Bacterial Proteomics

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BACTERIAL PROTEOMICS AND ITS ROLEIN ANTIBACTERIAL DRUG DISCOVERY

Heike Brotz-Oesterhelt,1

* Julia Elisabeth Bandow,2

and Harald Labischinski1

1 Bayer 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 to

study the physiological response of bacterial cells to various

environmental stress conditions. Complex protein toolkits co-

ordinated by sophisticated regulatory networks have evolved to

accommodate bacterial survival under ever-present stress

conditions such as varying temperatures, nutrient availability,

or antibiotics produced by other microorganisms that compete for habitat. In the last decades, application of man-made anti-

bacterial agents resulted in additional bacterial exposure to

antibiotic stress. Whereas the targeted use of antibiotics has

remarkably reduced human suffering from infectious diseases,

the ever-increasing emergence of bacteria that are resistant to

antibiotics has led to an urgent need for novel antibiotic

strategies. The intent of this review is to present an overview of

the major achievements of proteomic approaches to study

adaptation networks that are crucial for bacterial survival with

a 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 Spec

Rev 24:549–565, 2005

Keywords: 2D gel electrophoresis; proteomics; antibiotics;

drug discovery; bacteria

I. INTRODUCTION

The term proteome, in analogy to the term genome, was coined

to describe the complete set of proteins that an organism has

produced under a defined set of conditions (Wasinger et al.,

1995).The genomeis static because it represents theblueprintfor

all cellular properties that a cell is able to develop.In contrast, the

proteome is highly dynamic and much more complex than the

genome. It is critical for survival that the protein composition of a

cell is constantly adjusted to meet the challenges of changing

environmental conditions. Already in 1975, the powerful method

of two-dimensional-polyacrylamide gel electrophoresis (2D-

PAGE) was introduced that allowed one to separate highly

complex cellular protein extracts into individual proteins on a

single 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, has

been used from the beginning to study the bacterial proteome

under different growth conditions (Linn & Losick, 1976; Reeh,

Pedersen, & Neidhardt, 1977; Agabian & Unger, 1978) and

various external stress factors (Young & Neidhardt, 1978;

Krueger & Walker, 1984; Gomes et al., 1986).

However,it was only after 1995thata new era was openedtothe study of the dynamic behavior of the bacterial proteome by

the 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 became

possible to introduce large-scale mass spectrometry (MS) tech-

niques to identify virtually every protein detected on a 2D gel.

Theincrease in throughput, thepartial automation, andthe higher

reproducibility of 2D-PAGE analysis recently made it a very

attractive tool to study cellular functions on a molecular level.

The complete genomic sequences of more than 120 bacteria are

now publicly available (for an constantly updated list, see http://

www.tigr.org/tigr-scripts/CMR2/CMRGenomes.spl) that allow

one to select among a variety of microorganisms for proteomic

investigations according to the scientific question of interest. In

parallel, MS techniques, advanced to identify many proteins from

2D gels and from alternatives to gel electrophoresis such as the

Isotope-Coded Affinity Tag (ICAT) technology, have emerged to

overcome some of the weaknesses of the 2D-gel approach (for

recent 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 model

organisms to study regulatory networks, protein function, and

even cell differentiation, because their genomes are relatively

small and adaptation processes are less complex and involve

smaller numbers of protein components. Some bacteria are easily

genetically manipulated and are thus excellent models to studyprotein function. In addition, bacteria are commonly used in the

food industry as well as in biotechnology. In both areas, it is

desirable to understand bacterial metabolism in order to optimize

production yields and quality. Bacteria also have an even more

direct impact on human life in that a variety of species are

indispensable for aspects as immune-system maturation, nutri-

tion digestion, and vitamin production (a 70 kg human contains

approximately 1 kg of bacteria, and thus more bacterial than

human cells). On the other hand, interactions harmful to the

human host occur when bacteria overridethe defense barriers and

cause infections. In fact, infections by microorganisms cause

some 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-developed

countries, death dueto infectious diseasesis back to rank number

three even in the most developed countries such as the US

(Armstrong, Conn, & Pinner, 1999). One important reason for

that unpleasant development is the fact that bacteria that were

previously susceptible to the large armory of antibiotics have

now developed resistance against them (Hiramatsu et al., 2001;

WHO, 2001; Appelbaum, 2002; Walsh, 2003). Another reason is

ironically provided by the progress in medicine in general,

because we are becoming older and more often subject to

aggressive treatment regimens; for example, in surgery, trans-

plantation, and cancer chemotherapy. All of those manipulations

lead to a suppression of our immunological defense capabilities,

and, thereby, to more serious andmoredifficult to treat infections.

Thus, novel treatment options areurgently required, andthe need

for novel antibacterial agents without cross-resistance to existing

antibiotics as well as the development of alternative treatment

regimens should have high priority on any meaningful public

health agenda. In that environment, it is not astonishing that

proteome 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 bacterium

responds to a certain antimicrobial treatment. In addition, bene-

fits are expected in many other aspects of modern drug devel-

opmentapproaches such as theidentification of novel targetareas

and the elucidation of the molecular mechanisms of action of

novel drug candidates.

Thus, theintent of this review is to present an overview of the

major achievements in proteomic studies of adaptation networks

that are crucial for bacterial survival with a special emphasis on

stress that is induced by antibiotic treatment. A further focus

will be the review of the, so far few, published efforts to exploit

the knowledge derived from bacterial proteomic applications

directly for the antibacterial drug-discovery process.

We will also touch on some proteome studies that aim at a

more general insight into the physiological flexibility of bacteria

as well as on some methodological pre-requisites. However, the

reader interested in a full overview of the latter topics is referred

to some excellent recent reviews (Gorg et al., 2000; Nyman,

2001; Lilley, Razzaq, & Dupree, 2002; Hecker, 2003).

