Gel-based proteomics of Gram-positive bacteria: A powerful tool to address physiological questions

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DOI 10.1002/pmic.200800278

Proteomics 2008, 8, 4958–4975


Gel-based proteomics of Gram-positive bacteria: A powerful tool to address physiological questions Michael Hecker, Haike Antelmann, Knut Büttner and Jörg Bernhardt Institute of Microbiology, Ernst-Moritz-Arndt-University of Greifswald, Greifswald, Germany

In this review, we demonstrate the power of gel-based proteomics to address physiological questions of bacteria. Although gel-based proteomics covers a subpopulation of proteins only, fundamental issues of a bacterial cell such as almost all metabolic pathways or the main signatures of stress and starvation responses can be analyzed. The analysis of the synthesis pattern of single proteins, e.g., in response to environmental changes, requires gel-based proteomics because only this technique can compare protein synthesis and amount in the same 2-D gel. Moreover, highly sophisticated software packages facilitate the analysis of the regulation of the main metabolic enzymes or the stress/starvation responses, PTMs, protein damage/repair, and degradation and finally protein secretion mechanisms at a proteome-wide scale. The challenge of proteomics whose panorama view shows events never seen before is to select the most interesting issues for detailed follow up studies. This “road map of proteomics” from proteome data via new hypothesis and finally novel molecular mechanisms should lead to exciting information on bacterial physiology. However, many proteins escape detection by gel-based procedures, such as membrane or low abundance proteins. The smart combination of gel-free and gel-based approaches is the “state of the art” for physiological proteomics of bacteria.

Received: March 31, 2008 Revised: May 19, 2008 Accepted: May 22, 2008

Keywords: 2-D gels / Bacillus subtilis / Physiology / Starvation / Stress



The release of the first genome sequence of a living organism, the bacterium Haemophilus influenzae, opened a new era in life sciences, the era of functional genomics [1]. The year 1995, however, should also be kept in our mind because the term proteomics was coined [2] describing the complete set of proteins expressed under a defined physiological condition. Whereas the genome sequence only provides the “blue-print” of life, the proteomics is required to bring the genes to “real life of the proteins”. Genome and proteome are tightly inter-

Correspondence: Professor Michael Hecker, Institute of Microbiology, Ernst-Moritz-Arndt-University of Greifswald, F.-L.-JahnStr. 15, D-17487 Greifswald, Germany E-mail: [email protected] Fax: 149-3834-864202 Abbreviations: LMW, low molecular weight; MHQ, 2-methylhydroquinone; TCA, tricarboxylic acid

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connected: the genome sequence is essential to identify the proteins via MS techniques. The development of mild ionization methods allowing large-scale MS was a cornerstone technology to propel the proteomics world forward [3]. Proteomics started in 1975 as O’Farrell and Klose [4, 5] published the now famous and most frequently cited 2-DE technique (2-D PAGE) that allows the separation of thousands of proteins in an area of 20620 cm2 in size. Each single protein is separated according to its molecular weight and pI. Already in 1975 O’Farrell showed in an impressive way that hundreds of Escherichia coli proteins could be visualised as distinct spots at a 2-D gel in a resolution never seen before. Only a few years later Fred Neidhardt and Ruth Van Bogelen, the pioneers of physiological proteomics used this powerful technique to address crucial issues of E. coli cell physiology such as heat shock or starvation responses [6]. Because many of the main players of life, the proteins could be visualized, later identified, and even quantified a big step toward a comprehensive understanding of life processes was accomplished.


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To address physiological questions, a standardized proteome of a specific cell type was necessary to compare different physiological conditions on a series of 2-D gels. In the 1980s and 1990s amongst others Angelika Görg successfully improved the gel-based proteomics technique by the introduction of IPGs for IEF [7]. The introduction of easy to handle tailor-made pH-gradient gels and the higher reproducibility of the 2-D protein pattern were the essential outcomes of these improvements. This was the basis for highthroughput comparative gel-based proteomics studies [8, 9] and for the development of gel-based proteomics as a new and rapidly developing field in cell physiology. Because of their low complexity bacteria are extremely suitable model organisms for transferring the “blue-print of life to real life”, and thereby coming to a new quality in understanding life processes. Bacterial genomes code for about 600– 6000 genes (in some species even more) but only a part of the genome, usually 50–80% are expressed under specific life circumstances depending on the environmental stimuli that reach the bacterial cell. Usually, a set of only 1000–3000 different proteins makes a cell viable. The minimal set of proteins required for running life processes can be defined especially in extremely low complex bacteria, e.g., members of the genus Mycoplasma containing only 600–800 protein-encoding genes [10]. It turned out that a set of about 300 proteins appears to form the proteome complement of the minimal genome of a living cell! These low complexity organisms are reasonable model systems to address crucial and elementary issues of life processes by using proteomics approaches [11–13]. From a physiological point of view there are two major ways of life and consequently two major proteomes of bacteria can be observed: vegetative proteomes of growing cells with mainly house-keeping functions required for growth and multiplication, and proteomes required for adaptation to stress and starvation in a slow-growing or even nongrowing state. In order to understand such divergent ways of microbial life, the first step is to look for all proteins expressed under those specific physiological conditions, to identify these proteins, to investigate their function as the prerequisite for the understanding of life processes. However, protein expression profiling is only the starting point for physiological proteomics. Life is more “than a mixture of cellular proteins”. Proteins act as aggregates in cellular machineries, they are targeted to their final destinations inside or outside the cell, they can be reversibly or even irreversibly modified, damaged, repaired, and in hopeless cases even degraded. A combination of gel-based and gel-free proteomics enables one to follow the fate and destination of single proteins and their network of interactions from synthesis via targeting and modification to degradation. Bacillus subtilis, the best analyzed representative of the Gram-positive bacteria has been established as a model system for functional genomics of bacteria. The extensive knowledge on genetics, molecular biology and physiology of this model organism was the main reason to choose this bacterium as our primary model for physiological proteom© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim


ics [14, 15]. In the next step we transferred our proteome expertise from B. subtilis to pathogenic Gram-positive bacteria. Because of its dominant role in hospitals as the main causative agent for nosocomial infections and life-threatening diseases, such as endocarditis, osteomyelitis, and septic shock we have chosen Staphylococcus aureus as a second model organism. The main goal of these studies is to come to a new and comprehensive understanding of its cell physiology and infection biology by relying on the impressive power of physiological proteomics in the post-genome era [16, 17]. Sequencing of the B. subtilis genome revealed about 4100 genes including 1700 genes with still unknown functions [18, 19]. The genome sequences of more than ten different S. aureus strains are now available comprising of 2600–2700 genes [20–24]. After the initial enthusiasm for gel-based proteomics in the 1990s, now we have to confess that only a part of the proteome can be accessed by this approach. Many proteins cannot be detected using the 2-D gel-based approach, including, e.g., hydrophobic integral membrane proteins. Thus, gel-free proteomic techniques have undergone significant developments during the last 10 years, and revealed fascinating dynamics [25–31]. The combination of both, gelbased and gel-free approaches is required to follow the upand downregulation of single proteins during life processes of organisms. In this review we intend to show that despite covering only a subproteomic fraction gel-based proteomics is still a powerful approach to address fundamental physiological issues of low complexity model systems because: (i) the “life style” of both already known and even unknown bacteria with an emphasis on metabolic pathways as a main feature of life can be visualized and accessed; (ii) the protein synthesis pattern can be followed directly as the basis for figuring out rapid stress and starvation responses; (iii) the modification of the molecular mass and charge of proteins can be seen using 2-D gels; (iv) the damage by environmental stimuli and repair by cellular protection systems can be studied; and (v) the degradation of damaged or unprotected proteins can be followed at a proteome-wide scale. This review focuses mainly on our model system, B. subtilis, but in some cases data on related Gram-positive bacteria are included. The main goal of this review is to show that gel-based proteomics highlights crucial elements of bacterial life in a convenient and also “eye-catching” way as a powerful tool to analyze issues of bacterial physiology.


Gel-based proteomics visualizes metabolic pathways and stress and starvation responses – two fundamental features of bacterial life

Most of the main cellular functions of growing cells can be monitored by using gel-based proteomics. According to DNA array data, growing cells of B. subtilis express more than 2500


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genes [32]. These include 1500 proteins which should be visualized in the standard proteomic window pH 4–7 (Fig. 1). With the identification of about 750 cytosolic proteins more than 40% of the predicted proteins in this main window are currently accessible by gel-based proteomics. Zoom gels with narrow pH gradients to increase the resolution [32] or sensitive staining techniques using Flamingo or Deep purple stain, were employed to visualize low abundance proteins. About 50 alkaline proteins can be added to this cytosolic proteome, mainly including abundant ribosomal proteins [33] (Fig. 1). Intrinsic membrane proteins are totally absent from these gels. Even if only a part of the proteome can be covered and many cellular proteins escape detection by this gel-based approach, the gel-based proteome pattern of growing cells can be used to address crucial physiological issues.

