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8/13/2019 Bacterial Proteomics
http://slidepdf.com/reader/full/bacterial-proteomics 1/17
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]
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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.
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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.
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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 &
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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-
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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.
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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.
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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
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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
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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).
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(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-
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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.
REFERENCES
Abergel C, Coutard B, Byrne D, Chenivesse S, Claude JB, Deregnaud C,
Fricaux T, Gianesini-Boutreux C, Jeudy S, Lebrun R, Maza C,
Notredame C, Poirot O, Suhre K, Varagnol M, Claverie JM. 2003.
Structural genomics of highly conserved microbial genes of unknown
targets in search of new antibacterial targets. J Struct Funct Genomics
4:141–157.
Aebersold R, Mann M. 2003. Mass spectrometry-based proteomics. Nature
422:198–207.
Agabian N, Unger B. 1978. Caulobacter crescentus cell envelope: Effect of
growthconditions on murein and outer membrane proteincomposition.
J Bacteriol 133:987– 994.
& BROTZ-OESTERHELT ET AL.
562
8/13/2019 Bacterial Proteomics
http://slidepdf.com/reader/full/bacterial-proteomics 15/17
Alksne LE. 2002. Virulence as a target for antimicrobial chemotherapy.
Expert Opin Investig Drugs 11:1149–1159.
Allsop AE. 1998. New antibiotic discovery, novel screens, novel targets, and
impact of microbial genomics. Curr Opin Microbiol 1:530– 534.
Apfel CM, Locher H, Evers S, Takacs B, Hubschwerlen C, Pirson W, Page
MG,KeckW.2001.Peptide deformylaseas an antibacterialdrug target:
Target validation and resistance development. Antimicrob AgentsChemother 45:1058–1064.
Appelbaum PC. 2002. Resistance among Streptococcus pneumoniae:
Implications for drug selection. Clin Infect Dis 34:1613– 1620.
Armitage JP,DormanCJ, Hellingwerf K, Schmitt R, SummersD, Holland B.
2003. Micro meeting: Thinking and decision making, bacterial style:
BacterialNeuralNetworks,Obernai,France, 7th–12th June, 2002. Mol
Microbiol 47:583– 593.
Armstrong GL, Conn LA, Pinner RW. 1999. Trends in infectious disease
mortality in the United States during the 20thcentury. JAMA 281:61–66.
BandowJE, Brotz H,HeckerH. 2002. Bacillus subtilis tolerance of moderate
concentrations of rifampin involves the sigma(B)-dependent general
and multiple stress response. J Bacteriol 184:459– 467.
Bandow JE, Brotz H, Leichert LIO, Labischinski H, Hecker H. 2003a.
Proteomic approach to understanding antibiotic action. Antimicrob
Agents Chemther 47:948– 955.
Bandow J, Becher D, Buttner K, Hochgrafe F, Freiberg C, Brotz H, Hecker
M. 2003b. The role of peptide deformylase in protein biosynthesis: A
proteomic study. Proteomics 3:299–306.
Berggren K, Steinberg T, Lauber W, Carroll J, Lopez M, Chernokalskaya E,
Zieske L, Diwu Z, Haugland R, Patton W. 1999. A luminescent
ruthenium complexfor ultrasensitive detection of proteins immobilized
on membrane supports. Anal Biochem 279:129– 143.
Bernhardt J, Buttner K, Scharf C, Hecker M. 1999. Dual channel imaging of
two-dimensional electropherograms in Bacillus subtilis. Electrophor-
esis 20:2225–2240.
Bernhardt J, Weibezahn J, ScharfC, HeckerM. 2003. Bacillus subtilis during
feast and famine: Visualization of the overall regulation of protein
synthesis during glucose starvation by proteome analysis. Genome Res
13:224–237.Beyer D, Kroll H-P, Endermann R, Schiffer G, SiegelS, Bauser M, Pohlmann
J, BrandsM, ZiegelbauerK, Haebich D,EymannC, Brotz-Oesterhelt H.