II. THE ROLE OF PROTEOMICS TO DECIPHER

THE BACTERIAL RESPONSE TOWARDS CHANGES

IN ENVIRONMENTAL CONDITIONS AND

ANTIBIOTIC ATTACK

The capability to grow many bacterial species in well-defined

artificial culture media hasbeen a pre-requisite for our current in-

depth understanding of bacterial physiology. Very often, those

culture media provide cockaigne-like growth conditions that

allow for a maximal and uniform logarithmic bacterial growth

behavior until some components of the medium becomeexhaust-

ed and logarithmic growth ceases. Under such optimal condi-

tions, the protein composition of the cell is usually quite constant

and tuned to support the special conditions of fast growth as, for

example, support of several DNA-replication forks within a

single cell and maximal protein biosynthesis. However, outside

the laboratory bacteria face much less supportive and highly

variable growth conditions with respect to temperature, pH,

osmolarity, nutrient availability, host interactions, etc. It should

be noted that those stress situations, often regarded as ‘‘natura-

lly’’ occurring, do not principally differ from the stresses induced

by antibiotic attack. Antibiotics are a frequent encounter for

many 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 the

term antibiotic, although it is nowadays used more broadly to

include man-made compounds as well. Even antibiotic classes

that 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 inhibit

protein synthesis (Livermore, 2003), simulate a starvation-like

situation. Also, the quinolones as topoisomerase-inhibitors

(Drlica & Zhao, 1997) cause DNA-replication errors and repair

system failures to which the bacteria react with their SOS

response (Sutton et al., 2000).The evolutionary success of bacteriawas strongly dependent

on their ability to respond to such adverse conditions via a

bewildering range of behavioral responses (Armitage et al.,

2003). A large number of external and internal signal molecules

and signal transduction processes are present in bacteria to adapt

their protein composition to the changing requirements of their

environment (Armitage et al., 2003). Several of the environ-

mental challenges are experienced by manybacterial species, and

are, therefore, met by somewhat conserved response mechan-

isms. However, it should be clear from the foregoing that the

majority of the reactions are rather species-specific, and depend

on the environmental and lifestyle preferences of the species.

Proteomics technologies appear to be the natural tools to study

the consequences of those regulatory processes on protein

composition. Key to the physiological interpretation of proteome

studies performed by 2D-PAGE, the most commonly used

technology platform, is the determination of the identity of the

proteins contained in the spotson the gel. Thus, wewill start with

some remarks on the process of proteome mapping.

A. Proteome Mapping

The large collection of fully sequenced bacterial genomes

includes those of important pathogens such as H. influenzae,

Staphylococcus aureus, Enterococcus faecium, Enterococcus

faecalis, 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 a

wealth of information that helps an organism to survive, but this

blueprint does not reveal which of the encoded molecules are

relevant under any given condition. The majority of effector

molecules in a cell that act and interact to make life possible are

proteins. Although one can predict from the genome the number

of encoding entities (open reading frames), one cannot directly

deduce the number of different proteins that an organism is

capable of generating. One needs to perform global protein

analyses to define the protein composition of a given cell under a

certain circumstance.

& BROTZ-OESTERHELT ET AL.

550



quantitative catalog of proteins made by a cell under a given

circumstance (VanBogelen et al., 1999; VanBogelen, 2003). It is

the protein-expression profile that indicates which particular

subset of proteins is present under the growth condition studied.

In spite of the progress in proteome mapping described in the

previous section, it is still not technically feasible to obtain a

complete expression profile from a single 2D gel, because not all

proteins are equally well-separated by this technique. Extremely

basic, acidic, small, or large proteins as well as those that are

poorly soluble or appear in low-abundance still pose major

challenges. Nevertheless, classical 2D gels still show a substan-

tial portion of the protein expression profile and are widely used

to study the cellular response to external stimuli.

For all proteins with an altered expression in response to

a 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 term

stimulon describes the changes in protein expression on a pheno-

typic level, and does not provide any information on the

underlying transcriptional regulation. A ‘‘regulon,’’ on the other

hand, consists of proteins that are under the control of the same

global transcriptional regulator. Even in bacteria, the least-

complex organisms, a stimulon usually consists of more than one

regulon, demonstrating the complexity of regulation required for

adaptation. In our heat-shock example, three regulons contribute

to 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 global

regulator CtsR, and (3) the general stress proteins that depend on

the alternative sigma factor sB for transcription. In addition, a

fourth class contains further heat-responsive proteins that could

not yet be assigned to anyregulon (Hecker, Schumann, & Volker,

1996; Hecker, 2003).

By studying expression levels of a multitude of proteins

under a variety of different growth conditions, specific proteins

become 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 ‘‘proteomic

signature’’ (VanBogelen et al., 1999). To identify a proteomic

signature, it is essential to recognize the connection between the

expression 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 understand

the molecular basis for their expression. Often, it is necessary to

analyze several related and unrelated conditions to propose and

verify the proteomic signature for a certain environmental factor

of interest. However, once such protein signatures are established

for a variety of different physiological states, that compilation

can be extremely helpful in the interpretation of a protein

expression profile obtained under an unprecedented growth

condition.

Some proteomic signatures published previously for E. coli

(VanBogelen & Neidhardt, 1990; VanBogelen et al., 1999) are

particularly illustrative to describe how that concept can be

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

separatedby 2D-gelelectrophoresis,usingan immobilizedpH gradientin therange ofpI 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 fromHecker, Engelmann, & Cordwell

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


552



applied to studies on bacterial physiology in the presence of

external stress factors, including antibiotic treatment. In those

studies, a clear correlation was demonstrated between the pro-

teomic signatures for growth at high and low temperature on one

hand, and the changes in protein expression profiles in response

to 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, thereare protein subsets characteristicfor growth at low

and high temperature. Some proteins seem to behave as cellular

thermometers: their amount changes gradually with increasing/

decreasing temperature. Other proteins are regulated in an off/on

fashion, and are highly induced specifically at either high or low

growth temperature. At high temperature, the folding of newly

synthesized proteins is impaired, resulting in misfolded proteins

that trigger the induction of chaperones and proteases. In con-

trast, at low temperature the proteins involved in the translation

process (ribosomal proteins and elongation factors) are induced

in addition to the specific cold-shock proteins, suggesting

that under this condition translation is the rate-limiting step for

growth of E. coli.

The ribosome is also the target of many antibiotics, that

interfere with translation via different molecular mechanisms

of action, and their effects on the proteome overlap with the

signatures for growth temperature. Aminoglycosides such as

streptomycin and kanamycin interfere with the ribosomal proof-reading activity and cause an increase in mistranslation. The

resulting 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 abortive

translation—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 also

induces an additional response that is not observed during the

shift to high growth temperature:the stringentresponsein E. coli,

which is an adaptive response to limited availability of amino

acids.The stringentresponseis triggeredby 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

theGroEL andDnaKmachinery (marked by *),whichare crucial forproteinfoldingduring heat stress. The

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

protease,andthe thirdis thesB regulon(markedbyþþ). Whereas the former tworegulons react specificallyto heat stress, the large regulon controlled by the alternative sigma factorsB is induced by various kinds of

stressand starvation stimuli. The barcharts depictthe expression levels of one representativemember 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.