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Because most proteins of the cytosolic metabolic pathways and many other cellular components can be detected, the gelbased approach is a valuable tool to predict and visualize the life style of already known [32, 34–36] and even unknown bacteria [37]. Using gel image analysis software, active metabolic pathways can be reconstructed and their regulation can be analyzed. This is a convenient tool to come to a rapid overview on cell metabolism (“metabolism at a glance”). First data showing a strong regulation of glycolytic and tricarboxylic acid (TCA) pathways by glucose excess in B. subtilis (Fig. 2) – now a well established regulatory model [34] – originated from gel-based proteomic studies. These results revealed a clear upregulation of only a few glycolytic enzymes and a strong repression of the entire TCA cycle

Figure 1. (A) Theoretical proteome of B. subtilis showing the distribution of all 4100 predicted proteins according to their isolelectric points and molecular weights. (B) B. subtilis master 2-D gel for cytoplasmic proteins which are separated in the standard pH range 4–7 (right image) and in the alkaline pH range 7–12 (left image). In the master 2-D gel 519 proteins are labeled that were identified in the pH range 4–7. In addition, 174 proteins were identified in the narrow range pH gradients (pH 4–5, 4.5–5.5, 5–6, and 5.5– 6.7) and 52 proteins in the alkaline pH range 7–12. Cytoplasmic proteins were harvested from B. subtilis wild type cells grown in Belitsky minimal medium at an OD500 of 0.4 and separated by 2-D PAGE as described in Eymann et al. 2004 [32].

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Figure 2. Regulation of glycolysis and TCA cycle enzymes in B. subtilis grown with an excess of glucose. Glucose activates glycolysis (left) and represses the TCA cycle (right). Excess glycolytic intermediates that cannot enter the TCA cycle have to be secreted into the extracellular medium as a result of an overflow mechanism. ATP production is mainly accomplished via substrate level phosphorylation (adapted from Tobisch et al. 1999 [34]).

under glucose excess. In a similar way regulation of enzymes involved in amino acid biosynthesis was analyzed by a combined proteomic and transcriptomic approach [38]. Current results suggest that the gel-based approach can also be used to provide quantitative data, such as molecules per cell of a certain protein. This is one of the prerequisites required for modeling of metabolic pathways. For this purpose each step of the whole proteomics workflow has to be kept under control of the proteomic researcher. This includes detailed information about the experimental setup, such as (i) cultivation conditions, (ii) reliable determination of cell number and size, (iii) quantitative cell disruption, (iv) reliable protein measurement in solution, (v) knowledge about discrimination effects during sample entry into the gel, and (vi) the application of a protein staining/detection protocol with sufficient sensitivity, known signal response and minimal protein to protein variation. By using defined amounts of calibrant proteins or internal calibrants whose quantity was determined by Western blotting or quantitation via AQUA and related technologies [39, 40], it should be possible to transfer the lists of relative protein amounts [41] into lists © 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

providing estimates for the numbers of single protein molecules per cell. Gel-based proteomics can also be used to predict the cell physiology of nonculturable bacteria. The proteomic profile of the so far nonculturabe endosymbiont of the deep-sea worm Riftia pachyptila revealed the basic physiology of this bacterium characterized by a hidden life in 3000 m depth. This life style revealed an unusual carbon dioxide fixation and a dominant sulfur oxidation mechanism comprising about 10% of the cellular protein [37]. One of the most convincing advantages of gel-based proteomics is its amenability to the study of protein synthesis and turnover. Protein synthesis can be directly followed over time after pulse-labeling with radioactivelylabeled amino acids (Fig. 3). The dramatic changes in the gene expression profile in response to environmental stimuli is reasonably analyzed using DNA arrays because upor downregulation of genes can be followed on the level of highly unstable mRNA molecules in a more comprehensive way than by proteomics. While changes in gene expression within only very few minutes can be revealed by DNA array


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Figure 3. Comparison of protein synthesis (red) and protein amount (green) for selected proteins that respond to heat shock (ClpC; A) and glucose starvation (AcsA, Dps, Eno; B). B. subtilis wild type cells grown in minimal medium to an OD500 of 0.4 were pulse-labeled for 5 min with L-[35S]-methionine at control conditions and after exposure to heat shock at 487C (A, red-marked image tiles) or at different time points after transition to stationary phase provoked by glucose starvation (B, red-marked image tiles). The corresponding protein amounts obtained from stained (A, silver stained; B, krypton stained) 2-D gels (green-marked tiles) are shown below the autoradiograms (redmarked tiles). Note that in most cases protein synthesis is clearly changed (ClpC, AcsA, and Dps are induced; Eno is decreased) whereas the protein amounts do not change significantly.

analysis much more time is required to visualize the changes on the proteome level after imposition of stress or starvation stimuli because protein accumulation needs some time to be detected by staining procedures. Using radioactively labeled amino acids; however, these changes can be visualized in a 3 min range because only the radioactively labeled and newly synthesized proteins can be detected by autoradiography. In all cases where the rapid changes in the protein expression profile have to be measured gel-based approaches are still the “state of the art”. The analysis of protein expression networks in response to environmental stimuli asks for highly sophisticated gel evaluation procedures. Because stress and starvation situations are the rule in almost all natural ecosystems, the study of these issues is fundamental for understanding bacterial physiology. The first step in analyzing stress/starvation adaptation is to look for all proteins induced or repressed by the stimulus because all newly induced proteins will together accomplish the stress/starvation adaptation. The dual channel imaging technique is particularly suited to search for proteins induced or repressed by the stimuli [42]. This technique allows a rapid assignment of proteins to such environmental or regulation groups (stimulons, regulons) simply by a comparative analysis of protein amount (protein staining) versus protein synthesis (phosphoimaging) in one 2-D gel. Two digitized images of one 2-D gel have to be superimposed in alternate additive dual-color channels (Fig. 4). The first one (densitogram) displays protein amounts visualized by protein staining in intensity depend© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

ent green shades. The second image (autoradiogram) shows the proteins synthesized during pulse-labeling with 35S-Lmethionine false-colored red. When the two images are combined, proteins accumulated and synthesized in growing cells (steady state) appear yellow. However, proteins not previously present in the cell but newly synthesized after the imposition of a stress or starvation stimulus are colored red, because these proteins have already been radioactively labeled, but have not yet accumulated to levels that are detectable by the staining techniques employed. Looking for signals much stronger in the autoradiogram (the red-colored proteins) is a simple approach for visualizing all proteins induced by a single stimulus, thereby defining the entire set of proteins induced by one stimulus (stimulon). Proteins repressed by the stimulus can also be visualized by this technique. Green-colored proteins are no longer synthesized (no longer red) but still present in the cell and are candidates repressed by the stimulus. To quantify level and synthesis rate of proteins a software package such as Delta2D can be used. This type of false color imaging is not only convenient to define all proteins induced or repressed in response to environmental or other stimuli. In a next step all newly induced proteins can be identified in order to gain a global view of the stress/starvation adaptation process. This approach can also be used for a first, but still preliminary prediction of the role of still unknown proteins which should accomplish a function in adaptation to those stress stimuli that induced the protein. This is an essential issue because in most cases

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Figure 4. Dual channel image for simultaneous display of protein synthesis (red image) versus protein amount (green image) of B. subtilis in response to heat shock (487C). Cytoplasmic proteins were labeled with L-[35S]methionine after exposure to heat shock and separated by 2-D PAGE as described previously [42]. Proteins were stained using silver nitrate for detecting protein amounts and protein synthesis was determined from autoradiograms. The resulting 2-D gel images were analyzed using the DECODON Delta2D software. The proteins of the heat shock stimulon appear in red since these display strongly induced protein synthesis.

