order master regulator. The Rex modulon of S. aureus is a nice example of such a hierarchical regulation structure. The global repressor Rex blocks many genes active under anaerobic conditions, e.g. genes encoding enzymes for fermentation reactions, the arginine deiminase pathway and nitrate/nitrite respiration. A high NADH level leads to derepression of Rex which is no longer able to interact with the operator region. As first shown by a proteomics comparison between a wild-type and a rex mutant strain, derepression of the genes is in most cases necessary but not sufficient for gene activation. The usually weak promoters of the genes controlled by oxygen starvation in addition to Rex derepression require the binding of an activator, in most cases a phosphorylated response regulator (Pagels et al., unpublished) (Fig. 4). Summarizing this part, it was demonstrated that gel-based proteomics aids in visualization of many crucial cellular reactions including most of the metabolic pathways and abundant stress and starvation responses. This ‘‘panorama view-of proteomics’’, however, is not thought to merely produce long inventories of proteins, but rather crystallizes new ideas and hypotheses. Gelbased proteomics in most cases comprise the starting point of more detailed follow up-studies that address crucial aspects of bacterial physiology. The full power of proteomics unfolds in synergy with other ‘‘omics’’ technologies. Together with results gained from transcriptomics, metabolomics, and bioinformatics,

proteomics will pave the way to a quantitative description of cellular reactions within S. aureus thereby forging the field of S. aureus systems biology (Fig. 5). The fate of individual S. aureus proteins and developments towards the S. aureus phosphoproteome Protein expression profiling can provide the protein inventory of cells including the relative or even absolute amounts of individual proteins and their fates. Proteins can interact with each other forming protein aggregates and molecular machines, can be modified, e.g. phosphorylated, damaged and repaired and – ultimately – degraded. A combination of gel- and MS-based proteomics again is an excellent approach to follow the fate of individual proteins at a proteome-wide scale. Protein aggregates can be analyzed by tagging techniques followed by MS analyses of the interacting partners. It is highly probable that the main metabolic pathways are organized by large protein aggregates allowing an efficient channeling of the metabolic intermediates from one enzyme to the other (Commichau et al., 2009). Furthermore, protein phosphorylation can be followed by phosphoproteome studies (Lomas-Lopez et al., 2007; Eymann et al., 2007) (Fig. 6). The analysis of S. aureus mutants of still uncharacterized protein kinases and phosphatases will be an attractive approach to identify new target proteins and to study new signal transduction pathways that might play a role during infection and in antibiotic resistance (Soulat et al., 2007; Donat et al., 2009; Debarbouille´ et al., 2009; Truong-Bolduc et al., 2008). Protein damage and oxidation can also be investigated by proteomics, e.g. the irreversible oxidation of glyceraldehyde-3phosphate dehydrogenase by hydrogen peroxide treatment (Weber et al., 2004) (Fig. 7). Furthermore, there is information available that reactive cysteine residues in B. subtilis and S. aureus are cysteinylated to protect the proteins against oxidative damage ¨ et al., 2007, 2008) (Pother ¨ by irreversible oxidation (Hochgrafe et al., 2009). Finally, proteomics can be used to monitor the degradation of proteins synthesized in growing cells and no longer required in non-growing cells at a proteome-wide scale. Many vegetative proteins are degraded in glucose-starved cells according to a controlled protein degradation program. The proteomic data indicate that proteins which are no longer active and thereby no longer integrated and protected in functional complexes are degraded (Michalik et al., 2009) (Fig. 8). In B. subilits the Clp machine is in charge of the degradation of such unemployed proteins (Gerth et al., 2008). A proteomic view of S. aureus virulence factors – the S. aureus secretome The virulence of S. aureus is determined by the coordinated expression of a large set of different virulence factors including surface-associated and extracellular proteins. According to their function, staphylococcal virulence factors can be divided into at least four groups: (i) proteins involved in adhesion and invasion of host cells, (ii) proteins mediating degradation of host cells for both nutrition and spreading, (iii) proteins that enable the bacteria to evade the immune response, and (iv) proteins for utilization of nutrients that predominantly exist in the host. Most of the virulence factors have overlapping functions and can either act alone or in concert (Dinges et al., 2000; Foster and Hook, 1998; Foster, 2005; Lowy, 1998). Sequencing of several S. aureus genomes revealed a high heterogeneity within the species. Only 75% of the genome sequences seem to be conserved and represent the core genome. Housekeeping genes and also some virulenceassociated genes can be found in this part of the genome (Peacock

