Bacterial proteomics and its role in antibacterial drug discovery

BACTERIAL PROTEOMICS AND ITS ROLEIN ANTIBACTERIAL DRUG DISCOVERY

Heike Brotz-Oesterhelt,1* Julia Elisabeth Bandow,2 and Harald Labischinski11Bayer HealthCare AG, Anti-infective Research, Wuppertal, Germany2Pfizer, Inc., PGRD Ann Arbor, Michigan

Received 26 February 2004; received (revised) 29 April 2004; accepted 9 May 2004

Published online 29 July 2004 in Wiley InterScience (www.interscience.wiley.com) DOI 10.1002/mas.20030

Gene-expression profiling technologies in general, and proteo-mic technologies in particular have proven extremely useful tostudy the physiological response of bacterial cells to variousenvironmental stress conditions. Complex protein toolkits co-ordinated by sophisticated regulatory networks have evolved toaccommodate bacterial survival under ever-present stressconditions such as varying temperatures, nutrient availability,or antibiotics produced by other microorganisms that competefor habitat. In the last decades, application of man-made anti-bacterial agents resulted in additional bacterial exposure toantibiotic stress. Whereas the targeted use of antibiotics hasremarkably reduced human suffering from infectious diseases,the ever-increasing emergence of bacteria that are resistant toantibiotics has led to an urgent need for novel antibioticstrategies. The intent of this review is to present an overview ofthe major achievements of proteomic approaches to studyadaptation networks that are crucial for bacterial survival witha special emphasis on the stress induced by antibiotic treat-ment. A further focus will be the review of the, so far few,published efforts to exploit the knowledge derived from bac-terial proteomic studies directly for the antibacterial drug-discovery process. # 2004 Wiley Periodicals, Inc., Mass SpecRev 24:549–565, 2005Keywords: 2D gel electrophoresis; proteomics; antibiotics;drug discovery; bacteria

I. INTRODUCTION

The term proteome, in analogy to the term genome, was coinedto describe the complete set of proteins that an organism hasproduced under a defined set of conditions (Wasinger et al.,1995). The genome is static because it represents the blueprint forall cellular properties that a cell is able to develop. In contrast, theproteome is highly dynamic and much more complex than thegenome. It is critical for survival that the protein composition of acell is constantly adjusted to meet the challenges of changingenvironmental conditions. Already in 1975, the powerful methodof two-dimensional-polyacrylamide gel electrophoresis (2D-PAGE) was introduced that allowed one to separate highlycomplex cellular protein extracts into individual proteins on asingle gel based on two properties of the proteins the isoelectric

point (pI) and the molecular weight (MW), (Klose, 1975;O’Farrell, 1975). Proteomics, and 2D-PAGE in particular, hasbeen used from the beginning to study the bacterial proteomeunder different growth conditions (Linn & Losick, 1976; Reeh,Pedersen, & Neidhardt, 1977; Agabian & Unger, 1978) andvarious external stress factors (Young & Neidhardt, 1978;Krueger & Walker, 1984; Gomes et al., 1986).

However, it was only after 1995 that a new era was opened tothe study of the dynamic behavior of the bacterial proteome bythe advent of the first complete genome sequence of a bacterium,Haemophilus influenzae strain RD KW20 (Fleischmann et al.,1995). Based on a well-annotated genomic sequence, it becamepossible to introduce large-scale mass spectrometry (MS) tech-niques to identify virtually every protein detected on a 2D gel.The increase in throughput, the partial automation, and the higherreproducibility of 2D-PAGE analysis recently made it a veryattractive tool to study cellular functions on a molecular level.The complete genomic sequences of more than 120 bacteria arenow publicly available (for an constantly updated list, see http://www.tigr.org/tigr-scripts/CMR2/CMRGenomes.spl) that allowone to select among a variety of microorganisms for proteomicinvestigations according to the scientific question of interest. Inparallel, MS techniques, advanced to identify many proteins from2D gels and from alternatives to gel electrophoresis such as theIsotope-Coded Affinity Tag (ICAT) technology, have emerged toovercome some of the weaknesses of the 2D-gel approach (forrecent reviews, see e.g., Godovac-Zimmermann & Brown, 2001;Hamdan & Righetti, 2002; Aebersold & Mann, 2003; Lill, 2003;Sechi & Oda, 2003).

Compared to eukaryotic cells, bacteria are great modelorganisms to study regulatory networks, protein function, andeven cell differentiation, because their genomes are relativelysmall and adaptation processes are less complex and involvesmaller numbers of protein components. Some bacteria are easilygenetically manipulated and are thus excellent models to studyprotein function. In addition, bacteria are commonly used in thefood industry as well as in biotechnology. In both areas, it isdesirable to understand bacterial metabolism in order to optimizeproduction yields and quality. Bacteria also have an even moredirect impact on human life in that a variety of species areindispensable for aspects as immune-system maturation, nutri-tion digestion, and vitamin production (a 70 kg human containsapproximately 1 kg of bacteria, and thus more bacterial thanhuman cells). On the other hand, interactions harmful to thehuman host occur when bacteria override the defense barriers andcause infections. In fact, infections by microorganisms causesome 17 million deaths each year according to WHO statistics.

Mass Spectrometry Reviews, 2005, 24, 549– 565# 2004 by Wiley Periodicals, Inc.

————*Correspondence to: Heike Brotz-Oesterhelt, Bayer Pharma Research

Center, Building 405, D-42096 Wuppertal, Germany.

E-mail: [email protected]

Although most of those deaths occur in the less-developedcountries, death due to infectious diseases is back to rank numberthree even in the most developed countries such as the US(Armstrong, Conn, & Pinner, 1999). One important reason forthat unpleasant development is the fact that bacteria that werepreviously susceptible to the large armory of antibiotics havenow developed resistance against them (Hiramatsu et al., 2001;WHO, 2001; Appelbaum, 2002; Walsh, 2003). Another reason isironically provided by the progress in medicine in general,because we are becoming older and more often subject toaggressive treatment regimens; for example, in surgery, trans-plantation, and cancer chemotherapy. All of those manipulationslead to a suppression of our immunological defense capabilities,and, thereby, to more serious and more difficult to treat infections.Thus, novel treatment options are urgently required, and the needfor novel antibacterial agents without cross-resistance to existingantibiotics as well as the development of alternative treatmentregimens should have high priority on any meaningful publichealth agenda. In that environment, it is not astonishing thatproteome analysis of the consequences of antimicrobial treat-ment for bacteria has recently gained increasing interest. It can,on one hand, provide a deeper insight into how a bacteriumresponds to a certain antimicrobial treatment. In addition, bene-fits are expected in many other aspects of modern drug devel-opment approaches such as the identification of novel target areasand the elucidation of the molecular mechanisms of action ofnovel drug candidates.

Thus, the intent of this review is to present an overview of themajor achievements in proteomic studies of adaptation networksthat are crucial for bacterial survival with a special emphasis onstress that is induced by antibiotic treatment. A further focuswill be the review of the, so far few, published efforts to exploitthe knowledge derived from bacterial proteomic applicationsdirectly for the antibacterial drug-discovery process.

We will also touch on some proteome studies that aim at amore general insight into the physiological flexibility of bacteriaas well as on some methodological pre-requisites. However, thereader interested in a full overview of the latter topics is referredto some excellent recent reviews (Gorg et al., 2000; Nyman,2001; Lilley, Razzaq, & Dupree, 2002; Hecker, 2003).

II. THE ROLE OF PROTEOMICS TO DECIPHERTHE BACTERIAL RESPONSE TOWARDS CHANGESIN ENVIRONMENTAL CONDITIONS ANDANTIBIOTIC ATTACK

The capability to grow many bacterial species in well-definedartificial culture media has been a pre-requisite for our current in-depth understanding of bacterial physiology. Very often, thoseculture media provide cockaigne-like growth conditions thatallow for a maximal and uniform logarithmic bacterial growthbehavior until some components of the medium become exhaust-ed and logarithmic growth ceases. Under such optimal condi-tions, the protein composition of the cell is usually quite constantand tuned to support the special conditions of fast growth as, forexample, support of several DNA-replication forks within asingle cell and maximal protein biosynthesis. However, outsidethe laboratory bacteria face much less supportive and highly

variable growth conditions with respect to temperature, pH,osmolarity, nutrient availability, host interactions, etc. It shouldbe noted that those stress situations, often regarded as ‘‘natura-lly’’ occurring, do not principally differ from the stresses inducedby antibiotic attack. Antibiotics are a frequent encounter formany bacteria in their natural habitats, because many micro-organisms produce them to suppress the growth of competitors.Actually, the capability of a microorganism to produce a sub-stance that prevents the growth of another is eponymous for theterm antibiotic, although it is nowadays used more broadly toinclude man-made compounds as well. Even antibiotic classesthat stem from purely synthetic approaches and never experi-enced by bacteria during evolution can, to a certain extent, mimic‘‘natural’’ processes for which bacteria have developed regula-tory mechanisms. For instance, the oxazolidinones, which inhibitprotein synthesis (Livermore, 2003), simulate a starvation-likesituation. Also, the quinolones as topoisomerase-inhibitors(Drlica & Zhao, 1997) cause DNA-replication errors and repairsystem failures to which the bacteria react with their SOSresponse (Sutton et al., 2000).