more than 30% of the genome encode for proteins with unknown function. An alternative of the prediction of their function is using an interactomic approach. The interaction of the unknowns with a protein of already known function may indicate the biological role of the unknown protein (“guilty by association”). Finally we were able to establish a comprehensive stress/starvation proteomics signature library which helps to predict the physiological state of cells grown in a bioreactor [43, 44], in a biofilm or even under infection-related conditions [45]. This stress/starvation proteomics signature library was further complemented by an antibiotic signature catalog as a tool to predict the mode of action of both already known and still unknown antibacterial drugs [46–48]. Our proteomics signature library also revealed new stress adaptation mechanisms. The application of toxic compounds such as diamide, 2-methylhydroquinone (MHQ) or catechols to B. subtilis cells uncovered a new resistance mechanism against thiol-specific quinone-like and azocompound-like electrophilic compounds. This resistance mechanism includes at least two new regulons controlled by the previously unknown MarR-type regulators, YodB and YkvE [49–51]. Moreover, YodB which is highly conserved among Gram-positive bacteria is itself regulated by a novel PTM via thiol-(S)-alkylation which was in turn discovered using gel-free proteomic approaches. The dual channel imaging procedure has to be extended to a more generalized approach, if 2-D gel images of independently separated protein extracts have to be analyzed. In a © 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

first attempt variations of the spot positions in two or more independently created 2-D gels have to be compensated. This is done by using image analysis software that is based on image warping (e.g., Delta2D) [42, 52, 53]. Image warping corrects for the positional fluctuations in spot patterns resulting from the 2-D gel approach. In a second step by superimposing two warped 2-D gel images e.g. synthesis rates or the protein levels can be directly compared in one image. Also in this case dual channel imaging makes the differences in the proteome profile visible in a very convenient way. For the extraction of data of complete expression profiles from multiple sample setups the positionally corrected 2-D gel images are combined in a fusion gel image [52] that preserves the positional information of any spot ever appeared in the experiment. This fusion gel serves as a proteome map, in which the detection of an experimentwide spot consensus pattern (spot mask) is performed. Within the consensus spot boundaries, gray level integration on each single gel [53, 54] gives a value for the protein expression level (densitogram) or protein synthesis (autoradiogram), respectively. The use of the DIGE technique is an approach that avoids variations of spot positions among up to three different gels. The DIGE technique allows the separation of two or three differentially labeled samples in one single 2-D gel resulting in two or three congruent gel images without any fluctuations in spot positions. Because, labeling here occurs


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by using the covalent protein binding dyes Cy2, Cy3, and Cy5, this approach exclusively detects the amount of proteins. By using one of the three dyes for labeling of a standardized protein mixture (internal standard) and relative quantitation according to this standard (e.g. by using the Decyder software), quantitative data of outstanding reliability can be achieved [55–57]. The aspects that have been covered so far have just given a snapshot of a single moment in the life of a bacterial cell. However, this life is not static but highly dynamic, with sequential time-dependent gene expression programs as an essential feature. If one assembles such proteomic pictures through time, growth, and developmental processes can be followed at the molecular level as in a “life movie”. Examples are growing B. subtilis cells entering the stationary phase because of glucose starvation (Fig. 5) and S. aureus cells shifted from aerobic to anaerobic conditions [59, 60]. Using the dual channel imaging technique described above, synthesized (red) and accumulated (green) proteins were followed along the growth curve. These “movies of life” can provide valuable information on the fate of each single protein, its synthesis, time course associated expression, accumulation, and even degradation [14, 59, 60]. A complete reprogramming of the protein pattern was visualized for the first time as cells are transferred from a growing state to a nongrowing state induced by glucose starvation. If the proteome data that have been collected are interpreted in the context of basic physiological knowledge, one can gain a comprehensive overview of the regulatory events that happen during the transition phase and understand cell physiology as a whole. In order to get a more complete view of the reprogramming of gene expression, these proteomics studies were complemented by transcriptome analyses [61]. For visualizing more complex patterns of protein expression in the adaptational network, color coding of protein spots within proteome maps according to their expression profile is a useful tool which relies on the combination of data from more than two gel images (Fig. 6). Positionally corrected 2-D gel images of a whole, e.g., time course experiment are merged and serve as a proteome map [52] containing all relevant protein spots which ever appeared in the experiment of interest. In a second step proteins belonging to different stimulons and regulons are color coded according to their expression profile. Because many stress/starvation inducible genes are controlled by more than one signal transduction cascade there are many proteins which are induced by more than one stimulus. The sB-dependent general stress proteins, for instance, are induced by a different set of stress and starvation stimuli which can be visualized that way. Figure 6 shows the color coding approach to characterize global stress and starvation responses of B. subtilis. In total, 201 stress or starvation induced marker proteins were identified in these fused proteomic maps for stress or starvation. These include 79 marker proteins for heat, salt, oxidative stress, and 155 marker proteins for glucose, phos© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

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phate, ammonium or tryptophan starvation [51, 62]. These marker proteins can be dissected into specific regulons and are useful to define the mode of actions of novel antimicrobial compounds in B. subtilis [48]. For example, the heat-shock signature is indicated by the induction of the heat-specific HrcA-dependent chaperones and the general induction of the sB, CtsR, and Spx regulons. Hydrogen peroxide and paraquat caused inductions of the oxidative stress specific PerR and Fur regulons and the thiol-stress specific Spx regulon as indicator for oxidations of protein thiolates. The induction of the TnrA- and sL/BkdR-dependent catabolic enzymes for alternative nitrogen sources indicates an ammonium starvation specific proteome signature and the TRAP-regulated tryptophan biosynthesis enzymes are the tryptophan starvation specific proteomic signature. The glucose starvation proteomic signature can be monitored by the induction of the CcpA, CcpN, and AcoR-dependent carbon catabolite controlled proteins. Finally, phosphate starvation is indicated by induction of the PhoPR-dependent proteins. General marker proteins for more than one starvation conditions could be assigned to the CodY, RelA, sB, and sH transition phase regulons. Those are required for the adaptation of the cell to stationary phase processes such as survival under growth-restricting conditions, the development of genetic competence or sporulation. The use of imaging techniques and their application in physiological proteomics and the analysis of the resulting quantitative data has been summarized recently in Berth et al. [53]. To understand the changes in protein expression during stress and starvation, the underlying adaptational network has to be dissected into regulatory groups – the stimulons and regulons. For this purpose, the comparison of the expression profiles of wild type with those of mutants lacking specific regulatory proteins under inducing conditions is the state of the art approach. The corresponding proteomic and transcriptomic data can be assembled into different regulatory modules, e.g., the stress/starvation stimulons and regulons. These individual stress/starvation regulons do not exist independently of each other, but are tightly interconnected and overlapping, forming protein groups with similar expression pattern. The color coding approach clearly visualizes groups of elements underlying defined regulatory mechanisms and can give a first impression about the structure of the adaptational network [14]. In summary, this chapter should provide evidence that gel-based proteomics is still a useful technique to explore the basic features of bacterial physiology shown for metabolic pathways and for stress and starvation responses as examples. A comprehensive information on the mechanisms of stress and starvation responses, on the structure, function, and even regulation of single regulons, their interaction included can be provided by a combined application of transcriptomics with highly sophisticated proteomic techniques.

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Figure 5. (A) Fused proteome map of B. subtilis from different samples of a glucose starvation experiment. Protein synthesis patterns (autoradiograms) of B. subtilis at control conditions (points 1, 2 of the growth curve) and at different times after the transition (point 3) to stationary phase (points 4–9) provoked by glucose starvation were combined to generate a fused glucose starvation proteome map [52]. The proteins were color coded according to their time point of maximal expression and classified into functional categories. (B) Comparison of protein synthesis (red image) and protein amount (green image) of AcoB before (4 h) and at different time points after starvation for glucose. The dual channel image tiles were interpolated between the sampling points along the growth curve to give a movie like equidistant representation of the growth states (see also http://microbio1.


Gel-based proteomics follows the fate of single proteins – protein targeting, protein modification, protein damage and repair, and finally protein degradation

Protein expression profiling is the first step only when proteomics is used to explore the mysteries of life processes at a genome-wide scale. Cellular life functions as a cooperation of many different proteins and each of these plays a distinctive role in this protein interaction network. Proteomics is the only tool that can follow the fate of each single protein in this protein network at a proteome-wide scale. In this chapter it will be shown that at least most of the steps on the way of the proteins from birth to death can be followed by a combination of gel-based with gel-free procedures. The study of protein targeting and protein secretion mechanisms in bacteria at a global scale needs proteomics because this issue cannot be addressed by mRNA profiling. By bioinformatics tools that look for protein secretion signals on the sequence level the fate and destination of the single © 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

proteins can be predicted. For experimental proof of these predictions; however, proteomics has to provide evidence whether the predictions were correct or wrong. The first step is to analyze the cell culture supernatant to look for all proteins that are secreted to provide a “proteomic view of genome-based signal peptide predictions” [63, 64]. These analyses showed that only 50% of the extracellular proteins are predicted to be secreted since these have N-terminal signal peptides and lack retention signals. The remaining extracellular proteins are either lacking signal peptides (e.g., cytoplasmic, flagella- or phage-related proteins) or these possess cell wall or membrane retention signals in addition to the signal peptides. There are numerous controversal discussions about specific secretion pathways via which cytosolic proteins might be exported. However, our comprehensive large scale analyses of the secretomes of B. subtilis secretion mutants have revealed that in most instances cytoplasmic proteins leave the cell due to partial cell lysis [63–65]. The study of the secreted proteins and their separation and identification by 2-D PAGE is still the method of choice for characterizing the secretome. The use of the color-coding