PROTEOMICS AND ANTIBACTERIAL DRUG DISCOVERY &

553



and manifests in a down-regulation of many genes, including

those that encode rRNA and proteins involved in translation.

Thus, thecombination of thesignature formisfolded proteins and

that for the stringent response results in the characteristic pro-

teome expression profile for streptomycin, kanamycin, and

puromycin in E. coli (VanBogelen et al., 1999).

Another group of antibiotics impairs the efficiency of the

peptidyl transferase reaction: tetracycline, chloramphenicol,

erythromycin, fusidic acid, and spiramycin. Although they differ

in their exact binding sites and in the particular molecular

mechanisms of action, they all have one thing in common with

growth at low temperature: they slow down translation. In E. coli,

those antibiotics, as growth in the cold, lead to an induction of

cold 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 types

of proteins that are encoded and by variations in their amino acid

sequences. That adaption is achieved by differences in post-

transcriptional and post-translational regulation that mediate the

adaptationon the protein level. Therefore,proteins that constitutea proteomic signature for a specific condition in one organism do

not necessarily belong to the proteomic signature for the same

physiological state in another organism. We take the treatment

of E. coli and B. subtilis by antibiotic inhibitors of protein syn-

thesis as an example to demonstrate how protein signatures may

vary between bacterial species. When B. subtilis is treated with

kanamycin or streptomycin, chaperons and proteases are induced

asin E. coli, but in contrast to E. coli the stringent response is not

triggered by those antibiotics. Similarly, treatment of B. subtilis

with tetracycline, chloramphenicol, erythromycin, and fusidic

acid leads to an induction of proteins, forming the translation

apparatus; however in contrast to E. coli cold shock proteins are

not induced (Bandow et al., 2003a).

C. Snapshots of Protein Biosynthesis: Metabolic

Labeling and Dual-Channel Imaging

For a given cell, it is crucial for survival to quickly adjust its

protein composition and the activity of individual proteins in

order 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; protein

stability also effects protein levels (the amount of protein present

under a given condition at a certain time point), whereas post-

translational modification is often a means of regulating protein

activity. 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 protein

synthesis rates and thus give a sensitive read-out on adaptation

in progress. The set of proteins that is newly synthesized by an

organism can change dramatically in response to modifications

in growth conditions or environmental-stress factors. When con-

fronted with a new situation, the cell dedicates a large proportion

of its translation capacity to the de novo synthesis of proteins

needed at higher levels to adequately meet the challenges posed

upon it. Pulse-labeling of the proteins with 35

S-[L]-methionine is

a 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 any

time point during adjustment to the new conditionand in the new

steady state. Dual-channel imaging, first described by Bernhardt

et al. (1999), was developed to facilitate the comparison of

de novo protein synthesis detected on autoradiographs and

protein amounts detected by silver staining. In the original

protocol, the 2D gels of the 35

S-labeled protein extracts were

silver stained, dried, and exposed to phospho screens for auto-

radiography. The false color green was assigned to the protein

spotson the silver image by means of a photo editor,and the false

color red to the spots on the autoradiograph. When both false

color images were overlayed, proteins that were newly syn-

thesized during the pulse, but had not accumulated to amounts

detectable by silver staining, appeared in red. On the other hand,

proteins that had already been present prior to the pulse but were

no longer synthesized appeared in green. Similar expression

levels under both conditions resulted in a yellow color. Today,

more sophisticated software packages are available that contain

warping tools to overlay also independent 2D gels, and that

provide a variety of color schemes from which to chose (Delta2Dsoftware, DECODON GmbH, Greifswald/Germany; Z3 and

Z4000 software, Compugen Ltd., Tel Aviv, Israel).

Dual-channel imaging was applied, for instance, to the

identification of new stimulons. Proteins induced in response to

the 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-channel

imaging 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, was

exhausted. A transition phase wasfollowed by a phase of glucose

starvation and when, eventually, glucose was added to the

starving culture, exponential growth resumed. The snapshots of

protein synthesis taken at different time points during exponen-

tial growth and adaptation to starvation vividly show the changes

in cellular resource allocation. At all time-points, the rates of

de novo protein synthesis were compared to the total protein

amounts as visualized by silver staining. During the transition

from exponential growth to glucose starvation, the protein

expression pattern changed dramatically: about 150 proteins not

synthesized during exponential growth were induced, and the

synthesis of nearly 400 proteins ceased. Most of the 150 induced

proteins belonged either to regulons induced specifically by

glucose starvation, or to more general forms of stress response

induced in response to various stimuli. The glucose-starvation

specific 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 the

sB-dependent general stress regulon, the stringent response

stimulon, and the sporulation cascade.

D. Protein Modifications

A great advantage of 2D gel-based proteomics as opposed to, for

example, the newly developed ICAT technology is the detection

of protein modifications that cause a polypeptide to migrate to a

different pI/Mr location on the 2D gel. Because such modifica-


554



tions are often linked to protein function or protein activity, that

informationis crucial for understanding of the physiological state

of a cell. For example, the alternative sigma factor sBin

B. subtilis governs a large regulon that comprises more than 150

‘‘general stress proteins’’ that are induced under a great number

of different stress conditions. sB activity is regulated by a com-

plicated signaling cascade (Fig. 3), and is controlled eventually

by the phosphorylation state of the anti-anti-sigma factor RsbV

(for a review onsBregulation in B. subtilis, see Hecker& Volker,

2001). RsbVin its phosphorylated state has a reduced affinity for

the anti-sigma factor RsbW, which is in turn free to capturesB in

a stable complex, thereby preventing the transcription of the

genes of the sB-regulon. In contrast, dephosphorylated RsbV

binds RsbW and thus releases sB. The active alternative sigma

factor competes with the housekeeping sigma factor sA for the

polymerase 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 the

activity of the two phosphatases, RsbU and RsbP. RsbP senses

the energy status of the cell, and dephosphorylates RsbV uponglucose and phosphate starvation, whereas RsbU takes over this

function after exposure to heat, acid, or ethanol. Not only was 2D

gel-based proteomics instrumental in the identification of the

members of the sB regulon, it also allowed the monitoring of

the phosphorylation state of the anti-anti-sigma factor RsbV,

which appears on 2D gels in two distinct isoforms—one the

phosphorylated and the other the dephosphorylated protein.