The evolutionary success of bacteria was strongly dependenton their ability to respond to such adverse conditions via abewildering range of behavioral responses (Armitage et al.,2003). A large number of external and internal signal moleculesand signal transduction processes are present in bacteria to adapttheir protein composition to the changing requirements of theirenvironment (Armitage et al., 2003). Several of the environ-mental challenges are experienced by many bacterial species, andare, therefore, met by somewhat conserved response mechan-isms. However, it should be clear from the foregoing that themajority of the reactions are rather species-specific, and dependon the environmental and lifestyle preferences of the species.Proteomics technologies appear to be the natural tools to studythe consequences of those regulatory processes on proteincomposition. Key to the physiological interpretation of proteomestudies performed by 2D-PAGE, the most commonly usedtechnology platform, is the determination of the identity of theproteins contained in the spots on the gel. Thus, we will start withsome remarks on the process of proteome mapping.

A. Proteome Mapping

The large collection of fully sequenced bacterial genomesincludes those of important pathogens such as H. influenzae,Staphylococcus aureus, Enterococcus faecium, Enterococcusfaecalis, Streptococcus pneumoniae, Pseudomonas aerigunosa,Mycobacterium tuberculosis, or Escherichia coli, which are inthe focus of antibacterial drug discovery (http://www.tigr.org/tigr-scripts/CMR2/CMRGenomes.spl). The genome contains awealth of information that helps an organism to survive, but thisblueprint does not reveal which of the encoded molecules arerelevant under any given condition. The majority of effectormolecules in a cell that act and interact to make life possible areproteins. Although one can predict from the genome the numberof encoding entities (open reading frames), one cannot directlydeduce the number of different proteins that an organism iscapable of generating. One needs to perform global proteinanalyses to define the protein composition of a given cell under acertain circumstance.

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Classical 2D-PAGE is still the method of choice for theanalysis of the protein compartment of an organism. Proteomemapping utilizes different 2D gel formats to detect and index asmany proteins of an organism as possible. Protein identification,typically by mass spectrometry but also N-terminal sequencing,links a protein spot on the gel to its coding sequence andknowledge on protein function, which has mostly been generatedby classical molecular biology experiments. Ideally, each genewould be represented by one or more protein spot(s) on at leastone of the gel formats. Unfortunately, not all proteins separatewell in 2D-PAGE or are present in sufficient amounts for detec-tion, but proteome maps for some organisms are very advanced.The map of Mycoplasma pneumoniae, for instance, covers up to44% of the predicted open reading frames (Ueberle, Frank, &Herrmann, 2002). In the case of H. influenzae, the authorsidentified the gene products of 33% of the open reading framesand extrapolated from the number of identified and non-identifiedproteins on the gels that they could visualize approximately 70%of the predicted open reading frames (Langen et al., 2000).Similar coverage was reported for E. coli (Tonella et al., 2001).Today, many bacterial protein maps are available; among thosemaps several are from pathogenic bacteria, including Chlamydiapneumoniae (Vandahl et al., 2001), M. tuberculosis (Schmidtet al., 2003), S. aureus (Cordwell et al., 2002), and Helicobacterpylori (Cho et al., 2002). Several of those maps are accessiblevia World-Wide-Web servers (e.g., http://microbio2.biologie.uni-greifswald.de:8880/2dnet.htm, http://www.mpiib-berlin.mpg.de/2D-PAGE/, http://us.expasy.org/ch2d/).

One of the most comprehensive mapping studies was per-formed on H. influenzae, a causative agent for respiratory-tractinfections. Its genome encodes for approximately 1,742 geneproducts, and is comparably small. The proteome map ofH. influenzae is well-advanced because that organism is easilycultivated and has been used to study the proteome underdifferent growth conditions, including treatment with antibioticsthat inhibit DNA, RNA, and protein synthesis (Evers et al., 2001;Gmuender et al., 2001). Another proteome study of that organismaimed at the identification of potential vaccine candidates(Thoren et al., 2002). The 2D-gel proteome map of H. influenzaepublished in 2000 (Langen et al., 2000) displays approximately500 identified proteins, representing about 30% of all predictedopen reading frames. Those proteins were mainly identified bypeptide mass fingerprinting, using MALDI-TOF-MS, and someadditional proteins were identified by amino acid compositionanalyses. On 2D images of crude protein extracts (solublefraction pI 3–10), about 1,100 protein spots were detected.Different methods were studied to efficiently enrich low-abundance proteins that were not visible on 2D gels obtainedwith crude cell extracts. In addition, maps were created forsoluble basic proteins in the pI range of 8–11, and for envelope-bound proteins in the pI range of 4.5–9.5. Great progress wasachieved in the still-difficult separation of membrane proteins:70 membrane proteins were identified from 2D gels in that studyfor the first time. The membrane proteins were separated by aninverse, discontinuous electrophoresis system, using the cationicdetergent benzyldimethyl-n-hexadecylammonium chloride anda separation gel at pH 2.1 in the first dimension and a standardSDS gel (anionic detergent) in the second dimension. Thus,separation was performed according to molecular mass in both

dimensions. One-third of the �1,350 H. influenzae proteins withknown function have been identified on at least one of the gelformats applied in that mapping study. That 2D reference mapprovides an excellent basis for experiments designed to study thecellular response to external stimuli. Recently, a study on thesame organism has been published that elegantly demonstratesthe potential of non-gel-based technology for the purpose ofproteome mapping and analysis (Kolker et al., 2003). Approxi-mately 25% of all predicted open reading frames were detectedby liquid chromatography (LC) coupled with ion-trap tandemmass spectrometry (MS/MS). Whereas the number of identifiedproteins was quite similar to that identified in the 2D gel-basedstudy, there were some interesting differences in the type ofproteins detected. The LC/MS/MS technology identified moreribosomal proteins than the 2D gel approach (80 vs. 34%) and alsomore membrane proteins. Vice versa, this difference obviouslymeans that some proteins detected by 2D gel analysis were notfound in the LC/MS/MS experiments. Future will tell, whetherboth types of methods will co-evolve, or whether the inherentadvantages of one technique will attract attention predominantly.

The mapping study of S. aureus shall be taken as a furtherexample of proteome analysis of a crucial human pathogen.S. aureus is one of the major causes of community-acquired andnosocomial infections, and raises increasing public healthconcerns due to progressing multi-drug resistance (Hiramatsuet al., 2001; Sievert et al., 2002). Due to a variety of differentvirulence factors, including different cell wall-associated pro-teins and extracellular toxins, S. aureus causes a broad spectrumof infections, and the current drug-discovery programs of manypharmaceutical companies aim at targeting that pathogen.Despite its importance, information on the proteome of S. aureusemerged only recently. One reason is that the first genomesequence of this species, which is indispensable for rapid proteinidentification by MS-techniques, was not publicly available until2001 (Kuroda et al., 2001). However, because several groups arepursuing S. aureus proteomics, substantial progress has beenmade (for a review, see Hecker, Engelmann, & Cordwell, 2003).In a recent mapping study, 377 protein spots were analyzed froma cytoplasmic protein map (Fig. 1) that could be linked to 266(approximately 12%) of the open reading frames (Cordwell et al.,2002). Together with the extracellular proteins describedpreviously (Ziebandt et al., 2001), at least 20% of the theoreticalS. aureus proteome has now been identified.

The establishment of such protein reference maps is acrucial starting point for many physiological studies that mayfollow. However, because such proteome maps do not depict a‘‘real’’ gel, but represent virtual compilations of all proteins everdetected or identified in an organism, they do not disclose whichsubset of proteins is expressed under the specific growth con-dition of interest. In order to obtain that information, protein-expression profiles must be generated as discussed in thefollowing paragraph.