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Figure 6. Fused proteome maps of B. subtilis exposed to heat, salt, hydrogen peroxide, and paraquat stress (A) or starvation for ammonium, tryptophan, glucose, and phosphate (B). The protein synthesis patterns (autoradiograms) of B. subtilis exposed to different stress (A) or starvation (B) conditions were combined to generate a fused stress or starvation proteome map, respectively [62]. Induced marker proteins were color coded according to their expression profile. Accordingly, proteins can be classified as specifically or generally induced stress or starvation proteins using the respective color codes.

approach has been also proven to define a comprehensive extracellular starvation proteome map for B. licheniformis (Fig. 7) that dissects all enzymes secreted under ammonium, glucose, and phosphate starvation conditions into their corresponding stimulons [66]. Furthermore, the comparative extracellular proteome analysis of B. subtilis mutants with defects in different components of the protein export machinery provides a proteome-wide view of protein secretion mechanisms. One of the major results of our long-standing collaboration with colleagues from Groningen and Helsinki was the identification and proof of the first Tat-substrate, the strongly secreted alkaline phosphodiesterase PhoD [67] (Fig. 7B). In addition, new insights were provided about the major role of SecA and targeting factors (Ffh/FtsY) of the Sec-dependent protein secretion machinery, the redundant and overlapping substrate specificities of the five signal peptidases in protein secretion, the essential role of the diacyl glycerol transferase Lgt for lipid modification and subsequent membrane anchoring of lipoproteins, the minor role of the two minimal twin-arginine © 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

translocation pathways (TatAdCd and TatAyCy) in protein secretion or the major role of PrsA as an extracellular folding catalyst [63–71]. The study of the secretome of pathogenic bacteria carries great potential to describe the virulence factors of S. aureus because most of them are either cell-surface bound or even secreted. This approach depicted the majority of already known virulence factors of S. aureus but also of still unknown proteins whose role in virulence should be analyzed by follow up-studies. Parts of the cell surface-associated proteome such as lipid-anchored or sortase-anchored proteins can be detected by gel-based procedures if mutants missing the anchoring enzyme diacyl gycerol transferase (Lgt) or sortases have been analyzed (for detail see reviews [64, 72, 73]). PTM of proteins can trigger their activation, inactivation, or even degradation. Among them protein phosphorylation plays a pivotal role because the negative charge of a phosphate group can induce a distinct reorganization of the structure by attracting positively charged amino acids. The

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Figure 7. (A) Fused extracellular proteome map of B. licheniformis in response to starvation for ammonium, glucose, and phosphate. The coomassie-stained extracellular proteome of B. licheniformis grown under control conditions and in response to ammonium, glucose, and phosphate starvation were combined to generate a fused extracellular starvation proteome map of B. licheniformis [66]. Starvationinduced extracellular proteins were color coded according to their expression profiles as indicated in the color code schema. (B) The extracellular proteome of B. subtilis wild type (red image) compared to the total-tat mutant strain (green image) under phosphate starvation conditions. B. subtilis strains were grown under phosphate starvation conditions and extracellular proteins were harvested from medium fractions using trichloroacetic acid precipitation and subjected to 2-D PAGE as described previously [67–69]. PhoD was identified as specific substrate of the TatAdCd pathway. All other blue-labeled secreted proteins possess also RR/KR-motifs in their N-terminal signal peptides but these are no substrates for the Tat-pathway.

advantage of gel-based techniques is that protein modifications that lead to changes in charge or size can directly be visualized. In E. coli, Corynebacterium glutamicum, and B. subtilis the phosphoproteome was analyzed either by ProQDiamond staining, by 32P/33P-radiolabeling or by the use of phosphoprotein specific antibodies. Up to 100 labeled probably Ser/Thr/Tyr phosphorylated proteins were seen using these techniques in the 2-D gels because only esterlinked O-phosphates are chemically stable under the conditions of 2-DE [74–77]. Histidine or aspartate phosphorylations which are typical for the phosphotransferase system or two-component systems cannot be assessed using the gel-based approach. By a triple channel imaging approach of Flamingo-stained (green), ProQ-Diamond stained (red), and 33P-labeled (blue) proteins a reliable picture of the phosphoproteome of B. subtilis was provided (Fig. 8A). Recently, a very promising gel-free approach was used to enrich phosphorylated bacterial peptides on a global scale including low abundant proteins which were not detected by gel-based procedures [78]. The advantage of © 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

a gel-based view of the phosphoproteome; however, is the direct visualization of quantitative changes in the phosphoproteome pattern at a time lapse scale in response to various environmental stimuli which can profit from the 2-D protein map. As an example, Fig. 8B shows the dephosphorylation over time of HPr-Ser-46 phosphate which is an indicator for glucose starvation. The genome sequencing discovered a few Tyr/Ser/Thr protein kinases/ phosphatasaes whose function is totally unknown. The challenge of such studies is to leave the descriptive level which follows the phosphoproteome pattern in response to stress/starvation stimuli or along the growth curve (see Fig. 8B) but to understand the role of still unknown protein kinases and phosphatases in bacterial physiology. The elucidation of the partner switching mechanisms in sigma factor control of B. subtilis [79–84] or the role of McsA/ McsB in heat stress induction of the CtsR-regulon [85, 86] are prime examples for the approach which should be followed in future studies aiming in the exploration of new signal transduction pathways.


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Proteomics 2008, 8, 4958–4975 Figure 8. (A) Phosphoproteome map of growing B. subtilis cells showing a triple combination of the phosphorimage of 33[P]labeled proteins (blue), image of ProQ-Diamond-stained proteins (red), and the Flamingo-stained fluorescence image (green) during exponential growth. B. subtilis wild type cells were grown in minimal medium and cytoplasmic proteins were labeled with [33 PO34 ] during exponential growth as described previously [74]. Proteins were separated using 2-D PAGE, stained for the detection of protein amounts using the Flamingo stain and for phosphorylation using the phosphate residue sensitive ProQ-Diamond stain. [33P]-labeled proteins were detected using phosphorimaging. All phosphorylated proteins which were detected by using either the ProQ-Diamondstain (red), or the 33[P]-labeling (blue), or by both methods (ProQ/33[P], pink) were indicated in this fused phosphoproteome map. (B) Phosphorylation of RsbV, SpoIIAA, and PtsH according to the ProQ-Diamond stain at different times after starvation for glucose. Subsets of the dual channel images of the flamingo-stained image (green) versus the ProQ-Diamond stained image (red) are shown for the glucose starvation experiment according to the time points after transition to stationary phase as indicated.

Protein phosphorylation is not the only PTM in charge which can be directly visualized by gel-based proteomics. In addition, deformylation of proteins causes a shift of proteins to the acidic region of the 2-D gel [87]. The analysis of oxidative damage of proteins including reversible and irreversible thiol-modifications leads to new insights into oxidative stress physiology. This can also be addressed by a gel-based approach at a proteome wide scale. Thiol groups are particularly sensitive against oxidative damage leading to nonnative S-S bridges, via sulfenic and sulfinic to sulfonic acid forms of cysteine residues. Because, the last two reactions are irreversible thiol-modifications, oxidation of cysteine residues should be avoided in order to prevent protein inactivation. In E. coli, B. subtilis, S. aureus, and also eukaryotic organisms the glycolytic enzyme glyceraldehyde-3-phosphate-dehydrogenase (GapA) is a key redox© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

regulated enzyme which senses oxidative stress [88–91]. GapA has an active site thiolate which is highly reactive and susceptible to oxidation by ROS or alkylation by chemically diverse electrophilic compounds leading to an irreversible enzyme inactivation. This oxidation of the GapA thiolate to irreversible sulfonic acid in response to H2O2 treatment of S. aureus cells can be directly followed by a shift to the acidic region in the 2-D gel [90] (Fig. 9). After oxidative damage by H2O2 the enzyme of S. aureus is no longer active, followed by a drop in the ATP level and finally by a growth arrest. After almost 40 min the oxidant is detoxified by catalases and the newly synthesized GapA enzyme (left spot) cannot be damaged anymore. Subsequently, the ATP level increases followed by the resumption of growth (Fig. 9). The suggested oxidation of the cysteine thiol groups to sulfonic acid was directly proven by MS [90]. Hochgräfe et al. (2005) [92]