Given the importance of the sB-response in B. subtilis for

general stress adaptation, it was somewhat unexpected that, in a

recent proteomics study where B. subtilis was exposed to

sublethal concentrations of 30 antibiotics from various com-

pound classes, only rifampicin induced the sB-response in that

organism (Bandow, Brotz, & Hecker, 2002; Bandow et al.,

2003a). Even then, the general stress response was not induced

immediately after exposure to the antibiotic, but occurred with a

delay of about 1 hr during a drug-mediated growth arrest. A

mutant, in which the sigB gene was deleted, responded to

rifampicintreatment with a considerably prolonged growth arrest

compared to the wild-type, and it was, therefore, postulated that

the sBresponse helped B. subtilis to overcome the growth arrest.

To investigate which of the above-mentioned phosphatases is

involved in sB activation during rifampicin treatment, 35S

methionine pulse-labeling and 2D-PAGE were repeated with

rsbU and rsbP insertion mutants. (Bandow, Brotz, & Hecker,

2002). After rifampicin treatment, the active, dephosphorylated

form of RsbV was induced in the wild-type and the rsbU mutant,

but not in the rsbP mutant. This result indicates that, during

rifampicin exposure, the energy-signaling pathway via RsbP is

responsible 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 conditional

deformylase mutant (Bandow et al., 2003b). In bacterial protein

biosynthesis, formyl-methionine is always incorporated into

nascent proteins as the first amino acid. Peptide deformylase is

needed afterwards to remove that N -terminal formyl residues

from the polypeptide chains; that function is essential for bac-terial survival. With respect to antibacterial drug discovery, the

deformylase gained recent interest as a novel target, and the first

class of inhibitors has now reached phase I of clinical devel-

opment (Johnson et al., 2003). B. subtilis encodes two functional

peptide deformylases, Def and YkrB. The latter represents the

major deformylase in this organism, although both enyzmes can

at least partly substitute for each other, because single deletion

mutants in both genes remain viable (Haas et al., 2001). A def

deletion mutant in which the ykrB gene was placed under the

control of a xylose promoter was constructed and was analyzed

by 2D gel-electrophoresis (Bandow et al., 2003b). As long as

xylose was present in the growth medium, ykrB was transcribed

and the protein pattern of themutant matched that of the isogenic

wild-type. When xylose was depleted and glucose was added to

the medium for efficient repression of the xylose promoter, the

protein expression pattern of the mutant changed dramatically. A

new protein spot accumulated next to almost every protein spot

that had been present under non-repressing conditions. The

newly accumulating proteins were more acidic than their

counterparts, and were shown by ESI-Q-TOF-MS to still carry

the N -terminally formylated start methionine, which under

control conditions is usually removed from a large percentage

of the proteins. The same shift of protein spots to a more acidic

position was observed after treatment with the antibiotic acti-

nonin, which acts as a deformylase inhibitor (Bandow et al.,

2003b).

III. PROTEOMICS AND THE ANTIBACTERIAL

DRUG DISCOVERY PROCESS

So far, we have discussed in this review the sometimes asto-

nishing capacity of bacterial cells to adapt to environmentalstress

conditions, including antibiotic exposure, and also the utility of

proteomic techniques to elucidate those adaptive responses. In

the examples mentioned above, antibiotics were employed as a

kind of tool to modulate the bacterial metabolism by directed

inhibition of essential cellular functions. Treating a bacterium

with 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 releasingsB forits

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 or

pathway. However, with the search for novel antibacterial agents

in mind, more direct applications of proteomics in antibacterial

drug discovery can also be envisaged. We will discuss this topic in

more 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 and

processes in antibacterial drug discovery to give an impression of

the underlying aims and obstacles.

In 1972, the US Surgeon General made the often-cited

statement ‘‘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 combating

infectious diseases due to improved hygiene measures and the

causal treatment of many bacterial pathogens by antibiotics. The

role of that development can hardly be exaggerated with respect

to the increase in life expectancy and the avoidance of serious

complications of bacterial infections in well-developed coun-

tries. The success story first started with Gerhard Domagk’sdiscovery of the sulphonamides (introduced in 1936) and was

followed by the b-lactams (1940), the tetracyclines (1949),

chloramphenicol (1949), aminoglycosides (1950), macrolides

(1952), glycopeptides (1958), streptogramins (1962), and

quinolones (1962). Although extreme progress was made in the

chemical modification of those antibiotic classes, which led to

much improved new subclasses, almost 40 years passed until the

next truly new class, the oxazolidinones, was introduced into the

market in 1999 (Strahilevitz & Rubinstein, 2002). Given the

extraordinary adaptability of bacteria, it should come as no

surprise that many of the formerly effective antibiotics have to a

certain degree lost their ability to kill previously susceptible

pathogens. Under antibiotic pressure, bacteria have developed

various protective mechanisms such as additional barriers for

antibiotic penetration, active pump systems to extrude the drug

from intracellular compartment, enzymatic modification of the

drug to renderit ineffective, andmutation of themolecular targets

to prevent successful interaction between target and drug (for

review, see e.g., Walsh, 2003).

As a consequence, there is an urgent need for novel

antibacterial compounds that are devoid of cross-resistance to

the antibiotics already in use. This need can be addressed by (1)

structural modification of an existing antimicrobial compound

class such that it is no longerprone to the inactivationmechanism

(e.g., a b-lactam stable against b-lactamases), (2) a combination

of an antibiotic and a compound that inhibits the resistance

mechanism (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 been

exploited by any existing approaches. Several studies clearly

show that only a subset of the essential genes and cellular

functions (¼essential targets) of bacteria is hit by today’s

antibiotics (Fig. 4); thus, in principle, there should be ample

opportunity to find such novel antibacterial drugs. In order to

understand the potential role of bacterial proteomics in that

process, the present approaches of antibacterial drug discovery

are outlined below in somewhat more detail.

A. Current Approaches in Antibacterial

Drug Discovery

With respect to the strategies pursued in the search for novel

antibiotics, we will restrict ourselves here primarily to the dis-

cussion of therapeutic (as opposed to prophylactic) approaches

that aim at hitting bacterial pathogens by interfering with their

essential prokaryotic genes and functions. Antibacterial agents

derived from such a strategy will act in a somewhat classical way

by inhibiting bacterial growth even under standardized culture

conditions. It should be mentioned that several other approaches

are also under investigation, such as targeting genes that are not

essential for bacterial survival per se, but indispensable under

infection conditions such as virulence and pathogenicity factors

important for infection initiation, disease progression, or persis-

tence, as well as strategies that try to exploit eukaryotic defense

mechanisms to control infections (Alksne, 2002; Suga & Smith,

2003; Weidenmaier, Kristian, & Peschel, 2003).