B. The Concept of Proteomic Signatures

The protein map links a protein spot on a 2D gel to its corres-ponding open reading frame and the respective knowledge onprotein function. In contrast, the protein-expression profile is the

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quantitative catalog of proteins made by a cell under a givencircumstance (VanBogelen et al., 1999; VanBogelen, 2003). It isthe protein-expression profile that indicates which particularsubset of proteins is present under the growth condition studied.In spite of the progress in proteome mapping described in theprevious section, it is still not technically feasible to obtain acomplete expression profile from a single 2D gel, because not allproteins are equally well-separated by this technique. Extremelybasic, acidic, small, or large proteins as well as those that arepoorly soluble or appear in low-abundance still pose majorchallenges. Nevertheless, classical 2D gels still show a substan-tial portion of the protein expression profile and are widely usedto study the cellular response to external stimuli.

For all proteins with an altered expression in response toa particular stimulus, the expression ‘‘stimulon’’ was coined(Neidhardt, Ingraham, & Schaechter, 1990). For example, allproteins that are up- or down-regulated after a shift to high-growth temperature belong to the heat-shock stimulon. The termstimulon describes the changes in protein expression on a pheno-typic level, and does not provide any information on theunderlying transcriptional regulation. A ‘‘regulon,’’ on the otherhand, consists of proteins that are under the control of the sameglobal transcriptional regulator. Even in bacteria, the least-complex organisms, a stimulon usually consists of more than oneregulon, demonstrating the complexity of regulation required foradaptation. In our heat-shock example, three regulons contributeto the heat-shock stimulon in B. subtilis (Fig. 2): (1) class I heat-shock proteins under the control of the global repressor HrcA,

including the chaperones of the GroEL and DnaK machines,(2) class III heat-shock proteins under the control of the globalregulator CtsR, and (3) the general stress proteins that depend onthe alternative sigma factor sB for transcription. In addition, afourth class contains further heat-responsive proteins that couldnot yet be assigned to any regulon (Hecker, Schumann, & Volker,1996; Hecker, 2003).

By studying expression levels of a multitude of proteinsunder a variety of different growth conditions, specific proteinsbecome indicative of a particular physiological state of the cell.Such a subset of proteins, whose expression levels are charac-teristic for a defined condition, was also designated ‘‘proteomicsignature’’ (VanBogelen et al., 1999). To identify a proteomicsignature, it is essential to recognize the connection between theexpression levels of specific proteins and a particular physiolo-gical state. Knowledge of the identity or function of thoseproteins is not strictly required, although it helps to understandthe molecular basis for their expression. Often, it is necessary toanalyze several related and unrelated conditions to propose andverify the proteomic signature for a certain environmental factorof interest. However, once such protein signatures are establishedfor a variety of different physiological states, that compilationcan be extremely helpful in the interpretation of a proteinexpression profile obtained under an unprecedented growthcondition.

Some proteomic signatures published previously for E. coli(VanBogelen & Neidhardt, 1990; VanBogelen et al., 1999) areparticularly illustrative to describe how that concept can be

FIGURE 1. Example of a protein reference map. The proteome of Staphylococcus aureus 8325 was

separated by 2D-gel electrophoresis, using an immobilized pH gradient in the range of pI 4–7. Proteins were

stained with silver, and were identified by MALDI-MS after tryptic digestion. The identity of selected

proteins that serve as landmarks on the gel are indicated. Reproduced from Hecker, Engelmann, & Cordwell

(2003), with permission from Elsevier, copyright 2003.


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applied to studies on bacterial physiology in the presence ofexternal stress factors, including antibiotic treatment. In thosestudies, a clear correlation was demonstrated between the pro-teomic signatures for growth at high and low temperature on onehand, and the changes in protein expression profiles in responseto antibiotic inhibition of ribosomal function on the other hand.Between 23 and 378C, protein expression profiles do not showspecific signatures for growth temperature. Outside of that range,however, there are protein subsets characteristic for growth at lowand high temperature. Some proteins seem to behave as cellularthermometers: their amount changes gradually with increasing/decreasing temperature. Other proteins are regulated in an off/onfashion, and are highly induced specifically at either high or lowgrowth temperature. At high temperature, the folding of newlysynthesized proteins is impaired, resulting in misfolded proteinsthat trigger the induction of chaperones and proteases. In con-trast, at low temperature the proteins involved in the translationprocess (ribosomal proteins and elongation factors) are inducedin addition to the specific cold-shock proteins, suggesting

that under this condition translation is the rate-limiting step forgrowth of E. coli.

The ribosome is also the target of many antibiotics, thatinterfere with translation via different molecular mechanismsof action, and their effects on the proteome overlap with thesignatures for growth temperature. Aminoglycosides such asstreptomycin and kanamycin interfere with the ribosomal proof-reading activity and cause an increase in mistranslation. Theresulting accumulation of mistranslated and, therefore, mis-folded proteins leads, just as the increase in growth tempera-ture, to the induction of chaperons and proteases. Similarly,puromycin—a protein synthesis inhibitor that causes abortivetranslation—leads to the accumulation of truncated and mis-folded proteins, thereby also inducing the heat-shock signature.However, treatment with all three of those antibiotics alsoinduces an additional response that is not observed during theshift to high growth temperature: the stringent response inE. coli,which is an adaptive response to limited availability of aminoacids. The stringent response is triggered by an increase in ppGpp

FIGURE 2. Heat-shock stimulon in Bacillus subtilis. Three regulons contribute to the response to heat

stress in this organism. The first regulon is under control of the HrcA repressor, and contains chaperones of

the GroEL and DnaK machinery (marked by *), which are crucial for protein folding during heat stress. The

second is the CtsR regulon (marked by #), which regulates the chaperones of the Clp family and the Clp

protease, and the third is thesB regulon (marked byþþ). Whereas the former two regulons react specifically

to heat stress, the large regulon controlled by the alternative sigma factor sB is induced by various kinds of

stress and starvation stimuli. The bar charts depict the expression levels of one representative member of the

respective regulons under different stress conditions: C, control; H, heat; E, ethanol; S, salt; G, glucose

limitation; Pm, treatment with the antibiotic puromycin; Ox, oxidative stress. Figure kindly provided by J.

Bernhardt & M. Hecker, University of Greifswald.


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and manifests in a down-regulation of many genes, includingthose that encode rRNA and proteins involved in translation.Thus, the combination of the signature for misfolded proteins andthat for the stringent response results in the characteristic pro-teome expression profile for streptomycin, kanamycin, andpuromycin in E. coli (VanBogelen et al., 1999).

Another group of antibiotics impairs the efficiency of thepeptidyl transferase reaction: tetracycline, chloramphenicol,erythromycin, fusidic acid, and spiramycin. Although they differin their exact binding sites and in the particular molecularmechanisms of action, they all have one thing in common withgrowth at low temperature: they slow down translation. InE. coli,those antibiotics, as growth in the cold, lead to an induction ofcold shock proteins and of ribosomal proteins.

Each organism is adapted to a particular ecological niche,which is reflected on the genome level by differences in the typesof proteins that are encoded and by variations in their amino acidsequences. That adaption is achieved by differences in post-transcriptional and post-translational regulation that mediate theadaptation on the protein level. Therefore, proteins that constitutea proteomic signature for a specific condition in one organism donot necessarily belong to the proteomic signature for the samephysiological state in another organism. We take the treatmentof E. coli and B. subtilis by antibiotic inhibitors of protein syn-thesis as an example to demonstrate how protein signatures mayvary between bacterial species. When B. subtilis is treated withkanamycin or streptomycin, chaperons and proteases are inducedas in E. coli, but in contrast to E. coli the stringent response is nottriggered by those antibiotics. Similarly, treatment of B. subtiliswith tetracycline, chloramphenicol, erythromycin, and fusidicacid leads to an induction of proteins, forming the translationapparatus; however in contrast to E. coli cold shock proteins arenot induced (Bandow et al., 2003a).