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Figure 9. Protein sulfonlylation of the glyceraldehyde 3-phosphate dehydrogenase Gap in response to oxidative stress in S. aureus. Autoradiograms showing protein synthesis of S. aureus before (control) and at different times (5 and 40 min) after exposure to 100 mM H2O2 as described previously [90]. The right Gap spot represents the oxidatively damaged Gap protein due the Cys151-sulfonylation which is an irreversible thiol-modification. The left undamaged Gap protein is newly synthesized after detoxification of H2O2 which is in accordance with restoration of growth under these conditions.

oped a fluorescence-based thiol modification assay combined with 2-D PAGE and MS to monitor the in vivo thiol-modification state of cytoplasmic B. subtilis proteins [93]. In growing cells proteins are mainly present in the reduced state. Only a few proteins were found to be thiol-modified, particularly enzymes that use redox-active disulfide bonds during their catalytic cycle, such as the thiol peroxidases AhpC and Tpx. In response to diamide stress that induces nonnative S-S bridges a significant increase in oxidized thiols in many proteins were found indicating a reversible thiol-oxidation and as a consequence protein damage (Fig. 10). A detailed study, however, showed that the majority of thiol groups were not damaged by oxidation but cysteinylated. B. subtilis and other Gram-positive bacteria are unable to synthesize the low molecular weight (LMW) thiol glutathione [94, 95]. Instead cysteine, CoASH and probably novel abundant LMW thiols serve as free thiol buffer in B. subtilis [96, 97]. Only a minor fraction of the population of proteins with reduced thiols is probably caused by nonnative intra- or intermolecular S-S bridges formation. That means that those fluorescence signals in the 2-D gel seen after diamide exposure are not caused by protein oxidation and damage but in contrast to this suggestion by S-cysteinylation of protein thiolates leading to a protection against irreversible oxidation to sulfonic acids [96]. As a physiological consequence, the thiol-reactive azocompound diamide triggers a thiol-specific oxidative stress response as can be monitored by transcriptomic and proteomic approaches (Leichert et al., 2003). Proteomic and transcriptomic signatures revealed the induction of the thiolspecific stress response not only after diamide treatment but also after exposure of B. subtilis cells to catechol and MHQ [49–51, 98, 99]. Catechol and MHQ become electrophilic thiol-reactive compounds after auto-oxidation to toxic orthoand para-benzoquinones which form S-adducts with cellular © 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim



thiols by a 1,4-reductive Michael-type addition [50, 51, 100]. Thus, the azocompound diamide and quinone-generating electrophiles catechol and MHQ lead most likely to depletion of protein thiolates and LMW nonprotein thiolates and which causes the induction of the thiol-specific stress response in B. subtilis. This thiol-specific stress response to electrophilic compounds is governed by an increasing number of regulatory proteins including Spx, CtsR, PerR, CymR, MhqR (YkvE), and YodB [49–51, 101–104]. Spx is a global transcriptional regulator that controls genes that function in maintenance of thiol-homeostasis in response to thiol-reactive electrophiles [101, 102]. Recently, we showed that the two novel MarR-type repressors YodB and MhqR control paralogous azoreductases AzoR1 and AzoR2 as well as paralogous nitroreductases, YodC and MhqN after exposure to thiol-stress conditions [49–51]. These paralogous azoreductases are major quinone and azocompound resistance determinants and have common functions as quinone reductases and azoreductases in detoxification of thiol-reactive electrophiles in B. subtilis to prevent depletion of cellular protein thiolates. Numerous cytoplasmic, cell wall-associated or membrane bound proteases are responsible to degrade irreversibly damaged or nonfunctional proteins by proteolysis. Thus, proteolysis determines the final destination in the proteins life. The clp machinery of B. subtilis is probably the main system that controls the life span of the individual proteins. Damaged or malfolded proteins within the cell that can no longer be repaired were considered as main substrates for the clp proteases. The proteomic view of proteolysis at a proteome-wide scale revealed a new major group of Clp substrates: unemployed proteins probably no longer required and no longer protected against a proteolytic attack [105]. This is the main result of a pulse-chase experiment that followed the fate of labeled proteins synthesized in growing cells (Fig. 11). After 5 min 35S-L-methionine pulse followed by a chase with nonradioactive methionine the radioactivity of the single proteins on a 2-D gel can be measured along the growth curve. Radioactively labeled proteins of growing cells after the pulse were false-colored red and labeled proteins during different stages after the chase period phase false-colored green. By this approach the stability of vegetative proteins synthesized in growing cells after pulse labeling can be compared with several sampling points along the growth curve relying on a combination of two gel images. A yellow protein is stable (combination of the same intensity of red and green), but a red-labeled protein no longer radioactively labeled in nongrowing cells has been degraded. Using gel evaluation software quantitative data on protein degradation can be provided. This proteome-wide proteolysis study showed that many proteins synthesized in growing cells with house-keeping functions and no longer active in nongrowing cells are degraded in a temporally controlled sequence. Notably, several first-committed-step enzymes for biosynthesis of aromatic and branched amino acids, cell wall precursors, purines, and pyrimidines appeared as putative


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Figure 10. Dual channel image of SyproRuby-stained 2-D gel (green image) compared to the thiol-oxidation fluorescence image (red image) after exposure to the thiol-reactive compound diamide. For labeling of reversible thiol-oxidations, cytoplasmic protein extracts of the B. subtilis wild type were harvested from cells exposed to 1 mM diamide for 10 min as described previously [92]. Briefly, cell extracts were alkylated with iodoacetamide to block reduced protein thiols and subsequently treated with TCEP and BODIPY FL C1-IA to reduce and fluorescence-label oxidized protein thiols. All proteins with increased reversible thiol-oxidations in response to diamide treatment are labeled.

ClpCP target. The current hypothesis is that proteins which are still active are protected against proteolysis because their integration in functional complexes prevents the interaction with the Clp machine. This example illustrates how a data driven proteomics approach and the “panorama view of proteomics” discloses new cellular phenomena never seen before that lead to new working hypotheses which have; however, to be proven by detailed follow-up studies.


Outlook – comprehensive gel-based proteomic signatures evaluate the global physiological state of cells

This review shows that gel-based proteomics is still a useful approach to gain a global view of bacterial physiology. However, only about 30–40% of the expressed proteins are covered by gel-based studies. They represent the cellular “working horses” contributing to the main metabolic pathways and most important cell functions such as growth, structural integrity or stress/starvation responses. Monitoring their expression and regulation on the proteome level may lead to a basic understanding of general cell physiology. Except for the lipoproteome fraction, gel-based approaches have failed to analyze the dynamics of the entire membrane proteome [106, 107]. Thus, gel-free approaches will become an inevitable technique to address essential physiological issues, and each group interested in physiological proteomics has to establish at least the basic gel-free techniques. © 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Protein expression profiling should be complemented by transcriptome analyses which allows a complete view of the changes in gene expression at the level of mRNA. Nevertheless, some issues cannot be addressed by DNA array techniques in a reasonable way as, e.g., protein targeting and localization (membrane integration, protein secretion) or the analysis of proteins present in nongrowing cells which are no longer synthesized. Protein expression profiling in growing and nongrowing cells can be done either at the level of protein amount or synthesis using highly sophisticated gel image analysis software. However, protein expression profiling is only the first step to understand physiological issues of bacteria. A combination of gel-based and gel-free proteomics can be used to follow the dynamics and fate of each single protein and its integration into global protein interaction networks. Finally, the stability of vegetative or starvation/stress-induced proteins can be analyzed by a pulse-chase approach at a proteome-wide scale. Figure 12 summarizes these “comprehensive proteomics signatures” for growing cells in response to glucose starvation including information on protein synthesis, accumulation, phosphorylation, thiol-modification, or even degradation. These comprehensive proteomics signatures should show that gel-based proteomics is a reasonable approach to investigate the main metabolic pathways during growth, reproduction, or in response to environmental changes because of the low complexity of bacteria. Furthermore, quantitative data can be generated for protein synthesis, amount or specific half-life times providing comprehensive information on the physiological state of cells.

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Figure 11. The proteome of B. subtilis wild type and DclpP mutant showing differentially degraded proteins. Comparison of the dual channel images of B. subtilis wild type (left) and DclpP mutant cells (right) at different times (t0, red images; t8 h, green images) after pulse-chaselabeling during exponential growth phase. All red appearing proteins in the wild type are synthesized during the exponential growth (t0) and degraded 8 h later (t8 h). Several of these are substrates for ClpP since these are more stable and no longer degraded at the 8 h time point after pulse-chase labeling in the DclpP mutant. Details of the putative substrates for ClpP proteolysis are listed in Gerth et al. 2008 [105].