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Fig. 4. The regulatory cascade controlled by the S. aureus Rex repressor. Aerobic respiration via the respiratory chain influences the intracellular NAD/NADH ratio. A drop in that ratio leads to inactivation of Rex and consequently to derepression of ldh1 and ddh, resulting in maximal transcription of these genes. Lactate fermentation is a one-step pathway leading to NAD regeneration, probably as the immediate response. However, the Rex super-regulon also contains several sub-regulons comprising ethanol fermentation or nitrite/nitrate respiration. Rex controls the expression of these genes directly and/or indirectly via other regulators. This complex network allows the incorporation of several signals into the regulatory cascade, leading to a fine-adjusted adaptation. Colors of enzymes and transporters are adapted to the regulator(s) controlling their expression. MQ = menaquinone; HE = hemin cofactor; ox =oxidized form, red = reduced form; SrrA = DNA-binding response regulator; SrrB= sensor histidine kinase; ArcR= transcriptional regulator, Crp/Fnr family (SACOL2653); Rex= redox-sensing transcriptional regulator (SACOL2035); NreABC= control of nitrate reduction (SACOL2389 – SACOL2391); PflB = formate acetyltransferase; Adh1 =alcohol dehydrogenase, zinc-containing (SACOL0660); AdhE =alcohol dehydrogenase, iron-containing (SACOL0135); NirC= formate/nitrite transporter family protein (SACOL0301) NirB/NirD = nitrite reductase large/small subunit; NarG/NarH/NarI = respiratory nitrate reductase alpha/beta/gamma subunit; ArcA= arginine deiminase; Ldh1 =L-lactate dehydrogenase; Ddh= D-lactate dehydrogenase; LctP2 = L-lactate permease (SACOL2363); Ald1 = alanine dehydrogenase. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

et al., 2002; Holden and Lindsay, 2008). Certainly, most of the virulence-associated genes are located in the highly variable parts of the genome as pathogenicity islands and lysogenic phages (Lindsay, 2008). It has been accepted for a long time that the possession of a characteristic set of virulence genes gives rise to a specific virulence phenotype. However, several global studies on the virulence gene repertoire using various clinical S. aureus isolates belonging to different clonal lineages revealed that there is obviously no simple correlation between virulence gene repertoire and virulence potential of a given strain. Most probably any S. aureus genotype can induce life-threatening infections. However, certain strains are more virulent than others (Melles et al., 2004). Moreover, it has been shown that S. aureus virulence gene patterns characteristic for invasive diseases may also be important for nasal colonization (Lindsay et al., 2006). Extracellular proteins represent a reservoir of virulence factors. The extracellular proteome consists of all proteins that are actively secreted via different secretion pathways. S. aureus releases a large number of exoproteins such as toxins and enzymes into the extracellular surrounding. The theoretical extracellular proteome of S. aureus derived from the genome sequence (Sibbald et al., 2006) shows that most of the actively secreted proteins can be allocated to the pI region of 3.5 to 10.4 (Fig. 9). Accordingly, if secreted in detectable amounts, 90% of the predicted extracellular proteins should be detectable on gels with

a pI range of 3 to 10 and a molecular weight range of 10 to 140 kDa. Hence, gel-based proteomics represents a very attractive approach to identify secreted proteins and, particularly, to analyze expression of extracellular proteins of different strains and under different conditions, the regulation of these proteins and posttranslational modifications included. To define the exoproteome of a bacterium and to analyze the expression of extracellular virulence factors, proteins can be isolated from the culture supernatant and subsequently analyzed by a 2D gel approach combined with MS. For S. aureus COL, 24 of the predicted extracellular proteins have been identified thus far (Fig. 9) (Rogasch et al., 2006). Identification of extracellular proteins showed that about 60% of the proteins secreted via the Sec pathway appear as multiple spots on 2D gels. Such multiple spots are characterized by charge alterations (e.g. SEB, SEK, SEQ, Hla, Hlb, Lip, Geh) or fragmentations (Aly, Coa, LukF, LukM, Geh, SspA, SspB). Proteins with such deviations are possibly posttranslationally modified. To fully understand the pathogenicity of S. aureus, not only the expression profiling of virulence factors but also protein modifications (such as disulfide formation, lipid modification, phosphorylation) as well as protein stability and processing have to be analyzed. By using clinical S. aureus isolates, also novel virulence factors may be identified. By dissecting the exoproteomes of 25 clinical S. aureus isolates, we have demonstrated that, within a single species, the