C. Snapshots of Protein Biosynthesis: MetabolicLabeling and Dual-Channel Imaging

For a given cell, it is crucial for survival to quickly adjust itsprotein composition and the activity of individual proteins inorder to meet the challenges of ever-changing growth conditions.That adaptation is mediated on a number of levels: transcrip-tional, post-transcriptional, and translational regulation; proteinstability also effects protein levels (the amount of protein presentunder a given condition at a certain time point), whereas post-translational modification is often a means of regulating proteinactivity. 2D gel-based proteomics techniques are not only well-suited to study protein levels and to detect protein modifications,they also allow one to monitor changes in the relative proteinsynthesis rates and thus give a sensitive read-out on adaptationin progress. The set of proteins that is newly synthesized by anorganism can change dramatically in response to modificationsin growth conditions or environmental-stress factors. When con-fronted with a new situation, the cell dedicates a large proportionof its translation capacity to the de novo synthesis of proteinsneeded at higher levels to adequately meet the challenges posedupon it. Pulse-labeling of the proteins with 35S-[L]-methionine isa very sensitive method to visualize by autoradiography speci-fically the newly synthesized protein fraction. Short labeling

times allow one to capture snapshots of protein synthesis at anytime point during adjustment to the new condition and in the newsteady state. Dual-channel imaging, first described by Bernhardtet al. (1999), was developed to facilitate the comparison ofde novo protein synthesis detected on autoradiographs andprotein amounts detected by silver staining. In the originalprotocol, the 2D gels of the 35S-labeled protein extracts weresilver stained, dried, and exposed to phospho screens for auto-radiography. The false color green was assigned to the proteinspots on the silver image by means of a photo editor, and the falsecolor red to the spots on the autoradiograph. When both falsecolor images were overlayed, proteins that were newly syn-thesized during the pulse, but had not accumulated to amountsdetectable by silver staining, appeared in red. On the other hand,proteins that had already been present prior to the pulse but wereno longer synthesized appeared in green. Similar expressionlevels under both conditions resulted in a yellow color. Today,more sophisticated software packages are available that containwarping tools to overlay also independent 2D gels, and thatprovide a variety of color schemes from which to chose (Delta2Dsoftware, DECODON GmbH, Greifswald/Germany; Z3 andZ4000 software, Compugen Ltd., Tel Aviv, Israel).

Dual-channel imaging was applied, for instance, to theidentification of new stimulons. Proteins induced in response tothe stimulus could be conveniently detected by their red color,whereas repressed proteins were colored in green.

An impressive example of the utility of the dual-channelimaging technique was published recently (Bernhardt et al.,2003). Global changes in protein expression occurred in B.subtilis grown in synthetic medium, when, after a period of ex-ponential growth, the primary carbon source, glucose, wasexhausted. A transition phase was followed by a phase of glucosestarvation and when, eventually, glucose was added to thestarving culture, exponential growth resumed. The snapshots ofprotein synthesis taken at different time points during exponen-tial growth and adaptation to starvation vividly show the changesin cellular resource allocation. At all time-points, the rates ofde novo protein synthesis were compared to the total proteinamounts as visualized by silver staining. During the transitionfrom exponential growth to glucose starvation, the proteinexpression pattern changed dramatically: about 150 proteins notsynthesized during exponential growth were induced, and thesynthesis of nearly 400 proteins ceased. Most of the 150 inducedproteins belonged either to regulons induced specifically byglucose starvation, or to more general forms of stress responseinduced in response to various stimuli. The glucose-starvationspecific proteins indicated a drop in glycolysis, or were involvedin the utilization of alternative carbon sources and gluconeogen-esis. The general stress and starvation proteins belonged to thesB-dependent general stress regulon, the stringent responsestimulon, and the sporulation cascade.

D. Protein Modifications

A great advantage of 2D gel-based proteomics as opposed to, forexample, the newly developed ICAT technology is the detectionof protein modifications that cause a polypeptide to migrate to adifferent pI/Mr location on the 2D gel. Because such modifica-


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tions are often linked to protein function or protein activity, thatinformation is crucial for understanding of the physiological stateof a cell. For example, the alternative sigma factor sB inB. subtilis governs a large regulon that comprises more than 150‘‘general stress proteins’’ that are induced under a great numberof different stress conditions. sB activity is regulated by a com-plicated signaling cascade (Fig. 3), and is controlled eventuallyby the phosphorylation state of the anti-anti-sigma factor RsbV(for a review onsB regulation inB. subtilis, see Hecker & Volker,2001). RsbV in its phosphorylated state has a reduced affinity forthe anti-sigma factor RsbW, which is in turn free to capture sB ina stable complex, thereby preventing the transcription of thegenes of the sB-regulon. In contrast, dephosphorylated RsbVbinds RsbW and thus releases sB. The active alternative sigmafactor competes with the housekeeping sigma factor sA for thepolymerase core enzyme and induces transcription of the sB-dependent genes. The phosphorylation state of the anti-anti-sigma factor RsbV is carefully regulated, and involves theactivity of the two phosphatases, RsbU and RsbP. RsbP sensesthe energy status of the cell, and dephosphorylates RsbV uponglucose and phosphate starvation, whereas RsbU takes over thisfunction after exposure to heat, acid, or ethanol. Not only was 2Dgel-based proteomics instrumental in the identification of themembers of the sB regulon, it also allowed the monitoring ofthe phosphorylation state of the anti-anti-sigma factor RsbV,which appears on 2D gels in two distinct isoforms—one thephosphorylated and the other the dephosphorylated protein.

Given the importance of the sB-response in B. subtilis forgeneral stress adaptation, it was somewhat unexpected that, in arecent proteomics study where B. subtilis was exposed tosublethal concentrations of 30 antibiotics from various com-pound classes, only rifampicin induced the sB-response in thatorganism (Bandow, Brotz, & Hecker, 2002; Bandow et al.,2003a). Even then, the general stress response was not inducedimmediately after exposure to the antibiotic, but occurred with adelay of about 1 hr during a drug-mediated growth arrest. Amutant, in which the sigB gene was deleted, responded to

rifampicin treatment with a considerably prolonged growth arrestcompared to the wild-type, and it was, therefore, postulated thatthe sB response helped B. subtilis to overcome the growth arrest.To investigate which of the above-mentioned phosphatases isinvolved in sB activation during rifampicin treatment, 35Smethionine pulse-labeling and 2D-PAGE were repeated withrsbU and rsbP insertion mutants. (Bandow, Brotz, & Hecker,2002). After rifampicin treatment, the active, dephosphorylatedform of RsbV was induced in the wild-type and the rsbU mutant,but not in the rsbP mutant. This result indicates that, duringrifampicin exposure, the energy-signaling pathway via RsbP isresponsible for RsbV dephosphorylation and consequently sB

activation.A further example of protein modifications in bacteria stems

from the analysis of protein-expression profiles of a conditionaldeformylase mutant (Bandow et al., 2003b). In bacterial proteinbiosynthesis, formyl-methionine is always incorporated intonascent proteins as the first amino acid. Peptide deformylase isneeded afterwards to remove that N-terminal formyl residuesfrom the polypeptide chains; that function is essential for bac-terial survival. With respect to antibacterial drug discovery, thedeformylase gained recent interest as a novel target, and the firstclass of inhibitors has now reached phase I of clinical devel-opment (Johnson et al., 2003). B. subtilis encodes two functionalpeptide deformylases, Def and YkrB. The latter represents themajor deformylase in this organism, although both enyzmes canat least partly substitute for each other, because single deletionmutants in both genes remain viable (Haas et al., 2001). A defdeletion mutant in which the ykrB gene was placed under thecontrol of a xylose promoter was constructed and was analyzedby 2D gel-electrophoresis (Bandow et al., 2003b). As long asxylose was present in the growth medium, ykrB was transcribedand the protein pattern of the mutant matched that of the isogenicwild-type. When xylose was depleted and glucose was added tothe medium for efficient repression of the xylose promoter, theprotein expression pattern of the mutant changed dramatically. Anew protein spot accumulated next to almost every protein spotthat had been present under non-repressing conditions. Thenewly accumulating proteins were more acidic than theircounterparts, and were shown by ESI-Q-TOF-MS to still carrythe N-terminally formylated start methionine, which undercontrol conditions is usually removed from a large percentageof the proteins. The same shift of protein spots to a more acidicposition was observed after treatment with the antibiotic acti-nonin, which acts as a deformylase inhibitor (Bandow et al.,2003b).

III. PROTEOMICS AND THE ANTIBACTERIALDRUG DISCOVERY PROCESS

So far, we have discussed in this review the sometimes asto-nishing capacity of bacterial cells to adapt to environmental stressconditions, including antibiotic exposure, and also the utility ofproteomic techniques to elucidate those adaptive responses. Inthe examples mentioned above, antibiotics were employed as akind of tool to modulate the bacterial metabolism by directedinhibition of essential cellular functions. Treating a bacteriumwith an antibiotic from an established class with well-understood

FIGURE 3. Simplified scheme of the sB activation cascade: Different

environmental signals stimulate the phosphatase RsbP and RsbU to

desphosphorylate the anti-anti-sigma factor RsbV. Dephosphorylated

RsbV sequesters the anti sigma factor RsbW, thereby releasing sB for its

interaction with DNA polymerase. For a detailed review on further

regulatory elements in the process, refer, for example, to Hecker &

Volker (2001).


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mechanism of action provides valuable insights into the physio-logical consequence of an impaired metabolic function orpathway. However, with the search for novel antibacterial agentsin mind, more direct applications of proteomics in antibacterialdrug discovery can also be envisaged. We will discuss this topic inmore detail in ‘‘The Potential Roles of Proteomics in Anti-bacterial Drug Discovery’’ of the following chapter. Prior to that,we will outline in the next paragraphs some approaches andprocesses in antibacterial drug discovery to give an impression ofthe underlying aims and obstacles.