It is important to note that all antibiotics in clinical use or

in any phase of current clinical development stem from the

traditional approach of measuring their inhibitory activity on

bacterial growth in vitro. Only after their antibacterial potential

wasdiscovered dida detailed evaluation followto assessall of the

other properties that arerequired by a clinically useful drug (e.g.,

efficacy in animal infection models, pharmacokinetic properties,

and toxicological profile). Accordingly, without exception their

molecular targets and mechanisms of action were determined

much 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 numberof years.

The situation has changed dramatically due to the advent of

technologies that operate on the scale of complete microbial

genomes. Today’s approaches for the discovery of novel anti-

bioticclasses can be categorized as being eitherdirectedagainst a

specific moleculartarget or based on reversegenomics(Fig.5). In

theformer case,a certain molecular targetis carefully selected on

the basis of a theoretical and experimental rational, and com-

pound libraries are screened specifically for inhibitors of its

function. In the latter process, antibacterial compounds are

selected somewhat more classically by their promising inhibition

of 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 onlya few stem frompurely synthetic approaches (marked

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

tetrahydrofolate.


556



to their selective activity against a spectrum of (often predefined)

molecular targets or processes. Obviously, a list of desired

molecular targets for antibiotic attack is a pre-requisite for both

approaches, and target selection and validation are of paramount

importance. Initial target selection can be based on a variety of

considerations, including its proven occurrence and essentiality

in the desired spectrum of bacterial species, selectivity for

microbial versus eukaryotic counterparts, amenability for scre-

ening, and percentage of reduction of target function needed to

prevent bacterial growth. In addition, more difficult to generalize

criteria play a role, such as technical and scientific experience

in certain target areas, presumed drugability of the target, oravailability of further information like, for example, availability

of the 3D structure of the target (Abergel et al., 2003).

In the following, we will discuss to what extent proteomics

can be helpful in target selection, identification, and validation,

and we will illustrate this process with some of the still few

examples reported in the literature.

B. The Potential Roles of Proteomics in

Antibacterial Drug Discovery

As outlined in ‘‘The Role of Proteomics to Decipher theBacterial

Response Towards Changes in Environmental Conditions and

Antibiotic Attack,’’ proteomic studies have been successfully

applied to study bacterial adaptation to various stress situations,

including antibiotic drug action. In fact, one would expect any

antibacterial agent to induce a certain response in the bacterial

proteome that reflects its effects on the microbial physiology, at

least as long as the drug concentration is low enough to not

instantly 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 applied

to study the physiological response to environmental stresses

(outlined above). Nevertheless, there are many potential ques-

tions to be asked that arespecificfor drug-discovery applications.Antibiotics exert their antibacterial activity via binding to

and inhibition of certain molecular targets, thereby usually

blocking a function essential for microbial survival. Therefore,

one application of proteomics in drug discovery, that is easy to

imagine, is the identification of novel antibacterial targets. In

non-infectious diseases proteomic-based target identification

approaches rely on theanalyses of healthyversus diseasedhuman

or mammalian tissue to identify differentially expressed proteins

as valid starting points for a detailed investigation of their

disease-related role and their suitability as potential targets for

therapeutic 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

selectionof a suitable target, followed by thedevelopment of an assay to searchspecificallyfor 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 profilingcascade, both strategies follow the same procedure.


557



differently expressed in the bacterium after antibiotic attack

could serve as novel targets to either enhance the activity of the

drug under study, for example, in a combination therapy or for

independent attack, if the novel target proves to be suitable for

that purpose. Although this approach has been theoretically

considered in several publications (e.g., Allsop, 1998; Schmid,

2001; Tang & Moxon, 2001), we are not aware of any published

demonstration in the area of classical, broad-spectrum antibiotic

research, probably because knowledge about essential targets

and target selection in the antibacterial area is well-advanced and

not so much a bottleneck as in other therapeutic areas (see e.g.,

Payne et al., 2000). However, examples for the exploitation of

protein expression data in target finding exist for preventive

approaches such as vaccination as well as for narrow-spectrum

organism-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 was

performed with clear emphasis on antibacterial drug discovery

(sometimes in combination with transcriptional profiling), focus

on either target validation or mode of action studies, including

those studies that aim at a better molecular understanding of the

mechanisms 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; Ng

et al., 2003). Although those studies differ in important details,

the general procedure of all of them is similar. The proteome of

bacteria grown in vitro under standardized conditions in the

presence andabsence of the antibioticof interest is analyzed with

respect to changes in the protein-expression pattern. Data

analysis in most cases concentrates on listing the proteins with

significantly altered expression levels, which are subsequently

discussed with respect to the current knowledge of the anti-

biotic’s mode of action. If several antibiotics with known activity

in a certain metabolic pathway are investigated (e.g., antibiotics

such 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 as

quinolones and novobiocin, that inhibit DNA gyrase although by

quite distinct molecular mechanisms (Gmuender et al., 2001)),

then the data can be exploited to define a pathway-specific

stimulon or a proteomic signature that is indicative of the

inhibition of a specific target, which might prove useful later in

identifying and characterizing novel antibiotics that act withinthat pathway. In addition, protein-expression profiles for com-

pounds synthesized within a lead-optimization program can be

used to investigate whether the modified compounds still act

against the intended target, or whether they have lost their

specific mode of action during chemical derivatization. Another

application for proteomic studies within the drug-discovery

process is the verification that a compound, which inhibits the

activity of a desired isolated protein in a biochemical targetassay,

acts indeedas expectedwhen tested against whole bacterial cells,

and does not kill the cell due to other, not target-related, possibly

undesired and non-specific activities such as general membrane

perturbation or intercalation into nucleic acids. Thus, mode of

action determinations as well as validations are important and

expected outcomes from such studies. Most publications cited

above can be categorizedas proof of principle studies limited to a

certain 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 of

protein-expression profiles for as many different compound

classes with known or suspected modes of action as possible, to,

ideally, represent all of the potential responses that bacteria are

capable of inducing under various types of antibiotic attack. In

order to allow a direct comparison between the proteomic

signatures obtained for different antibiotics, it is important that

highly reproducible experimental conditions are applied during

bacterial growth and antibiotic treatment, as well as during 2D-

PAGE and data analysis. It is straightforward to build up such a

data set for one selected bacterial species as a model organism,

because all parameters apart from the various antibacterial agents

under investigation are kept constant and, thus, a relatively large

number of compounds may be analyzed by a limited number of

gels. However, in a second step it is also desirable to obtaininformation on the responses of additional bacterial species to