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Fig. 5. Characterization of the physiological network of anaerobic adaptation of S. aureus by different ‘‘omics’’ techniques. The combination of different global approaches complements our view on physiology. Gel-based proteomics visualizes changes of enzymes belonging to most of the metabolic pathways. By means of this technique, accumulation, synthesis and modification of proteins can be adequately studied (green circle: central metabolism). Transcriptomics provides additional data on the transcription of the respective genes. Moreover, the transcriptomics approach allows expression studies for genes encoding low-abundance and/or membrane proteins that cannot be identified by gel-based proteomics (blue circles: anaerobic respiration components, transport and regulation). Metabolomics provides further information on the activity of different metabolic pathways and the turnover of extracellular and intracellular metabolites (orange circle: extracellular metabolites). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

exoproteome composition was extremely variable (Fig. 10) (Ziebandt et al., unpublished). Altogether, 206 distinct proteins were identified whereas solely 63 of these proteins were predicted to be secreted into the extracellular surrounding via the Sec pathway. Only seven out of 63 identified extracellular proteins were found in all strains. These are Aly, IsaA, Lip, LytM, Nuc, SA0620, and SA2097. Most interestingly, the amounts of these invariant proteins varied strikingly between the different isolates. A further nine proteins were found in at least 80% of the isolates. Both groups of proteins were defined as the core exoproteome of S. aureus. In contrast, 31 proteins seem to be unique for one or two strains. This was only partially explained by genome plasticity but mainly resulted from an unexpected high degree of expression heterogeneity. These data impressively show that only the combination of proteomics and genomics might give a complete picture of the virulence potential of a strain (Ziebandt et al., 2001; Rogasch et al., 2006; Burlak et al., 2007; Pocsfalvi et al., 2008) (Ziebandt et al., unpublished). Most interestingly, the N-terminal sequences of two proteins were determined that did not match any of the protein sequences in the NCBI and related databases indicating that our knowledge of extracellular proteins produced by S. aureus is not yet complete. Comprehensive investigations of virulence factor production will therefore add a new dimension to our insight into staphylocococcal pathogenesis. The function of at least 70% of the extracellular proteins is not yet clear and a possible role in virulence has to be elucidated. Variations in the expression level of virulence factors may relate to different activities of specific virulence factors. Most virulence factors are produced in a growth phase-dependent manner: while extracellular proteins accumulate at high optical densities, surface-associated proteins are mainly expressed at low optical densities. The best characterized regulators thus far are

RNAIII, the alternative sigma factor sB, Sar proteins such as SarA, Rot, and SarS, and the two-component systems ArlRS, SaeRS, and WalKR. Apparently, the regulatory network of virulence gene expression consists of many overlapping regulons which has been shown by both transcriptomic and proteomic approaches (Bischoff et al., 2004; Dubrac et al., 2007; Dunman et al., 2001; Fournier et al., 2001; Liang et al., 2005; Pane´-Farre´ et al., 2006; Rogasch et al., 2006; Said-Salim et al., 2003; Ziebandt et al., 2001, 2004). By this means, the bacterium is able to precisely coordinate expression of virulence factors to ensure an optimal mix of virulence factors at optimal concentrations during the course of infection. The network, however, is presumably not confined to the interactions of these regulators since many additional regulatory proteins appear to be encoded in the S. aureus genome, and detailed functional characterization of these regulators will be a very ambitious goal. As already mentioned, the majority of virulence factors are directly secreted into the medium or are attached to the cell surface of S. aureus. Thus, protein export mechanisms may contribute significantly to the virulence of this pathogen. Proteins that need to be transported to an extracytoplasmic location generally contain an N-terminal signal peptide that directs them to a particular transport way. Protein export mechanisms have been well characterized in the model organism for Gram-positive bacteria, B. subtilis (Tjalsma et al., 2000, 2004). Six different pathways can be deduced from the genome sequence of S. aureus: Sec pathway, Tat pathway, ABC transporters, ESAT-6 pathway, Com pathway, and holins (Sibbald et al., 2006). However, the functionality of these pathways is poorly analyzed. By means of prediction programs the type of sorting signals and the resulting export pathway can be predicted for a given protein. Sibbald et al. (2006) published an overview on all the exported virulence