In 1972, the US Surgeon General made the often-citedstatement ‘‘The book of infectious diseases can now be ulti-mately closed.’’ The rational for this—as we know today—clearly wrong statement was the enormous success in combatinginfectious diseases due to improved hygiene measures and thecausal treatment of many bacterial pathogens by antibiotics. Therole of that development can hardly be exaggerated with respectto the increase in life expectancy and the avoidance of seriouscomplications of bacterial infections in well-developed coun-tries. The success story first started with Gerhard Domagk’sdiscovery of the sulphonamides (introduced in 1936) and wasfollowed by the b-lactams (1940), the tetracyclines (1949),chloramphenicol (1949), aminoglycosides (1950), macrolides(1952), glycopeptides (1958), streptogramins (1962), andquinolones (1962). Although extreme progress was made in thechemical modification of those antibiotic classes, which led tomuch improved new subclasses, almost 40 years passed until thenext truly new class, the oxazolidinones, was introduced into themarket in 1999 (Strahilevitz & Rubinstein, 2002). Given theextraordinary adaptability of bacteria, it should come as nosurprise that many of the formerly effective antibiotics have to acertain degree lost their ability to kill previously susceptiblepathogens. Under antibiotic pressure, bacteria have developedvarious protective mechanisms such as additional barriers forantibiotic penetration, active pump systems to extrude the drugfrom intracellular compartment, enzymatic modification of thedrug to render it ineffective, and mutation of the molecular targetsto prevent successful interaction between target and drug (forreview, see e.g., Walsh, 2003).

As a consequence, there is an urgent need for novelantibacterial compounds that are devoid of cross-resistance tothe antibiotics already in use. This need can be addressed by (1)structural modification of an existing antimicrobial compoundclass such that it is no longer prone to the inactivation mechanism(e.g., a b-lactam stable against b-lactamases), (2) a combinationof an antibiotic and a compound that inhibits the resistancemechanism (e.g., b-lactamase inhibitors (Bush, 2002), already aclinically proven concept, or efflux pump inhibitors (Lomovs-kaya & Watkins, 2001), or (3) most preferentially, a new com-pound class that would act on a target site that has not yet beenexploited by any existing approaches. Several studies clearlyshow that only a subset of the essential genes and cellularfunctions (¼essential targets) of bacteria is hit by today’santibiotics (Fig. 4); thus, in principle, there should be ampleopportunity to find such novel antibacterial drugs. In order tounderstand the potential role of bacterial proteomics in thatprocess, the present approaches of antibacterial drug discoveryare outlined below in somewhat more detail.

A. Current Approaches in AntibacterialDrug Discovery

With respect to the strategies pursued in the search for novelantibiotics, we will restrict ourselves here primarily to the dis-cussion of therapeutic (as opposed to prophylactic) approachesthat aim at hitting bacterial pathogens by interfering with theiressential prokaryotic genes and functions. Antibacterial agentsderived from such a strategy will act in a somewhat classical wayby inhibiting bacterial growth even under standardized cultureconditions. It should be mentioned that several other approachesare also under investigation, such as targeting genes that are notessential for bacterial survival per se, but indispensable underinfection conditions such as virulence and pathogenicity factorsimportant for infection initiation, disease progression, or persis-tence, as well as strategies that try to exploit eukaryotic defensemechanisms to control infections (Alksne, 2002; Suga & Smith,2003; Weidenmaier, Kristian, & Peschel, 2003).

It is important to note that all antibiotics in clinical use orin any phase of current clinical development stem from thetraditional approach of measuring their inhibitory activity onbacterial growth in vitro. Only after their antibacterial potentialwas discovered did a detailed evaluation follow to assess all of theother properties that are required by a clinically useful drug (e.g.,efficacy in animal infection models, pharmacokinetic properties,and toxicological profile). Accordingly, without exception theirmolecular targets and mechanisms of action were determinedmuch later than their original discovery, and an in-depth under-standing of the molecular basis of their activity often took thework of several laboratories over a considerable number of years.The situation has changed dramatically due to the advent oftechnologies that operate on the scale of complete microbialgenomes. Today’s approaches for the discovery of novel anti-biotic classes can be categorized as being either directed against aspecific molecular target or based on reverse genomics (Fig. 5). Inthe former case, a certain molecular target is carefully selected onthe basis of a theoretical and experimental rational, and com-pound libraries are screened specifically for inhibitors of itsfunction. In the latter process, antibacterial compounds areselected somewhat more classically by their promising inhibitionof bacterial growth, but are examined immediately with respect

FIGURE 4. Targets of antibiotics in clinical application. Only a limited

number of cellular processes/metabolic reactions are, so far, targeted by

marketed antibiotics. Most compounds are derived from natural

products, and only a few stem from purely synthetic approaches (marked

by italics). p-AB, para-aminobenzoic acid; DHF, dihydrofolate; THF,

tetrahydrofolate.


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to their selective activity against a spectrum of (often predefined)molecular targets or processes. Obviously, a list of desiredmolecular targets for antibiotic attack is a pre-requisite for bothapproaches, and target selection and validation are of paramountimportance. Initial target selection can be based on a variety ofconsiderations, including its proven occurrence and essentialityin the desired spectrum of bacterial species, selectivity formicrobial versus eukaryotic counterparts, amenability for scre-ening, and percentage of reduction of target function needed toprevent bacterial growth. In addition, more difficult to generalizecriteria play a role, such as technical and scientific experiencein certain target areas, presumed drugability of the target, oravailability of further information like, for example, availabilityof the 3D structure of the target (Abergel et al., 2003).

In the following, we will discuss to what extent proteomicscan be helpful in target selection, identification, and validation,and we will illustrate this process with some of the still fewexamples reported in the literature.

B. The Potential Roles of Proteomics inAntibacterial Drug Discovery

As outlined in ‘‘The Role of Proteomics to Decipher the BacterialResponse Towards Changes in Environmental Conditions and

Antibiotic Attack,’’ proteomic studies have been successfullyapplied to study bacterial adaptation to various stress situations,including antibiotic drug action. In fact, one would expect anyantibacterial agent to induce a certain response in the bacterialproteome that reflects its effects on the microbial physiology, atleast as long as the drug concentration is low enough to notinstantly kill and lyse the cells. Thus, the application of pro-teomics to the antibiotic-discovery process, technically spoken,requires the same methodological approaches as those appliedto study the physiological response to environmental stresses(outlined above). Nevertheless, there are many potential ques-tions to be asked that are specific for drug-discovery applications.

Antibiotics exert their antibacterial activity via binding toand inhibition of certain molecular targets, thereby usuallyblocking a function essential for microbial survival. Therefore,one application of proteomics in drug discovery, that is easy toimagine, is the identification of novel antibacterial targets. Innon-infectious diseases proteomic-based target identificationapproaches rely on the analyses of healthy versus diseased humanor mammalian tissue to identify differentially expressed proteinsas valid starting points for a detailed investigation of theirdisease-related role and their suitability as potential targets fortherapeutic intervention (Yoshida, Loo, & Lepleya, 2001; Graves& Haystead, 2002). One might also expect that proteins

FIGURE 5. Antibacterial drug-discovery process. Current strategies for the discovery of novel

antibacterial agents can be grouped into two major categories. The target-based approach starts with the

selection of a suitable target, followed by the development of an assay to search specifically for inhibitors of

its function. In contrast, in the ‘‘reverse-genomics’’ approach, a compound is selected for its promising

antibacterial activity, and the target is determined in a second step. Later in the hit-and-lead profiling

cascade, both strategies follow the same procedure.


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differently expressed in the bacterium after antibiotic attackcould serve as novel targets to either enhance the activity of thedrug under study, for example, in a combination therapy or forindependent attack, if the novel target proves to be suitable forthat purpose. Although this approach has been theoreticallyconsidered in several publications (e.g., Allsop, 1998; Schmid,2001; Tang & Moxon, 2001), we are not aware of any publisheddemonstration in the area of classical, broad-spectrum antibioticresearch, probably because knowledge about essential targetsand target selection in the antibacterial area is well-advanced andnot so much a bottleneck as in other therapeutic areas (see e.g.,Payne et al., 2000). However, examples for the exploitation ofprotein expression data in target finding exist for preventiveapproaches such as vaccination as well as for narrow-spectrumorganism-specific therapeutic strategies (H. pylori, M. tubercu-losis, P. aerigunosa) that aim either at essential or virulence-associated targets (see e.g., Glass, Belanger, & Robertson, 2002;Kornilovska et al., 2002; Mollenkopf et al., 2002; Zhang &Amzel, 2002; Guina et al., 2003a,b; Lee, Almqvist, & Hultgren,2003; Mathesius et al., 2003).