complete the picture. Furthermore, because there are molecular

targets for which there is no inhibitory compound available,

conditional mutants in such targets should, ideally, also be

included. Finally, because many compounds that are active

against bacteria act by mechanisms too non-specific to be

exploited for antibacterial drug-discovery purposes (e.g., DNA

alkylation or intercalation, detergent-like membrane damage,

etc.), a comprehensive database should also include proteome

data that are characteristic for such undesired activities for an

earlyrejection of such compounds. Thevalue of a database of that

scope for major strategies, target-based drug discovery as well as

reverse genomics methodologies, can hardly be overestimated

and would nicely complement similar approaches that use

alternative methods such as genome-wide mRNA-expression

profiling (Shaw& Morrow, 2003; Shaw et al., 2003; Fischer et al.,

2004) and rapid phenotypic approaches such as conventional

radioactive precursor incorporation techniques (limited to some

rough pathway identification scope) or whole-cell FT-IR

spectroscopy (Gale et al., 1981, Naumann & Labischinski,

1990; Kaderbhai et al., 2003). Although such a comprehensive

database is not yet available, a recent publication shows that a

realization is within reach. In the study from Bandow et al.

(2003a), some 30 antibiotics have been analyzed by proteomics

under uniform conditions, comprising examples for almost all

known marketed antibacterial compound classes, several experi-

mental drugs with novel mechanisms that rank high on currentpriority lists of pharmaceutical companies, and examples of

drugs with undesired modes of action. The model organism

chosenfor that study was B. subtilis, theworkhorse for molecular

biology studies in the Gram-positive arena. Whereas at a first

glance thatchoice of a non-pathogenic species appears somewhat

illogicalfor drug-discoverypurposes, it wasbased on thefact that

most major pharmaceutical companies for medical and econom-

ical reasons search for broad-spectrum antibiotics, targeting at

least all of the most frequently isolated Gram-positive pathogens

as 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


558



alsoin B. subtilis almost without exception.Of course, thatmeans

vice versa that projects that aim at small spectrum drugs (e.g.,

targeting staphylococci only) must rely on other adequate

organisms, even though experimental difficulties will be

higher, because molecular-biology tools and organism-specific

databases, etc. will not be as easily available or as rich in

information.

For each antibacterial agent investigated in that study,

samples of a B. subtilis culture were collected for 2D-PAGE after

exposure to compounds at two different concentrations at one or

more different time-points after addition of the antibiotic. The

proteins that were newly synthesized in response to antibiotic

treatment were visualized by pulse-labeling with35

S-methionine

followed by autoradiography, and compared to the proteins that

were newly synthesized by an untreated control culture during

the same labeling period. The two autoradiographs of the un-

treated and antibiotic-treated sample were superimposed and

analyzed by the red-green dual-channel imaging technology

described in ‘‘Snapshots of Protein Biosynthesis: Metabolic

Labeling and Dual-Channel Imaging.’’ Several interestingobservations could be obtained by that approach: first and quite

importantly, it turned out that the differential-expression patterns

obtained, although never completely predictable, were in general

consistent with the respective mode of action of the antibiotic as

far as known. For example, protein-synthesis inhibitors clearly

led to a reduction in overall translation, as expected. However,

looking at the different protein-synthesis inhibitors in more

detail, the proteomic signatures of those antibiotics, which

‘‘simply’’ reduce the rate of protein-synthesis, such as for

example, chloramphenicol or tetracycline, were quite distinct

from the signatures of compounds such as aminoglycosides and

puromycin, that led to the production of mistranslated or

truncated proteins. In addition, both of these groups could be

distinguished from mupirocin, which interferes with protein

synthesis 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 (Eymann

et al., 2002; Bandow et al., 2003a). Second, it was obvious, that

irrespective of the overall consistency of the protein-expression

data with the known mode of action of a given antibiotic, there

were always some proteins with a rather unexpected induction,

indicating that our present knowledge about the detailed mech-

anismsof antibiotic action andthe cellular response to antibiotics

is still limited. A special examplewas provided by the analysis of

nitrofurantoin, an antibioticintroduced in the1950s andstill used

frequently in the therapy of urinary tract infections. In earlier

studies, its activity was attributed to such distinct target areas asDNA and/or RNA synthesis, carbohydrate metabolism, or an

inhibition of othermetabolicenzymes (Guay,2001). The protein-

expression profile of nitrofurantoin showed a remarkable

similarity to that of diamide (Bandow et al., 2003a), an agent

that causes oxidative damage by inducing non-native disulfide

bonds (Kosower & Kosower, 1995). That result led the authors to

propose that protein inhibition due to non-native disulfide

formation may be the primary antibacterial mode of action of

nitrofurantoin; that proposal would explain nicely the pleiotropic

effects reported earlier and is also compatible with studies that

attributed its toxic side-effects on eukaryotic cells to the rapid

formation 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 for

drug-discovery purposes, it was demonstrated that crucial hints

on the molecular mechanisms of novel antibacterial compounds

can be obtained when the new mechanism is similar to that of a

reference antibiotic already included in the protein-expression

database. 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 1057

A/B (Brands et al., 2003). Even by mere visual inspection, the

proteomic pattern was almost identical to that of chloramphe-

nicol and other peptidyltransferase inhibitors, leading to the

suggestion that BAY 50-2369 as well as TAN 1057 inhibited the

same target, although in a slightly distinct manner because no

cross-resistance to other peptidyltransferase inhibitors was

observed (Limburg et al., 2004). That interpretation has been

proven correct in the meantime by direct mechanistic studies

(Boeddecker et al., 2002; Limburg et al., 2004). A second

recently 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 very

similar to that of mupirocin. In fact, proteins that belong to the

stringent response were similarly overexpressed after exposure

to both antibiotics. Interestingly, both antibiotics led to an

induction of their respective direct targets: the alpha-subunit of

Phe-RS was induced in cells treated with the novel compound

class, whereas mupirocin induced the corresponding Ile-RS

subunit (Fig. 6).

It should be noted that those conclusions could have been

reached 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 personal

impression of the respective observer, Bandow et al. (2003a)

applied a marker-protein-based concept, which allows one to

draw conclusions that are independent of the researcher who

analyzed the data. In addition, as the reference database of

proteomic signatures grows, a direct side-by-side evaluation of

the gels becomes a very tedious task, and the marker-protein

approach substantially reduces the evaluation efforts. In short,

marker proteins were defined as such proteins that were

overexpressed at least twofold under antibiotic influence in two

independent experiments, and that made up at least 0.05% of the

total protein synthesized during the pulse-labeling period. Thenumber of such marker proteins for each of the 30 antibiotics

varied between 0 and 34 with an average of 13.3, and can be used

in, for example, cluster analyses to obtaina first hint to a potential

mode of action.