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factors and the respective pathways mediating protein transport as deduced from the genome sequence of different S. aureus isolates. The proteome technique, however, is essential for the verification of the predicted transport mechanisms and the localization of the respective proteins. The most commonly used pathway for bacterial protein transport is the general secretory (Sec) pathway. Of the 206 proteins identified in the supernatant of the 25 different S. aureus strains at least 107 showed signal sequences typical for Sec-translocated proteins; 63 of the Sectranslocated proteins were predicted to be extracellular, 19 proteins are possible cell surface-associated proteins and the localization of a further 25 proteins is currently unknown. Moreover, 72 cytoplasmic proteins and 5 membrane proteins were reproducibly identified (Ziebandt et al., unpublished).

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Surface-exposed proteins contain additional signals targeting them to the respective location. Lipoproteins are retained to the membrane-cell wall interface via a diacylglyceryl moiety while proteins that contain an LPXTG motif are covalently linked to the cell wall by a sortase-dependent mechanism (Sibbald et al., 2006). By using mutants that lack either sortases (SrtA and SrtB) or the lipoprotein diacylglyceryl transferase (Lgt), surface-exposed proteins are no longer attached to the cell surface but released into ¨ the medium (Antelmann et al., 2001; Baumgartner et al., 2007). Besides the major Sec pathway, a secondary accessory SecA2SecY2 pathway is present in S. aureus. This SecA2-SecY2 pathway was shown to contribute to the export of cytoplasmic and cell wall proteins in other pathogenic Gram-positives (Bensing and Sullam, 2002; Braunstein et al., 2003; Lenz et al., 2003; Gibbons et al., 2007). As shown by proteomics, in S. aureus, the accessory system seems to be exclusively required for the export of the adhesin SraP (Siboo et al., 2008). Conclusion and outlook of gel-based proteomics In the previous sections, it was demonstrated that proteomics provides an excellent toolbox to obtain comprehensive information on cell physiology and on the virulence potential of S. aureus grown in the laboratory. The challenge for future studies is now to translate this information to infection-related conditions. The first step is to simulate those infection-related conditions in the laboratory, such as oxygen or iron starvation, oxidative or cell surface stress and to study the responses of S. aureus to these stimuli at proteomics level. Further proteomics experiments analyzing the in vivo situation are necessary in order to understand the infection biology of the pathogen. Initial steps of such analyses have been taken by identification of the protein profiles of S. aureus cells isolated from infected human cells. By integrating these data with results of in vivo transcriptional profiling (Garzoni et al., 2007; Voyich et al., 2005, 2008), the physiological situation of S. aureus living in eukaryotic cells can be predicted, including information on the preferred carbon and nitrogen sources or on the predominant growth-restricting stress/ starvation stimuli. Again, global ‘‘omics’’ techniques will drive hypothesis generation and allow insights into S. aureus survival mechanisms within the host. With this knowledge at hand, traditional cell biological techniques can be used to analyze the interaction of single S. aureus virulence factors with host processes (Burlak et al., 2007).