Most of the few available studies, in which protemics wasperformed with clear emphasis on antibacterial drug discovery(sometimes in combination with transcriptional profiling), focuson either target validation or mode of action studies, includingthose studies that aim at a better molecular understanding of themechanisms of action of existing drugs (Gray & Keck, 1999;Apfel et al., 2001; Evers et al., 2001; Gmuender et al., 2001;Singh, Jayaswal, & Wilkinson, 2001; Bandow et al., 2003a,b; Nget al., 2003). Although those studies differ in important details,the general procedure of all of them is similar. The proteome ofbacteria grown in vitro under standardized conditions in thepresence and absence of the antibiotic of interest is analyzed withrespect to changes in the protein-expression pattern. Dataanalysis in most cases concentrates on listing the proteins withsignificantly altered expression levels, which are subsequentlydiscussed with respect to the current knowledge of the anti-biotic’s mode of action. If several antibiotics with known activityin a certain metabolic pathway are investigated (e.g., antibioticssuch asb-lactams, glycopeptides, D-cycloserine, and fosfomycin,which all act at different stages of bacterial cell wall synthesis(Singh, Jayaswal, & Wilkinson, 2001), or compounds such asquinolones and novobiocin, that inhibit DNA gyrase although byquite distinct molecular mechanisms (Gmuender et al., 2001)),then the data can be exploited to define a pathway-specificstimulon or a proteomic signature that is indicative of theinhibition of a specific target, which might prove useful later inidentifying and characterizing novel antibiotics that act withinthat pathway. In addition, protein-expression profiles for com-pounds synthesized within a lead-optimization program can beused to investigate whether the modified compounds still actagainst the intended target, or whether they have lost theirspecific mode of action during chemical derivatization. Anotherapplication for proteomic studies within the drug-discoveryprocess is the verification that a compound, which inhibits theactivity of a desired isolated protein in a biochemical target assay,acts indeed as expected when tested against whole bacterial cells,and does not kill the cell due to other, not target-related, possiblyundesired and non-specific activities such as general membraneperturbation or intercalation into nucleic acids. Thus, mode of

action determinations as well as validations are important andexpected outcomes from such studies. Most publications citedabove can be categorized as proof of principle studies limited to acertain subclass of antibacterial compounds. A broader exploita-tion of such proteomic mode of action analyses for the anti-bacterial drug-discovery process requires a large compilation ofprotein-expression profiles for as many different compoundclasses with known or suspected modes of action as possible, to,ideally, represent all of the potential responses that bacteria arecapable of inducing under various types of antibiotic attack. Inorder to allow a direct comparison between the proteomicsignatures obtained for different antibiotics, it is important thathighly reproducible experimental conditions are applied duringbacterial growth and antibiotic treatment, as well as during 2D-PAGE and data analysis. It is straightforward to build up such adata set for one selected bacterial species as a model organism,because all parameters apart from the various antibacterial agentsunder investigation are kept constant and, thus, a relatively largenumber of compounds may be analyzed by a limited number ofgels. However, in a second step it is also desirable to obtaininformation on the responses of additional bacterial species tocomplete the picture. Furthermore, because there are moleculartargets for which there is no inhibitory compound available,conditional mutants in such targets should, ideally, also beincluded. Finally, because many compounds that are activeagainst bacteria act by mechanisms too non-specific to beexploited for antibacterial drug-discovery purposes (e.g., DNAalkylation or intercalation, detergent-like membrane damage,etc.), a comprehensive database should also include proteomedata that are characteristic for such undesired activities for anearly rejection of such compounds. The value of a database of thatscope for major strategies, target-based drug discovery as well asreverse genomics methodologies, can hardly be overestimatedand would nicely complement similar approaches that usealternative methods such as genome-wide mRNA-expressionprofiling (Shaw & Morrow, 2003; Shaw et al., 2003; Fischer et al.,2004) and rapid phenotypic approaches such as conventionalradioactive precursor incorporation techniques (limited to somerough pathway identification scope) or whole-cell FT-IRspectroscopy (Gale et al., 1981, Naumann & Labischinski,1990; Kaderbhai et al., 2003). Although such a comprehensivedatabase is not yet available, a recent publication shows that arealization is within reach. In the study from Bandow et al.(2003a), some 30 antibiotics have been analyzed by proteomicsunder uniform conditions, comprising examples for almost allknown marketed antibacterial compound classes, several experi-mental drugs with novel mechanisms that rank high on currentpriority lists of pharmaceutical companies, and examples ofdrugs with undesired modes of action. The model organismchosen for that study wasB. subtilis, the workhorse for molecularbiology studies in the Gram-positive arena. Whereas at a firstglance that choice of a non-pathogenic species appears somewhatillogical for drug-discovery purposes, it was based on the fact thatmost major pharmaceutical companies for medical and econom-ical reasons search for broad-spectrum antibiotics, targeting atleast all of the most frequently isolated Gram-positive pathogensas staphylococci, enterococci, and streptococci and, therefore,restrict their research to targets common to all of these bacteria.Genomic comparisons have shown that such targets are present


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also inB. subtilis almost without exception. Of course, that meansvice versa that projects that aim at small spectrum drugs (e.g.,targeting staphylococci only) must rely on other adequateorganisms, even though experimental difficulties will behigher, because molecular-biology tools and organism-specificdatabases, etc. will not be as easily available or as rich ininformation.

For each antibacterial agent investigated in that study,samples of a B. subtilis culture were collected for 2D-PAGE afterexposure to compounds at two different concentrations at one ormore different time-points after addition of the antibiotic. Theproteins that were newly synthesized in response to antibiotictreatment were visualized by pulse-labeling with 35S-methioninefollowed by autoradiography, and compared to the proteins thatwere newly synthesized by an untreated control culture duringthe same labeling period. The two autoradiographs of the un-treated and antibiotic-treated sample were superimposed andanalyzed by the red-green dual-channel imaging technologydescribed in ‘‘Snapshots of Protein Biosynthesis: MetabolicLabeling and Dual-Channel Imaging.’’ Several interestingobservations could be obtained by that approach: first and quiteimportantly, it turned out that the differential-expression patternsobtained, although never completely predictable, were in generalconsistent with the respective mode of action of the antibiotic asfar as known. For example, protein-synthesis inhibitors clearlyled to a reduction in overall translation, as expected. However,looking at the different protein-synthesis inhibitors in moredetail, the proteomic signatures of those antibiotics, which‘‘simply’’ reduce the rate of protein-synthesis, such as forexample, chloramphenicol or tetracycline, were quite distinctfrom the signatures of compounds such as aminoglycosides andpuromycin, that led to the production of mistranslated ortruncated proteins. In addition, both of these groups could bedistinguished from mupirocin, which interferes with proteinsynthesis via inhibition of isoleucine-t-RNA synthetase (Ile-RS)and which shows a protein-expression profile that is character-ized by the induction of the classical stringent response (Eymannet al., 2002; Bandow et al., 2003a). Second, it was obvious, thatirrespective of the overall consistency of the protein-expressiondata with the known mode of action of a given antibiotic, therewere always some proteins with a rather unexpected induction,indicating that our present knowledge about the detailed mech-anisms of antibiotic action and the cellular response to antibioticsis still limited. A special example was provided by the analysis ofnitrofurantoin, an antibiotic introduced in the 1950s and still usedfrequently in the therapy of urinary tract infections. In earlierstudies, its activity was attributed to such distinct target areas asDNA and/or RNA synthesis, carbohydrate metabolism, or aninhibition of other metabolic enzymes (Guay, 2001). The protein-expression profile of nitrofurantoin showed a remarkablesimilarity to that of diamide (Bandow et al., 2003a), an agentthat causes oxidative damage by inducing non-native disulfidebonds (Kosower & Kosower, 1995). That result led the authors topropose that protein inhibition due to non-native disulfideformation may be the primary antibacterial mode of action ofnitrofurantoin; that proposal would explain nicely the pleiotropiceffects reported earlier and is also compatible with studies thatattributed its toxic side-effects on eukaryotic cells to the rapidformation of, for example, glutathione disulfides, glutathione-

protein disulfides, and protein–protein disulfides (Hoener et al.,1989; Silva, Khan, & O’Brien, 1993).