In spite of the progress reached and documented in that

study, it should still be mentioned that thegeneral applicabilityof

the method for drug-discovery purposes in routine fashion is

limited (i) by the time and effort needed to study a novel

compound by theexperimentally still demanding 2D gels and the

evaluation of the massive data sets obtained, and, (ii) due to the

number of conditions/compounds/mutants studied so far. Both of

those bottlenecks will continue to benefit greatly from further


559



technological advances in 2D gel-based and non-gel-based

technologies, which will be discussed briefly in the following

section.

IV. REMARKS ON TECHNOLOGICAL PROGRESS

A. Progress in Two-Dimensional Gel-Based

Technologies

The introduction of 2D-PAGE in 1975 (Klose, 1975; O’Farrell,

1975) marked a major breakthrough in the analysis of the

complex protein mixture of whole cells and tissues. Resolution

and sensitivity were already high in those original studies, where

polyacrylamide tube gels with Ampholines were employed for

protein separation according to pI. After isoelectric focusing,

proteins were reduced and alkylated in an equilibration stepbeforeseparation on an SDSgel according to Mr,whichisstillthe

standard procedure to this day. Autoradiographs of dried 2D gels

of E. coli crude cell extracts allowed the detection of about 1,100

protein species. For direct detection on the gel, proteins were

stained with Coomassie Blue. Since its introduction, 2D gel-

based proteomics has come a long way. Major technological

developments were aimed at enhancing the reproducibility of the

separation, increasing sensitivity and resolution, and addressing

proteins with physico-chemical properties unfavorable for 2D-

PAGE. However, only when identification of the proteins from

the 2D gel became easier, faster, and cheaper, did the popularity

of proteomics begin to increase rapidly.

The introduction of immobilized pH gradient (IPG) strips

for use in the first dimension (Gorg, Postel, & Gunther, 1988)

certainly enhanced reproducibility. Different silver-staining

protocols (Switzer, Merril, & Shifrin, 1979; De Moreno, Smith,

& Smith, 1985; Rabilloud, 1999) offer high sensitivity but do not

have a broad linear dynamic range that would allow reliable

protein quantification. Fluorescent dyes such as Sypro Ruby

(Berggren et al., 1999; Steinberg et al., 1999) are at least as

sensitive as silver, are more sensitive than colloidal Coomassie

Brilliant Blue staining methods, and offer a linear dynamic range

of three orders of magnitude (Patton, 2000). The 2-Dimensional

Fluororescence Difference Gel-Electrophoresis (DIGE) technol-

ogy from Amersham Biosciences (Piscataway, NJ) relies on

covalently labeling a small fraction (about 1%) of the proteins in

the sample with Cy2, Cy3, or Cy5 dye. Because these dyes have

distinct excision and emission wavelengths, up to three samples

can be labeled, mixed prior to IEF, and separated in a single 2Dgel to further enhance cross-sample comparison by decreasing

any gel-to-gel variation. In addition, narrow pI gradient IPG

strips 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 the

separation of basic andpoorlysoluble proteins such as membrane

proteins (Gorg et al., 1999; Molloy et al., 2001; Ohlmeier, Scharf,

& Hecker, 2000),those protein species as well as extremelysmall

and large proteins (<15 and >120 kDa) still pose major

challenges to the 2D gel technology. Much progress has also

been achieved with respect to throughput and automation. The

newly 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 thedual-channelimagingtechnique ontothe untreatedcontrol(green). Proteinsinducedduringantibiotic

exposureappearin red,and repressedproteins appear in green. The proteomic signatureof the PTUincluded

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-subunitof 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).


560



(Carlsbad, CA) allows 2D-PAGE separation in 24 hr from

rehydration of thesample into theIPG strip forthe first dimension

to protein staining. Furthermore, NextGen Sciences Ltd.

(Cambridgeshire, UK) recently introduced a fully automated

robot that is capable of analyzing three 2D gels at a time under

highly reproducible conditions. Finally, although protein quanti-

fication is still one of the bottlenecks in the 2D gel-based

workflow, image analysis software packages have evolved to

facilitatethe quantificationof all protein spots withinlargesets of

2D gels, requiring different levels of user interaction to ensure

data quality.

As mentioned above, it was the progress in protein identi-

fication from 2D gels that made proteomics attractive to the

broader research community. Although a protein pattern-

matching approach can be successful in finding similarities

between protein expression profiles for a large number of tested

growth conditions, any detailed physiological understanding of

the 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 by

repeatedcycles of Edman degradation (Edman & Begg,1967).In

each cycle phenylisothiocyanate was added to the N -terminal

amino acid, and the cyclic amino acid derivative was removed

under mild acidic conditions and identified by HPLC. Addition

and removal reactions were repeated until the length of the

analyzed protein sequence was sufficient to allow the identifica-

tion of theprotein by comparison with available protein- or DNA-

sequence information. A major drawback besides the lack of

sensitivity was that about one-third of the bacterial proteins are

N -terminally blocked and, therefore, eluded identification by this

method. Two factors arose in the mid-1990s that substantially

simplified proteomicanalyses. For the first time, DNA sequences

of whole bacterial genomes became available and allowed the

prediction of the approximate total number of encoded open

reading frames. At the same time, progress in mass spectrometry

facilitated the analysis of peptides and small proteins, and the

mass accuracy of the measured peptide masses was sufficient

to allow peptide mass fingerprinting. Experimentally obtained

peptide masses of a digested protein spot were compared to a

database that contained all theoretical peptide masses derived

from an in silico digestion of the proteins predicted from the

genome sequence (Henzel et al., 1993). In addition, recent

automation of protein identification significantly increased

throughput (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 into

microtiter plates. A digest-robot performs the in-gel tryptic

digests directly in the microtiter plate, and a spotting robot

applies peptide samples to MALDI targets. MALDI-TOF mass

spectrometers acquire the data, and software packages are

available to automatically extract peptide masses from the

derived spectra, which are submitted to the database search.

Ideally, the scientist is left only to do a quick quality check to

ensure that the hits from the database match the predominant

peaks on the spectra. The same level of automation is also

available for mass spectrometric approaches that involve MS/MS

peptide sequence elucidation. Although MS/MS is particularly

useful when working with highly complex genomes, where

peptide mass fingerprinting is not reliable enough, it is also the

method of choice for the identification of those bacterial proteins

(especially small ones) that do not yield a sufficient number of

peptides after tryptic digestion to ensure an unambiguous identi-

fication by peptide mass fingerprinting. In addition, MS/MS

de novo sequencing of peptides is extremely useful when

working with organisms with incomplete genomic information.