Fig. 6. Phosphoproteome of S. aureus COL. Dual-channel image of Flamingostained total proteins (green) and Pro-Q Diamond-stained phosphoproteins (red) of exponentially growing S. aureus cells. Protein spots with high Pro-Q Diamond/ Flamingo log ratios represent putatively phosphorylated proteins and appear as red spots in this picture. About 80 protein spots were identified as putatively phosphorylated proteins (unpublished data provided by K. Gronau). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

The impact of MS-based proteomics towards the elucidation of the entire proteome of S. aureus In contrast to DNA arrays that can cover the entire genome, there is no chance to catch the entire proteome in one single fraction. Subproteomic fractions have to be analyzed which also provides an advantage: the targeting of individual proteins can be

Fig. 7. Thiol oxidation of S. aureus COL glyceraldehyde-3-phosphate dehydrogenase (Gap) under oxidative stress conditions. Sectors of 2D gels covering the region where the Gap is located. Protein extracts of S. aureus COL before (control) and different times after addition to 100 mM H2O2 were separated and stained with silver nitrate. Note the shift of Gap to a more acid pH under oxidative stress conditions caused by oxidation of the thiol group of the cystein residue to sulfonic acid. Gap occurs at its original position again 30 min after treatment.

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Fig. 8. Protein degradation after long-term starvation in S. aureus. Pulse-chase experiments using [35S]methionine show the global degradation of cytoplasmic proteins after long-term starvation. Cells were cultivated in synthetic medium. Sample time points are indicated by arrows above the growth curve. The labeled protein pattern of exponentially growing cells shown in red was compared with the labeled protein pattern of non-growing cells (28 h later) shown in green. Proteins that appear in red were degraded over time. Yellow spots represent proteins that are relatively stable over time. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 9. The theoretical and experimental master gel of secreted proteins of S. aureus COL. (A) The theoretical reference gel of the exoproteome of S. aureus COL predicted by Sibbald et al. (2006). The theoretical pI and molecular weight (MW) of the native proteins (without signal sequences) derived from the genome sequences of S. aureus COL was obtained from the NCBI database (www.ncbi.nlm.nih.gov). (B) The theoretical reference gel of extracellular proteins of S. aureus COL that have been identified on 2D gels thus far. (C) Extracellular protein pattern of S. aureus COL. Proteins (100 mg) isolated from the supernatant of S. aureus COL (grown in TSB medium to an OD540 of 10) were separated on a 2D gel. The identified proteins are assigned to the open reading frame number as defined in the S. aureus COL genome sequencing project. Note that many proteins appear as multiple spots on 2D gels and differ in their position compared to the theoretical reference gel.

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to the 630 proteins identified by both procedures almost 500 proteins were identified by MS-based approaches only, in most cases low-abundance proteins. In total, in growing and nongrowing cells more than 1200 proteins were identified corresponding to a coverage of almost 67% of the predicted cytosolic proteins (Becher et al., 2009). For the visualization of the membrane proteome, we need MSbased approaches because membrane proteins escape detection by gel-based proteomics due to their hydrophobicity. We used different strategies (Wolff et al., 2008; Hahne et al., 2008) to cover the entire membrane proteome. From the almost 600 membrane proteins predicted from the genome sequence more than 300 were identified by a combination of different techniques. These intrinsic membrane proteins are characterized by one to 21 transmembrane domains (TMDs; Fig. 11). In addition to intrinsic membrane proteins, we also found many cytosolic proteins probably representing cytosolic contaminations but also proteins which are physiologically directly connected to the membrane (e.g. response regulators bound to membrane-bound histidine kinases). Thus, the entire membrane proteome covers intrinsic membrane proteins as well as membrane-associated proteins. A very crucial group of proteins is formed by the cell surfaceexposed proteins because these proteins are the first to interact with the host cell. Sortase-anchored, lipid-anchored, membrane proteins with extracellular loops or cell wall-associated proteins belong to this class. For the characterization of this subproteomic fraction two approaches were used. The affinity labeling approach employs the biotinylation of extracellular loops of cell surfaceexposed proteins (the biotin reagent is not able to enter the cell). The biotinylated protein fraction can be recovered by affinity purification on avidin columns, followed by 1D gel separation and LC-MS/MS analysis. With an optimized method especially for S. aureus, it is possible to analyze up to 80% of the predicted surface-associated proteins (Hempel et al., unpublished). A particularly high coverage was reached for the lipoproteins: from the predicted 66 lipoproteins of S. aureus COL 55 could be identified under the conditions used. As already shown in the previous chapters, the method of choice to characterize the extracellular proteins is gel-based proteomics. Even in this subproteomic fraction, however, additional proteins were identified by MS-based approaches. Fig. 10. Extracellular proteomes of different S. aureus isolates belonging to the same sequence type (ST8, agr1). Three S. aureus clinical isolates belonging to sequence type 8 were grown in TSB medium to the stationary phase. Proteins in culture supernatants were collected by trichloroacetic acid precipitation; 350 mg protein extract of each isolate was separated on 2D gels, using commercially available IPG strips (pH 3-10, GE-Healthcare, Sweden) for the first dimension. Protein spots were detected by staining with colloidal Coomassie Brillant Blue.