Third, and most relevant for the use of proteomics data fordrug-discovery purposes, it was demonstrated that crucial hintson the molecular mechanisms of novel antibacterial compoundscan be obtained when the new mechanism is similar to that of areference antibiotic already included in the protein-expressiondatabase. One example provided in the study of Bandow et al.(2003a) was the novel pyridiminone antibiotic BAY 50-2369,which is structurally related to the natural compound TAN 1057A/B (Brands et al., 2003). Even by mere visual inspection, theproteomic pattern was almost identical to that of chloramphe-nicol and other peptidyltransferase inhibitors, leading to thesuggestion that BAY 50-2369 as well as TAN 1057 inhibited thesame target, although in a slightly distinct manner because nocross-resistance to other peptidyltransferase inhibitors wasobserved (Limburg et al., 2004). That interpretation has beenproven correct in the meantime by direct mechanistic studies(Boeddecker et al., 2002; Limburg et al., 2004). A secondrecently published example (Beyer et al., 2004) proved themode of action of a novel class of phenyl-thiazolylurea-sulfonamides as phenylalanyl-t-RNA-synthetase (Phe-RS) inhi-bitors by demonstrating that the proteomic signature was verysimilar to that of mupirocin. In fact, proteins that belong to thestringent response were similarly overexpressed after exposureto both antibiotics. Interestingly, both antibiotics led to aninduction of their respective direct targets: the alpha-subunit ofPhe-RS was induced in cells treated with the novel compoundclass, whereas mupirocin induced the corresponding Ile-RSsubunit (Fig. 6).

It should be noted that those conclusions could have beenreached by a mere visual comparison of the respective dual-channel images of the 2-D gels, an identification of the differ-entially expressed protein spots by peptide mass fingerprinting,and a comparison with the well-annotated master gels. However,because visual observation is always influenced by the personalimpression of the respective observer, Bandow et al. (2003a)applied a marker-protein-based concept, which allows one todraw conclusions that are independent of the researcher whoanalyzed the data. In addition, as the reference database ofproteomic signatures grows, a direct side-by-side evaluation ofthe gels becomes a very tedious task, and the marker-proteinapproach substantially reduces the evaluation efforts. In short,marker proteins were defined as such proteins that wereoverexpressed at least twofold under antibiotic influence in twoindependent experiments, and that made up at least 0.05% of thetotal protein synthesized during the pulse-labeling period. Thenumber of such marker proteins for each of the 30 antibioticsvaried between 0 and 34 with an average of 13.3, and can be usedin, for example, cluster analyses to obtain a first hint to a potentialmode of action.

In spite of the progress reached and documented in thatstudy, it should still be mentioned that the general applicability ofthe method for drug-discovery purposes in routine fashion islimited (i) by the time and effort needed to study a novelcompound by the experimentally still demanding 2D gels and theevaluation of the massive data sets obtained, and, (ii) due to thenumber of conditions/compounds/mutants studied so far. Both ofthose bottlenecks will continue to benefit greatly from further


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technological advances in 2D gel-based and non-gel-basedtechnologies, which will be discussed briefly in the followingsection.

IV. REMARKS ON TECHNOLOGICAL PROGRESS

A. Progress in Two-Dimensional Gel-BasedTechnologies

The introduction of 2D-PAGE in 1975 (Klose, 1975; O’Farrell,1975) marked a major breakthrough in the analysis of thecomplex protein mixture of whole cells and tissues. Resolutionand sensitivity were already high in those original studies, wherepolyacrylamide tube gels with Ampholines were employed forprotein separation according to pI. After isoelectric focusing,proteins were reduced and alkylated in an equilibration stepbefore separation on an SDS gel according to Mr, which is still thestandard procedure to this day. Autoradiographs of dried 2D gelsof E. coli crude cell extracts allowed the detection of about 1,100protein species. For direct detection on the gel, proteins werestained with Coomassie Blue. Since its introduction, 2D gel-based proteomics has come a long way. Major technologicaldevelopments were aimed at enhancing the reproducibility of theseparation, increasing sensitivity and resolution, and addressingproteins with physico-chemical properties unfavorable for 2D-PAGE. However, only when identification of the proteins fromthe 2D gel became easier, faster, and cheaper, did the popularityof proteomics begin to increase rapidly.

The introduction of immobilized pH gradient (IPG) stripsfor use in the first dimension (Gorg, Postel, & Gunther, 1988)certainly enhanced reproducibility. Different silver-stainingprotocols (Switzer, Merril, & Shifrin, 1979; De Moreno, Smith,& Smith, 1985; Rabilloud, 1999) offer high sensitivity but do nothave a broad linear dynamic range that would allow reliableprotein quantification. Fluorescent dyes such as Sypro Ruby(Berggren et al., 1999; Steinberg et al., 1999) are at least assensitive as silver, are more sensitive than colloidal CoomassieBrilliant Blue staining methods, and offer a linear dynamic rangeof three orders of magnitude (Patton, 2000). The 2-DimensionalFluororescence Difference Gel-Electrophoresis (DIGE) technol-ogy from Amersham Biosciences (Piscataway, NJ) relies oncovalently labeling a small fraction (about 1%) of the proteins inthe sample with Cy2, Cy3, or Cy5 dye. Because these dyes havedistinct excision and emission wavelengths, up to three samplescan be labeled, mixed prior to IEF, and separated in a single 2Dgel to further enhance cross-sample comparison by decreasingany gel-to-gel variation. In addition, narrow pI gradient IPGstrips were successfully employed to increase protein resolution(Cordwell et al., 2000; Corthals et al., 2000; Gorg et al., 2000).However, although some progress has been made in theseparation of basic and poorly soluble proteins such as membraneproteins (Gorg et al., 1999; Molloy et al., 2001; Ohlmeier, Scharf,& Hecker, 2000), those protein species as well as extremely smalland large proteins (<15 and >120 kDa) still pose majorchallenges to the 2D gel technology. Much progress has alsobeen achieved with respect to throughput and automation. Thenewly launched ZOOM IPGRunner system from Invitrogen

FIGURE 6. Cytoplasmic protein-expression profile of B. subtilis after treatment with a phenyl-

thiazolylurea-sulfonamide (PTU). The autoradiograph of the antibiotic-treated sample (red) was warped

by the dual-channel imaging technique onto the untreated control (green). Proteins induced during antibiotic

exposure appear in red, and repressed proteins appear in green. The proteomic signature of the PTU included

the induction of many proteins previously identified during norvaline (Eymann et al., 2002) or mupirocin

(Bandow et al., 2003a) treatment of B. subtilis; for example, Ald, MinD, Spo0A, SpoVG, YurP, and YvyD.

Those proteins are known to be positively controlled by the stringent response in this organism (Eymann

et al., 2002). However, there were also differences in the protein-expression profiles of the two antibiotics.

The direct target, thea-subunit of Phe-RS (PheS), was induced in phenyl-thiazolylurea-sulfonamide-treated

cells, whereas mupirocin, as expected, did not induce PheS, but rather the corresponding IleS and additional

proteins of isoleucine/valine biosynthesis (Bandow et al., 2003a).


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(Carlsbad, CA) allows 2D-PAGE separation in 24 hr fromrehydration of the sample into the IPG strip for the first dimensionto protein staining. Furthermore, NextGen Sciences Ltd.(Cambridgeshire, UK) recently introduced a fully automatedrobot that is capable of analyzing three 2D gels at a time underhighly reproducible conditions. Finally, although protein quanti-fication is still one of the bottlenecks in the 2D gel-basedworkflow, image analysis software packages have evolved tofacilitate the quantification of all protein spots within large sets of2D gels, requiring different levels of user interaction to ensuredata quality.

As mentioned above, it was the progress in protein identi-fication from 2D gels that made proteomics attractive to thebroader research community. Although a protein pattern-matching approach can be successful in finding similaritiesbetween protein expression profiles for a large number of testedgrowth conditions, any detailed physiological understanding ofthe changes in protein composition relies heavily on the identi-fication of the differentially expressed proteins. Protein identi-fication in the early days was time-consuming and expensive,because Coomassie stained proteins had to be sequenced byrepeated cycles of Edman degradation (Edman & Begg, 1967). Ineach cycle phenylisothiocyanate was added to the N-terminalamino acid, and the cyclic amino acid derivative was removedunder mild acidic conditions and identified by HPLC. Additionand removal reactions were repeated until the length of theanalyzed protein sequence was sufficient to allow the identifica-tion of the protein by comparison with available protein- or DNA-sequence information. A major drawback besides the lack ofsensitivity was that about one-third of the bacterial proteins areN-terminally blocked and, therefore, eluded identification by thismethod. Two factors arose in the mid-1990s that substantiallysimplified proteomic analyses. For the first time, DNA sequencesof whole bacterial genomes became available and allowed theprediction of the approximate total number of encoded openreading frames. At the same time, progress in mass spectrometryfacilitated the analysis of peptides and small proteins, and themass accuracy of the measured peptide masses was sufficientto allow peptide mass fingerprinting. Experimentally obtainedpeptide masses of a digested protein spot were compared to adatabase that contained all theoretical peptide masses derivedfrom an in silico digestion of the proteins predicted from thegenome sequence (Henzel et al., 1993). In addition, recentautomation of protein identification significantly increasedthroughput (for review, see Godovac-Zimmermann & Brown,2001; Mann, Hendrickson, & Pandey, 2001; Yarmush &Jayaraman, 2002). Robots have been developed that exciseprotein spots from 2D gels and transfer the gel plugs intomicrotiter plates. A digest-robot performs the in-gel trypticdigests directly in the microtiter plate, and a spotting robotapplies peptide samples to MALDI targets. MALDI-TOF massspectrometers acquire the data, and software packages areavailable to automatically extract peptide masses from thederived spectra, which are submitted to the database search.Ideally, the scientist is left only to do a quick quality check toensure that the hits from the database match the predominantpeaks on the spectra. The same level of automation is alsoavailable for mass spectrometric approaches that involve MS/MSpeptide sequence elucidation. Although MS/MS is particularly