Another important application of mass spectrometry-based

sequencing is the identification of amino acid residues that carry

protein modifications (e.g., phosphorylation), which are often

crucial for protein activity.

B. Progress in Non-Gel-Based Proteomics

Although 2D gel-based proteomics is the method of choice for

many proteomic studies, there are certain limitations to that

technique 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 antibacterial

research would greatly benefitfrom closing these gaps, andbeing

able to analyze the whole proteome of bacterial cells. Evolving

mass spectrometry-based technologies circumvent some of the

limitations of protein separation by 2D-PAGE. Protein extracts

aresubject to tryptic digest, andthe complex peptidemixturesare

separated by liquid chromatography coupled to mass spectro-

metric analysis (LC–MS). Either 1D gel electrophoresis can be

used to reduce the complexity of the protein mixture prior to

digestion (Lasonder et al., 2002; Li, Steen, & Gygi, 2003) or in

the case of multi-dimensional protein identification technology

(MudPit) sample complexity is reduced after digestion by

separating the peptides on strong cation-exchange resins and

subsequent reversed-phase liquid chromatography (Washburn,

Wolters, & Yates, 2001). Multiple approaches utilize heavy-

isotope labeling for quantification; for instance, 15

N-labeling

(Oda et al., 1999; Lahm & Langen, 2000), stable-isotope labeling

with 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. Most

of those technologies are still in the proof-of-concept stage, and

are currently being compared to 2D-PAGE (e.g., Schmidt et al.,

2003) and to each other regarding their benefit for proteomics

studies. The reader interested in those technologies is referred to

some 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 with

chromatography and mass spectrometry hold extreme promise

for the proteomics research community because they are capable

of qualitative andquantitative analysis of protein samples with no

obvious bias towards high solubility or a certain pI. However,

high molecular weight proteins were over-represented in a study

that compared ICAT technology and the classical 2D-gel

approach (Schmidt et al., 2003). MS-based technologies seem

to be more sensitive than the classical 2D-gel approach, and they

have been shown to yield a good coverage of predicted open-


561



reading frames (Florens et al., 2002; Washburn et al., 2002).

Different shortcomings are associated with the quantitative

analysis of peptides ratherthan whole proteins:the analysis of the

huge number of MS spectra obtained from a mixture of peptides

derived from two complex protein samples poses a great

challenge to developers of analysis software, and to chromato-

graphy. Furthermore, and somewhat in contrast to 2D-gel-based

proteomics, the quantification of protein modifications is ex-

tremely difficult because it requires the modified peptide to be

detected and recognized as being modified. In addition, it should

be kept in mind that protein quantification with ICAT technology

requires the presence of cysteine residues in the protein sequence,

because it relies on labeling these cysteines with alkylating

agents of different isotope composition.

In summary, enormous technical progress has been made in

the past decade in gel-based and non-gel-based proteomics

technologies, and further progress is still to be expected, that will

contribute greatly to the popularity and usefulness of proteomics

in the area of drug discovery.

V. OUTLOOK

The increasing resistance development of pathogenic bacteria

requires the counteractive development of new antibiotics with

novel modes of action and free of cross-resistance to presently

applied drugs as an important public health priority. The drugs in

use today stem with no exception from traditional approaches of

random screeningof chemical and natural compound libraries for

antibacterial activity. Whereas still in its early days, there is

reason to believe thatmoretarget-directed, molecular approaches

will be instrumental in finding new antibacterial drugs and will

help to facilitate the rational selection of compound classes

stemming, for example, from the classical screening for anti-

bacterial activity as well as target-based screening. The technical

basis for this scenariowas laid by thedeciphering of the genomes

of more than 120 bacterial strains, and on the evolving

technologies of gene-expression analysis, in particular transcrip-

tome and proteome technologies. The latter two techniques were

themselves crucially dependent on progress in even more basic

methodologies such as chip production and MS spectrometry as

well as software tools to effectively deal with the large datasets

produced by such approaches. There have often been debates

whether proteomics or transcriptomics should be the most

relevant technique for drug-discovery purposes. For example,

proteomics appears to be preferred by many, because proteins are

often the direct drug targets, and they also happen to be theeffector molecules that mediate and regulate the basic cellular

functions. On the other hand, present proteomic technology still

does not offer to study the full genomic equivalent of all proteins,

whereas transcriptome analyses cover the whole genomic

sequence and are also able to produce data at a much higher

pace. Nevertheless, transcript expression profiling is unable to

distinguish between different gene products derived from the

same coding region on the genome (due to, e.g., modifications,

truncations, splice variants). It should also be kept in mind that

none of these technologies will deliver novel drugs on their own.

As many of such technologies as possible should be applied in

combination to provide a deeper biological understanding of a

compound’s action against a living microorganism. That knowl-

edge will be instrumentalin selecting from themanyantibacterial

molecules available those drugs with a desired and promising

biological profile, thereby reducing the target-based attrition

rates in later, more costly, stages of development.

With respect to proteomics, substantial progress has

already been made in elucidating the basic regulatory networks

that form the basis for the extraordinary capacity of bacteria to

adapt to a diversity of lifestyles and external stress factors. The

application of this method for antibacterial drug-discovery

purposes, however, is still in its early days. One reason for this

phenomenon is the fact that the discovery of novel targets, which

is one of the most important applications of proteome studies in

other areas of drug discovery, is not so much a bottleneck in

antibiotic research, because the pathophysiology of most

bacterial 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 become

obvious that proteome applications, alone or in concert with

transcriptome analysis and other more phenotypic methods, playan increasing role in target validation and mode of action

determination of novel compounds and variants of existing

compound classes. In particular, those methodologies are very

helpful in reducing the time needed to obtain that information,

which is important in every drug discovery project. Successful

exploitation of those technologies for the antibacterial drug

discovery process depends on further progress in three main

areas:

1. The data collection, which should be expanded to comprise

as manyantibacterial compounds with diverse mechanisms

of action as possible, to cover, ideally, all relevant targets.

Because for novel targets, such referenceantibiotics arenotalways available, the analysis of conditional mutants in

such targets should be included.

2. The data analysis tools, which should be optimized or

developed to handle the enormous datasets efficiently and

to facilitate data evaluation in terms of mechanism-specific

signatures; for example, by including clustering, chemo-

metric, and artificial intelligence approaches.

3. Last but not least, further methodological progress in order

to increase thespeed, throughput, andreproducibilityof 2D

gel-based as well as non-gel-based techniques.

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