followed at a proteome-wide scale using this approach. In Grampositive bacteria, we have to study at least four main proteomics subfractions: cytosolic, membrane and membrane-associated, cell surface-exposed, and finally secreted proteins. The last two fractions are of great importance because most of the virulence factors are either exposed on the cell surface or even secreted into the extracellular space. As shown in the previous chapters, gel-based proteomics is a reasonable approach to cover many cytosolic and extracellular proteins. For cytosolic proteins, we identified almost 700 proteins in the pI range 4-7 corresponding to a coverage of about 25% of the 2600 protein-encoding genes and about 40% of the predicted 1800 cytosolic proteins. This calculation does not consider that only a portion of the genome is expressed under specific physiological conditions. Nevertheless, MS-based approaches are required to identify the still missing cytosolic proteins. In addition

Concluding remarks Becher et al. (2009) will provided one of the most comprehensive protein inventories of a living cell, including quantitative data for almost 1700 S. aureus proteins with many proteins up- or down-regulated in the non-growing state compared to growing cells. The study shows that many proteins were degraded in nongrowing cells obviously because they were no longer protected by integration into functional complexes. Because only a part of the genome is expressed under the conditions used, we suggest that more than 80% of the proteins synthesized in a living cell have been quantified. Furthermore, a sorting list for all proteins will be provided. The progress in bacterial proteomics will have a great impact on future studies on cell physiology of S. aureus and related Grampositive bacteria. Almost all proteins that make a cell viable were identified and quantified. Protein expression profiling, however, is only the first step to understand cell function. A combination of gel- and MS-based proteomics can be used to follow the fate of each individual protein in the cell. The protein inventory of S. aureus is ready for many follow-up studies in cooperation with groups interested in S. aureus infection biology. Insights obtained

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Fig. 11. Allocation of identified proteins in respect to their number of transmembrane domains (TMDs) depending on the applied analysis method. Only about one-third of the proteins identified via 1D gel-LC-MS/MS represent intrinsic membrane proteins (IMPs), and the proportion of proteins containing only one or two TMDs is relatively high. If samples are separated by 2D LC the fraction of non-IMPs becomes smaller (about 60%), but the most efficient enrichment of proteins integral to the membrane, especially of the ones with multiple TMDs, is achieved by the shaving procedure (proteinase K-chymotrypsin digestion of membrane proteins; only 3.3% non-IMPs) (reproduced from Wolff et al. (2008)).

by this kind of cooperation will dramatically improve our knowledge on the fate of the bug in the human host. It thus paves the way towards a new quality in understanding cell physiology and pathophysiology of S. aureus and related pathogenic bacteria.

Acknowledgements We are very grateful to all coworkers and students for their excellent data on proteomics of Bacillus and Staphylococcus and to Martin Fraunholz for critical reading the manuscript. Further¨ ¨ more, we thank Jorg Hacker, Jurgen Wehland, Karl Heinz ¨ Altendorf, Jan Maarten van Dijl, Fritz Gotz, Knut Ohlsen, Christof ¨ von Eiff, Richard Proctor, and Uwe Volker, for longstanding and fruitful cooperation and Decodon GmbH (Greifswald) for providing Delta2D software. This work was supported by grants of the the DFG (SFB/TRR34/1-2006, GK212/3-00, FOR 585), BMBF (031U107A/-207A; 031U213B), the EU (StaphDynamics, LSHMCT-2006-019064; BaSysBio, LSHG-CT-2006-037469 and the Fonds der Chemischen Industrie.

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