Figure 12. Comprehensive proteome analyses of B. subtilis cells as shown by dual channel imaging. The different dual channel imaging possiblities are shown reflecting the comparison of (1) protein amounts (2) protein synthesis (4) protein stabilities (5) protein phosphorylations, and (6) reversible thiol-oxidations during the exponential growth (green images) and stationary phase (red images). The dual channel comparison of protein amount (green) versus synthesis (red) during the stationary phase is shown in (3).

© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim


M. Hecker et al.

In conclusion, gel-based proteomics will still assist gelfree techniques to come to new insights into essential issues, such as protein targeting, interaction, aggregation, PTM, protein damage, repair, and finally degradation at a proteome-wide scale. This should be the roadmap for physiological proteomics: the panorama view of proteomics visualizes cellular events never seen before. But now the challenge of proteomic studies is to get exciting ideas for future research, followed by detailed genetic and biochemical experiments that lead to novel mechanisms of cell physiology.

We are very thankful to all coworkers of the group of Michael Hecker for their contributions to the work presented in this article. The authors wish to thank further U. Völker, K. Altendorf, and R. Schmid for their great support and pioneering work in the development of the gel-based proteomics technology and N-terminal protein sequencing. In addition, we are thankful to U. Gerth, B. Voigt, D. Becher, S. Engelmann, and F. Hochgräfe for kindly providing figures for this article. We are indebted to DECODON GmbH for the close cooperation and the prerelease access to new software tools. We thank Volker Brözel (Brookings, SD, USA) for helpful and critical comment. This work was supported by grants from the Deutsche Forschungsgemeinschaft (SFB/TR34 and HE 1887/7-4 and HE 1887/8-1), the Bundesministerium für Bildung und Forschung (BACELL-SysMo 031397A), the Fonds der Chemischen Industrie, the Bildungsministerium of the country Mecklenburg-Vorpommern and European Union grants BACELL-Health (LSHG-CT-2004-503468) and BACELLBaSysBio (LSHG-CT-2006-037469) to M. H. The authors have declared no conflict of interest.



Proteomics 2008, 8, 4958–4975 [8] Görg, A., Weiss, W., Dunn, M. J., Current two-dimensional electrophoresis technology for proteomics. Proteomics 2004, 4, 3665–3685. [9] Görg, A., Obermaier, C., Boguth, G., Harder, A. et al., The current state of two-dimensional electrophoresis with immobilized pH gradients. Electrophoresis 2000, 21, 1037– 1053. [10] Himmelreich, R., Hilbert, H., Plagens, H., Pirkl, E., Li, B. C., Herrmann, R., Complete sequence analysis of the genome of the bacterium Mycoplasma pneumoniae. Nucleic Acids Res. 1996, 24, 4420–4449. [11] Regula, J. T., Ueberle, B., Boguth, G., Görg, A. et al., Towards a two-dimensional proteome map of Mycoplasma pneumoniae. Electrophoresis 2000, 21, 3765–3780. [12] Jaffe, J. D., Berg, H. C., Church, G. M., Proteogenomic mapping as a complementary method to perform genome annotation. Proteomics 2004, 4, 59–77. [13] Herrmann, R., Ruppert T., Proteome of Mycoplasma pneumoniae. Methods Biochem. Anal. 2006, 49, 39–56. [14] Hecker, M., Völker, U., Towards a comprehensive understanding of Bacillus subtilis cell physiology by physiological proteomics. Proteomics 2004, 4, 3727–3750. [15] Völker, U., Hecker, M., From genomics via proteomics to cellular physiology of the Gram-positive model organism Bacillus subtilis. Cell. Microbiol. 2005, 7, 1077–1085. [16] Hecker, M., Engelmann, S., Cordwell, S. J., Proteomics of Staphylococcus aureus – current state and future challenges. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 2003, 787, 179–195. [17] Engelmann, S., Hecker, M., Proteomic analysis to investigate regulatory networks in Staphylococcus aureus. Methods Mol. Biol. 2007, 431, 25–46. [18] Kunst, F., Ogasawara, N., Moszer, I., Albertini, A. M. et al., The complete genome sequence of the Gram-positive bacterium Bacillus subtilis. Nature 1997, 390, 249–256. [19] Kobayashi, K., Ehrlich, S. D., Albertini, A., Amati, G. et al., Essential Bacillus subtilis genes. Proc. Natl. Acad. Sci. USA 2003, 100, 4678–4683.

[1] Fleischmann, R. D., Adams, M. D., White, O., Clayton, R. A. et al., Whole-genome random sequencing and assembly of Haemophilus influenzae Rd. Science 1995, 269, 496–512.

[20] Baba, T., Takeuchi, F., Kuroda, M., Yuzawa, H. et al., Genome and virulence determinants of high virulence communityacquired MRSA, Lancet 2002, 359, 1819–1827.

[2] Wasinger, V. C., Cordwell, S. J., Cerpa-Poljak, A., Yan, J. X. et al., Progress with gene-product mapping of the Mollicutes: Mycoplasma genitalium. Electrophoresis 1995, 16, 1090– 1094.

[21] Diep, B. A., Gill, S. R., Chang, R. F., Phan, T. H. et al., Complete genome sequence of USA300, an epidemic clone of community-acquired methicillin-resistant Staphylococcus aureus. Lancet 2006, 367, 731–739.

[3] Aesbersold, R., Mann, M., Mass spectrometry-based proteomics. Nature 2003, 422, 198–207

[22] Gill, S. R., Fouts, D. E., Archer, G. L., Mongodin, E. F. et al., Insights on evolution of virulence and resistance from the complete genome analysis of an early methicillin-resistant Staphylococcus aureus strain and a biofilm-producing methicillin-resistant Staphylococcus epidermidis strain. J. Bacteriol. 2005, 187, 2426–2438.

[4] O’Farrell, P. H., High resolution two-dimensional electrophoresis of proteins. J. Biol. Chem. 1975, 250, 4007–4021. [5] Klose, J., Protein mapping by combined isoelectric focusing and electrophoresis of mouse tissues. A novel approach to testing for induced point mutations in mammals. Humangenetik 1975, 26, 231–243. [6] Neidhardt, F. C., VanBogelen, R. A., in: Storz, G., HenggeAronis, R. (Eds.) Bacterial Stress Responses, ASM Press, Washington DC 2000, p. 445.

[23] Holden, M. T., Feil, E. J., Lindsay, J. A., Peacock, S. J. et al., Complete genomes of two clinical Staphylococcus aureus strains: Evidence for the rapid evolution of virulence and drug resistance. Proc. Natl. Acad. Sci. USA 2004, 101, 9786– 9791.

[7] Görg, A., Two-dimensional electrophoresis with immobilized pH gradients: Current state. Biochem. Soc. Trans. 1993, 21, 130–132.

[24] Kuroda, M., Ohta, T., Uchiyama, I., Baba, T. et al., Whole genome sequencing of methicillin-resistant Staphylococcus aureus. Lancet 2001, 357, 1225–1240.

© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Proteomics 2008, 8, 4958–4975



[25] Tao, W. A., Aebersold, R., Advances in quantitative proteomics via stable isotope tagging and mass spectrometry. Curr. Opin. Biotechnol. 2003, 14, 110–118.

[41] Büttner, K., Bernhardt, J., Scharf, C., Schmid, R. et al., A comprehensive two-dimensional map of cytosolic proteins of Bacillus subtilis. Electrophoresis 2001, 22, 2908–2935.

[26] Link, A. J., Eng, J., Schieltz, D. M., Carmack, E. et al., Direct analysis of protein complexes using mass spectrometry. Nat. Biotechnol. 1999, 17, 676–682.

[42] Bernhardt, J., Büttner, K., Scharf, C., Hecker, M., Dual channel imaging of two-dimensional electropherograms in Bacillus subtilis. Electrophoresis 1999, 20, 2225–2240.

[27] Wolters, D. A., Washburn, M. P., Yates, J. R., III An automated multidimensional protein identification technology for shotgun proteomics. Anal. Chem. 2001, 73, 5683–5690.

[43] Voigt, B., Schweder, T., Becher, D., Ehrenreich, A. et al., A proteomic view of cell physiology of Bacillus licheniformis. Proteomics 2004, 4, 1465–1490.

[28] Davis, M. T., Beierle, J., Bures, E. T., McGinley, M. D. et al., Automated LC-LC-MS-MS platform using binary ionexchange and gradient reversed-phase chromatography for improved proteomic analyses. J. Chromatogr. B Biomed. Sci. Appl. 2001, 752, 281–291.

[44] Antelmann, H., Sapolsky, R., Miller, B., Ferrari, E. et al., Quantitative proteome profiling during the fermentation process of pleiotropic Bacillus subtilis mutants. Proteomics 2004, 4, 2408–2424.