useful when working with highly complex genomes, wherepeptide mass fingerprinting is not reliable enough, it is also themethod of choice for the identification of those bacterial proteins(especially small ones) that do not yield a sufficient number ofpeptides after tryptic digestion to ensure an unambiguous identi-fication by peptide mass fingerprinting. In addition, MS/MSde novo sequencing of peptides is extremely useful whenworking with organisms with incomplete genomic information.Another important application of mass spectrometry-basedsequencing is the identification of amino acid residues that carryprotein modifications (e.g., phosphorylation), which are oftencrucial for protein activity.

B. Progress in Non-Gel-Based Proteomics

Although 2D gel-based proteomics is the method of choice formany proteomic studies, there are certain limitations to thattechnique that are mainly based on the wide diversity of physico-chemical properties of proteins. It is still difficult to achieve theseparation of hydrophobic, or of extremely small or large,proteins. Proteomics applications in the field of antibacterialresearch would greatly benefit from closing these gaps, and beingable to analyze the whole proteome of bacterial cells. Evolvingmass spectrometry-based technologies circumvent some of thelimitations of protein separation by 2D-PAGE. Protein extractsare subject to tryptic digest, and the complex peptide mixtures areseparated by liquid chromatography coupled to mass spectro-metric analysis (LC–MS). Either 1D gel electrophoresis can beused to reduce the complexity of the protein mixture prior todigestion (Lasonder et al., 2002; Li, Steen, & Gygi, 2003) or inthe case of multi-dimensional protein identification technology(MudPit) sample complexity is reduced after digestion byseparating the peptides on strong cation-exchange resins andsubsequent reversed-phase liquid chromatography (Washburn,Wolters, & Yates, 2001). Multiple approaches utilize heavy-isotope labeling for quantification; for instance, 15N-labeling(Oda et al., 1999; Lahm & Langen, 2000), stable-isotope labelingwith amino acids in cell culture (SILAC) (Ong et al., 2002),isotope-coded affinity tag (ICAT) technology (Gygi et al., 1999),or enzymatic labeling with 18O during protein digestion(Mirgorodskaya et al., 2000), to name the most prominent. Mostof those technologies are still in the proof-of-concept stage, andare currently being compared to 2D-PAGE (e.g., Schmidt et al.,2003) and to each other regarding their benefit for proteomicsstudies. The reader interested in those technologies is referred tosome excellent recent reviews (Hamdan & Righetti, 2002; Pasa-Tolic et al., 2002; Lill, 2003; Sechi & Oda, 2003; Tao &Aebersold, 2003; Wu & Yates, 2003; Ong, Foster, & Mann,2003). Those heavy-isotope labeling techniques combined withchromatography and mass spectrometry hold extreme promisefor the proteomics research community because they are capableof qualitative and quantitative analysis of protein samples with noobvious bias towards high solubility or a certain pI. However,high molecular weight proteins were over-represented in a studythat compared ICAT technology and the classical 2D-gelapproach (Schmidt et al., 2003). MS-based technologies seemto be more sensitive than the classical 2D-gel approach, and theyhave been shown to yield a good coverage of predicted open-


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reading frames (Florens et al., 2002; Washburn et al., 2002).Different shortcomings are associated with the quantitativeanalysis of peptides rather than whole proteins: the analysis of thehuge number of MS spectra obtained from a mixture of peptidesderived from two complex protein samples poses a greatchallenge to developers of analysis software, and to chromato-graphy. Furthermore, and somewhat in contrast to 2D-gel-basedproteomics, the quantification of protein modifications is ex-tremely difficult because it requires the modified peptide to bedetected and recognized as being modified. In addition, it shouldbe kept in mind that protein quantification with ICAT technologyrequires the presence of cysteine residues in the protein sequence,because it relies on labeling these cysteines with alkylatingagents of different isotope composition.

In summary, enormous technical progress has been made inthe past decade in gel-based and non-gel-based proteomicstechnologies, and further progress is still to be expected, that willcontribute greatly to the popularity and usefulness of proteomicsin the area of drug discovery.

V. OUTLOOK

The increasing resistance development of pathogenic bacteriarequires the counteractive development of new antibiotics withnovel modes of action and free of cross-resistance to presentlyapplied drugs as an important public health priority. The drugs inuse today stem with no exception from traditional approaches ofrandom screening of chemical and natural compound libraries forantibacterial activity. Whereas still in its early days, there isreason to believe that more target-directed, molecular approacheswill be instrumental in finding new antibacterial drugs and willhelp to facilitate the rational selection of compound classesstemming, for example, from the classical screening for anti-bacterial activity as well as target-based screening. The technicalbasis for this scenario was laid by the deciphering of the genomesof more than 120 bacterial strains, and on the evolvingtechnologies of gene-expression analysis, in particular transcrip-tome and proteome technologies. The latter two techniques werethemselves crucially dependent on progress in even more basicmethodologies such as chip production and MS spectrometry aswell as software tools to effectively deal with the large datasetsproduced by such approaches. There have often been debateswhether proteomics or transcriptomics should be the mostrelevant technique for drug-discovery purposes. For example,proteomics appears to be preferred by many, because proteins areoften the direct drug targets, and they also happen to be theeffector molecules that mediate and regulate the basic cellularfunctions. On the other hand, present proteomic technology stilldoes not offer to study the full genomic equivalent of all proteins,whereas transcriptome analyses cover the whole genomicsequence and are also able to produce data at a much higherpace. Nevertheless, transcript expression profiling is unable todistinguish between different gene products derived from thesame coding region on the genome (due to, e.g., modifications,truncations, splice variants). It should also be kept in mind thatnone of these technologies will deliver novel drugs on their own.As many of such technologies as possible should be applied incombination to provide a deeper biological understanding of a

compound’s action against a living microorganism. That knowl-edge will be instrumental in selecting from the many antibacterialmolecules available those drugs with a desired and promisingbiological profile, thereby reducing the target-based attritionrates in later, more costly, stages of development.

With respect to proteomics, substantial progress hasalready been made in elucidating the basic regulatory networksthat form the basis for the extraordinary capacity of bacteria toadapt to a diversity of lifestyles and external stress factors. Theapplication of this method for antibacterial drug-discoverypurposes, however, is still in its early days. One reason for thisphenomenon is the fact that the discovery of novel targets, whichis one of the most important applications of proteome studies inother areas of drug discovery, is not so much a bottleneck inantibiotic research, because the pathophysiology of mostbacterial infections is relatively well-understood and simple:killing the bacterium or interfering with its growth and, possibly,its virulence is usually all it takes. However, it has becomeobvious that proteome applications, alone or in concert withtranscriptome analysis and other more phenotypic methods, playan increasing role in target validation and mode of actiondetermination of novel compounds and variants of existingcompound classes. In particular, those methodologies are veryhelpful in reducing the time needed to obtain that information,which is important in every drug discovery project. Successfulexploitation of those technologies for the antibacterial drugdiscovery process depends on further progress in three mainareas:

1. The data collection, which should be expanded to compriseas many antibacterial compounds with diverse mechanismsof action as possible, to cover, ideally, all relevant targets.Because for novel targets, such reference antibiotics are notalways available, the analysis of conditional mutants insuch targets should be included.

2. The data analysis tools, which should be optimized ordeveloped to handle the enormous datasets efficiently andto facilitate data evaluation in terms of mechanism-specificsignatures; for example, by including clustering, chemo-metric, and artificial intelligence approaches.

3. Last but not least, further methodological progress in orderto increase the speed, throughput, and reproducibility of 2Dgel-based as well as non-gel-based techniques.

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Bacterial proteomics and its role in antibacterial drug discovery