[29] Washburn, M. P., Wolters, D., Yates, J. R., III Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat. Biotechnol. 2001, 19, 242– 247. [30] Lipton, M. S., Pasa-Tolic, L., Anderson, G. A., Anderson, D. J. et al., Global analysis of the Deinococcus radiodurans proteome by using accurate mass tags. Proc. Natl. Acad. Sci. USA 2002, 99, 11049–11054. [31] Gygi, S. P., Rist, B., Gerber, S. A., Turecek, F. et al., Quantitative analysis of complex protein mixtures using isotopecoded affinity tags. Nat. Biotechnol. 1999, 17, 994–999. [32] Eymann, C., Dreisbach, A., Albrecht, D., Bernhardt, J. et al., A comprehensive proteome map of growing Bacillus subtilis cells. Proteomics 2004, 4, 2849–2876.

[45] Becker, D., Selbach, M., Rollenhagen, C., Ballmaier, M. et al., Robust Salmonella metabolism limits possibilities for new antimicrobials. Nature 2006, 440, 303–307. [46] Bandow, J. E., Brötz, H., Leichert, L. I., Labischinski, H., Hecker, M., Proteomic approach to understanding antibiotic action. Antimicrob. Agents Chemother. 2003, 47, 948–955. [47] Brötz-Oesterhelt, H., Bandow, J. E., Labischinski, H., Bacterial proteomics and its role in antibacterial drug discovery. Mass Spectrom. Rev. 2005, 24, 549–565. [48] Bandow, J. E., Hecker, M., Proteomic profiling of cellular stresses in Bacillus subtilis reveals cellular networks and assists in elucidating antibiotic mechanisms of action. Prog. Drug Res. 2007, 64, 81–101.

[33] Ohlmeier, S., Scharf, C., Hecker M., Alkaline proteins of Bacillus subtilis: First steps towards a two-dimensional alkaline master gel. Electrophoresis 2000, 21, 3701–3709.

[49] Töwe, S., Leelakriangsak, M., Kobayashi, K., Van Duy, N. et al., The MarR-type repressor MhqR (YkvE) regulates multiple dioxygenases/glyoxalases and an azoreductase which confer resistance to 2-methylhydroquinone and catechol in Bacillus subtilis. Mol. Microbiol. 2007, 65, 40–56.

[34] Tobisch, S., Zühlke, D., Bernhardt, J., Stülke, J., Hecker, M., Role of CcpA in regulation of the central pathways of carbon catabolism in Bacillus subtilis. J. Bacteriol. 1999, 181, 6996– 7004.

[50] Leelakriangsak, M., Huyen, N. T. T., Töwe, S., van Duy, N. et al., Regulation of quinone detoxification by the thiol-stress sensing DUF24/MarR-like repressor, YodB in Bacillus subtilis. Mol. Microbiol. 2008, 67, 1108–1124.

[35] Hieu, C. X., Voigt, B., Albrecht, D., Becher, D. et al., Detailed proteome analysis of growing cells of the planctomycete Rhodopirellula baltica SH1(T). Proteomics 2008, 8, 1608– 1623.

[51] Antelmann, H., Hecker, M., Zuber, P., Proteomic signatures uncover thiol-specific electrophile resistance mechanisms in Bacillus subtilis. Expert Rev. Proteomics 2008, 5, 77–90.

[36] Gade, D., Gobom, J., Rabus, R., Proteomic analysis of carbohydrate catabolism and regulation in the marine bacterium Rhodopirellula baltica. Proteomics 2005, 5, 3672–3683.

[52] Luhn, S., Berth, M., Hecker, M., Bernhardt, J., Using standard positions and image fusion to create proteome maps from collections of two-dimensional gel electrophoresis images. Proteomics 2003, 3, 1117–1127.

[37] Markert, S., Arndt, C., Felbeck, H., Becher, D. et al., Physiological proteomics of the uncultured endosymbiont of Riftia pachyptila. Science 2007, 315, 247–250.

[53] Berth, M., Moser, F. M., Kolbe, M., Bernhardt, J., The state of the art in the analysis of two-dimensional gel electrophoresis images. Appl. Microbiol. Biotechnol. 2007, 76, 1223– 1243.

[38] Mäder, U., Homuth, G., Scharf, C., Büttner, K. et al., Transcriptome and proteome analysis of Bacillus subtilis gene expression modulated by amino acid availability. J. Bacteriol. 2002, 184, 4288–4295.

[54] Clark, B. N., Gutstein, H. B., The myth of automated, highthroughput two-dimensional gel analysis. Proteomics 2008, 8, 1197–1203.

[39] Kirkpatrick, D. S., Gerber, S. A., Gygi, S. P., The absolute quantification strategy: A general procedure for the quantification of proteins and post-translational modifications. Methods 2005, 35, 265–273. [40] Wang, G., Wu, W. W., Zeng, W., Chou, C. L., Shen, R. F., Label-free protein quantification using LC-coupled ion trap or FT mass spectrometry: Reproducibility, linearity, and application with complex proteomes. J. Proteome Res. 2006, 5, 1214–1223.

© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

[55] Ünlü, M., Morgan, M. E., Minden, J. S., Difference gel electrophoresis: A single gel method for detecting changes in protein extracts. Electrophoresis 1997, 18, 2071–2077. [56] Patton, W. F., Detection technologies in proteome analysis. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 2002, 771, 3–31. [57] Lilley, K. S., Friedman, D. B., All about DIGE: Quantification technology for differential-display 2D-gel proteomics. Expert Rev. Proteomics 2004, 1, 401–409.


M. Hecker et al.

Proteomics 2008, 8, 4958–4975

[58] Marouga, R., David, S., Hawkins, E., The development of the DIGE system: 2D fluorescence difference gel analysis technology.Anal. Bioanal. Chem. 2005, 382, 669–678.

[74] Eymann, C., Becher, D., Bernhardt, J., Gronau, K. et al., Dynamics of protein phosphorylation on Ser/Thr/Tyr in Bacillus subtilis. Proteomics 2007, 7, 3509–3526.

[59] Bernhardt, J., Weibezahn, J., Scharf, C., Hecker, M., Bacillus subtilis during feast and famine: Visualization of the overall regulation of protein synthesis during glucose starvation by proteome analysis. Genome Res. 2003, 13, 224–237.

[75] Cortay, J. C., Rieul, C., Duclos, B., Cozzone, A. J., Characterization of the phosphoproteins of Escherichia coli cells by electrophoretic analysis. Eur. J. Biochem. 1986, 159, 227– 237.

[60] Fuchs, S., Pané-Farré, J., Kohler, C., Hecker, M., Engelmann, S., Anaerobic gene expression in Staphylococcus aureus. J. Bacteriol. 2007, 189, 4275–4289.

[76] Bendt, A. K., Burkovski, A., Schaffer, S., Bott, M. et al., Towards a phosphoproteome map of Corynebacterium glutamicum. Proteomics 2003, 3, 1637–1646.

[61] Koburger, T., Weibezahn, J., Bernhardt, J., Homuth, G., Hecker, M., Genome-wide mRNA profiling in glucose starved Bacillus subtilis cells. Mol. Genet. Genomics 2005, 274, 1–12.

[77] Lévine, A., Vannier, F., Absalon, C., Kuhn, L. et al., Analysis of the dynamic Bacillus subtilis Ser/Thr/Tyr phosphoproteome implicated in a wide variety of cellular processes. Proteomics 2006, 6, 2157–2173.

[62] Tam, L. T., Antelmann, H., Eymann, C., Albrecht, D. et al., Proteome signatures for stress and starvation in Bacillus subtilis as revealed by a 2-D gel image color coding approach. Proteomics 2006, 6, 4565–4585.

[78] Oda, Y., Nagasu, T., Chait, B. T., Enrichment analysis of phosphorylated proteins as a tool for probing the phosphoproteome. Nat. Biotechnol. 2001, 19, 379–382.

[63] Antelmann, H., Tjalsma, H., Voigt, B., Ohlmeier, S. et al., A proteomic view on genome-based signal peptide predictions. Genome Res. 2001, 11, 14984–14502. [64] Tjalsma, H., Antelmann, H., Jongbloed, J. D., Braun, P. G. et al., Proteomics of protein secretion by Bacillus subtilis: Separating the “secrets” of the secretome. Microbiol. Mol. Biol. Rev. 2004, 68, 207–33. [65] Tjalsma, H., Lambooy, L., Hermans, P. W., Swinkels, D. W., Shedding & shaving: Disclosure of proteomic expressions on a bacterial face. Proteomics 2008, 8, 1415–1428. [66] Voigt, B., Schweder, T., Sibbald, M. J., Albrecht, D. et al., The extracellular proteome of Bacillus licheniformis grown in different media and under different nutrient starvation conditions. Proteomics 2006, 6, 268–281. [67] Jongbloed, J. D. H., Martin, U., Antelmann, H., Hecker, M. et al., TatC is a specificity determinant for protein secretion via the twin-arginine translocation pathway. J. Biol. Chem. 2000, 275, 41350–41357. [68] Jongbloed, J. D. H., Antelmann, H., Hecker, M., Nijland, R. et al., Selective contribution of the twin-arginine translocation pathway to protein secretion in Bacillus subtilis. J. Biol. Chem. 2002, 277, 44068–44078.

[79] Price, C. W., in: Sonenshein, A. L., Hoch, J. A., Losick, R. (Eds.)General stress response, Bacillus subtilis and Its Closest Relatives: From Genes to Cells, American Society for Microbiology Press, Washington, DC 2002, pp. 369–384. [80] Hardwick, S. W., Pané-Farré, J., Delumeau, O., MarlesWright, J. et al., Structural and functional characterization of partner switching regulating the environmental stress response in Bacillus subtilis. J. Biol. Chem. 2007, 282, 11562– 11572. [81] Gaidenko, T. A., Kim, T. J., Weigel, A. L., Brody, M. S., Price, C. W., The blue-light receptor YtvA acts in the environmental stress signaling pathway of Bacillus subtilis. J. Bacteriol. 2006, 188, 6387–6395. [82] Avila-Pérez, M., Hellingwerf, K. J., Kort, R., Blue light activates the sigmaB-dependent stress response of Bacillus subtilis via YtvA. J. Bacteriol. 2006, 188, 6411–6414. [83] van Schaik, W., Abee, T., The role of sigmaB in the stress response of Gram-positive bacteria – targets for food preservation and safety. Curr. Opin. Biotechnol. 2005, 16, 218– 224. [84] Hecker, M., Pané-Farré, J., Völker, U., SigB-dependent general stress response in Bacillus subtilis and related grampositive bacteria. Annu. Rev. Microbiol. 2007, 61, 215–236.

[69] Jongbloed, J. D., Grieger, U., Antelmann, H., Hecker, M. et al., Two minimal Tat translocases in Bacillus. Mol. Microbiol. 2004, 54, 1319–1325.

[85] Kirstein, J., Turgay, K., A new tyrosine phosphorylation mechanism involved in signal transduction in Bacillus subtilis. J. Mol. Microbiol. Biotechnol. 2005, 9, 182–188.

[70] Zanen, G., Antelmann, H., Meima, R., Jongbloed, J. D. et al., Proteomic dissection of potential signal recognition particle dependence in protein secretion by Bacillus subtilis. Proteomics 2006, 6, 3636–3648.

[86] Kirstein, J., Dougan, D. A., Gerth, U., Hecker, M., Turgay, K., The tyrosine kinase McsB is a regulated adaptor protein for ClpCP. EMBO J. 2007 26, 2061–2070.

[71] Vitikainen, M., Lappalainen, I., Seppala, R., Antelmann, H. et al., Structure-function analysis of PrsA reveals roles for the parvulin-like and flanking N- and C-terminal domains in protein folding and secretion in Bacillus subtilis. J. Biol. Chem. 2004, 279, 19302–19314.

[87] Bandow, J. E., Becher, D., Büttner, K., Hochgräfe, F. et al., The role of peptide deformylase in protein biosynthesis: A proteomic study. Proteomics 2003, 3, 299–306. [88] Leichert, L. I., Jakob, U., Global methods to monitor the thiol-disulfide state of proteins in vivo. Antioxid. Redox Signal. 2006, 8, 763–772.

[72] Marraffini, L. A., Dedent, A. C., Schneewind, O., Sortases and the art of anchoring proteins to the envelopes of grampositive bacteria. Microbiol. Mol. Biol. Rev. 2006, 70, 192– 221.

[89] Leichert, L. I., Gehrke, F., Gudiseva, H. V., Blackwell, T. et al., Quantifying changes in the thiol redox proteome upon oxidative stress in vivo. Proc. Natl. Acad. Sci. USA 2008, 105, 8197–8202.

[73] Desvaux, M., Dumas, E., Chafsey, I., Hébraud, M., Protein cell surface display in Gram-positive bacteria: From single protein to macromolecular protein structure. FEMS Microbiol. Lett. 2006, 256, 1–15.

[90] Weber, H., Engelmann, S., Becher, D., Hecker, M., Oxidative stress triggers thiol oxidation in the glyceraldehyde-3-phosphate dehydrogenase of Staphylococcus aureus. Mol. Microbiol. 2004, 52, 133–140.

© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Proteomics 2008, 8, 4958–4975 [91] Chuang, D. M., Hough, C., Senatorov, V. V., Glyceraldehyde3-phosphate dehydrogenase, apoptosis, and neurodegenerative diseases. Annu. Rev. Pharmacol. Toxicol. 2005, 45, 269–290. [92] Hochgräfe, F., Mostertz, J., Albrecht, D., Hecker, M., Fluorescence thiol modification assay: Oxidatively modified proteins in Bacillus subtilis. Mol. Microbiol. 2005, 58, 409–425. [93] Leichert, L. I., Scharf, C., Hecker, M., Global characterization of disulfide stress in Bacillus subtilis. J. Bacteriol. 2003, 185, 1967–1975. [94] Gusarov, I., Nudler, E., NO-mediated cytoprotection: Instant adaptation to oxidative stress in bacteria. Proc. Natl. Acad. Sci. USA 2005, 102, 13855–13860.



ent degradation systems involved in the catabolism of aromatic compounds in Bacillus subtilis. Proteomics 2007, 7, 1391–1408. [100] Monks, T. J., Hanzlik, R. P., Cohen, G. M., Ross, D., Graham D. G., Quinone chemistry and toxicity. Toxicol. Appl. Pharmacol. 1992, 112, 2–16. [101] Nakano, S., Kuster-Schock, E., Grossman, A. D., Zuber, P., Spx-dependent global transcriptional control is induced by thiol-specific oxidative stress in Bacillus subtilis. Proc. Natl. Acad. Sci. USA 2003, 100, 13603–13608. [102] Zuber, P., Spx-RNA polymerase interaction and global transcriptional control during oxidative stress. J. Bacteriol. 2004, 186, 1911–1918.

[95] Newton, G. L., Arnold, K., Price, M. S., Sherrill, C. et al., Distribution of thiols in microorganisms: Mycothiol is a major thiol in most actinomycetes. J. Bacteriol. 1996, 178, 1990– 1995.

[103] Even, S., Burguiere, P., Auger, S., Soutourina, O. et al., Global control of cysteine metabolism by CymR in Bacillus subtilis. J. Bacteriol. 2006, 188, 2184–2197.

[96] Hochgräfe, F., Mostertz, J., Pöther, D. C., Becher, D. et al., Scysteinylation is a general mechanism for thiol protection of Bacillus subtilis proteins after oxidative stress. J. Biol. Chem. 2007, 282, 25981–25985.

[104] Leelakriangsak, M., Kobayashi, K., Zuber, P., Dual negative control of spx transcription initiation from the P3 promoter by repressors PerR and YodB in Bacillus subtilis. J. Bacteriol. 2007, 189, 1736–1744.

[97] Lee, J.-W., Soonsanga, S., Helmann, J. D., A complex thiolate switch regulates the Bacillus subtilis organic peroxide repressor OhrR. Proc. Natl. Acad. Sci. USA 2007, 104, 8743– 8748.

[105] Gerth, U., Kock, H., Kusters, I., Michalik, S. et al., Clp-dependent proteolysis down-regulates central metabolic pathways in glucose-starved Bacillus subtilis. J. Bacteriol. 2008, 190, 321–331.

[98] Tam, L. T., Eymann, C., Albrecht, D., Sietmann, R. et al., Differential gene expression in response to phenol and catechol reveals different metabolic activities for the degradation of aromatic compounds in Bacillus subtilis. Environ. Microbiol. 2006, 8, 1408–1427.

[106] Wolff, S., Otto, A., Albrecht, D., Zeng, J. S. et al., Gel-free and gel-based proteomics in Bacillus subtilis: A comparative study. Mol. Cell. Proteomics 2006, 5, 1183–1192.

[99] Duy, N. V., Wolf, C., Mäder, U., Lalk, M. et al., Transcriptome and proteome analyses in response to 2-methylhydroquinone and 6-brom-2-vinyl-chroman-4-on reveal differ-

© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

[107] Wolff, S., Antelmann, H., Albrecht, D., Becher, D. et al., Towards the entire proteome of the model bacterium Bacillus subtilis by gel-based and gel-free approaches. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 2007, 849, 129– 140.

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