2004. Discovery of a new class of bacterial phenylalanyl-tRNA syn-
thetase inhibitors with high potency and broad spectrum activity.
Antimicrob Agents Chemother 48:525–532.
BoeddeckerN, BahadorG, Gibbs C, MaberyE, WolfJ, Xu L, Watson J. 2002.
Characterization of a novel antibacterial agent that inhibits bacterial
translation. RNA 8:1120– 1128.
Brands M, Endermann R, Gahlmann R, Kruger J, Raddatz S. 2003.
Dihydropyrimidinones—A new class of anti-staphylococcal antibio-
tics. Bioorg Med Chem Lett 13:241– 245.
Bush K. 2002. The impact of b-lactamses on the develoment of novel
antimicrobial agents. Curr Opin Invest Drugs 3:1284–1290.
ChoMJ,JeonBS,ParkJW, Jung TS, SongJY, LeeWK,ChoiYJ,ChoiSH,Park
SG, Park JU, Choe MY, Jung SA, Byun EY, Baik SC, Youn HS, Ko GH,Lim D, Rhee KH. 2002. Identifying the major proteome components of
Helicobacter pylori strain 26695. Electrophoresis 23: 1161–1173.
Cordwell SJ, Nouwens AS, Verrills NM, Basseal DJ, Walsh BJ. 2000.
Subproteomics based upon protein cellular location and relative
solubilities in conjunction with composite two-dimensional electro-
phoresis gels. Electrophoresis 21:1094–1103.
Cordwell SJ,Larsen MR, Cole RT, WalshBJ. 2002. Comparative proteomics
of Staphylococcus aureus and the response of methicillin-resistant and
methicillin-sensitive strains to Triton X-100. Microbiology 148:2765–
2781.
Corthals GL, Wasinger VC, Hochstrasser DF, Sanchez JC. 2000. The dyna-
mic range of protein expression: A challenge for proteomic research.
Electrophoresis 21:1104–1115.
De Moreno MR, Smith JF, Smith RV. 1985. Silver staining of proteins in
polyacrylamide gels: Increased sensitivity through combined Coomas-
sie blue-silver stain procedure. Anal Biochem 151:466– 470.
Drlica K, Zhao X. 1997. DNA gyrase, topoisomerase IV, and the
4-quinolones. Microbiol Mol Biol Rev 61:377– 392.
Edman P, Begg G. 1967. A protein sequenator. Eur J Biochem 1:80– 91.
Evers S, Di Padova K, Meyer M, Langen H, Fountoulakis M, Keck W,
Gray CP. 2001. Mechanism-related changes in the gene transcription
and protein synthesis patterns of Haemophilus influenzae after treatment
with transcriptional and translational inhibitors. Proteomics 1:522–
544.
Eymann C, HomuthG, ScharfC, HeckerM. 2002. Bacillus subtilis functional
genomics: Global characterization of the stringent response by pro-
teome and transcriptome analysis. J Bacteriol 184:2500– 2520.
Fischer HP, Brunner NA, Wieland B, Paquette J, Macko L, Ziegelbauer K,
Freiberg C. 2004. Identification of antibiotic stress-inducible promo-
ters: A systematic approach to novel pathway-specific reporter assays
for antibacterial drug discovery. Genome Res 14:90–98.
Fleischmann RD,AdamsMD, White O, Clayton RA,Kirkness EF, Kerlavage
AR, Bult CJ, Tomb JF, Dougherty BA, Merrick JM, McKenney K,
SuttonG, FitzHugh W, FieldsW,GocayneJD, ScottJ, Shirley R, LiuLI,
Glodek A, Kelley JM, Weidman JF, Phillips CA, Spriggs A, Hedblom
E, Cotton MD, Utterback TR, Hanna MC, Nguyen DT, Saudek DM,
Brandon RC, Fine LD, Fritchman LJ, Fuhrmann JL, Geoghagen
NSM, Gnehm CL, McDonald LA, Small KV, Fraser CM, Smith HO,
Venter JC. 1995. Whole-genome random sequencing and assembly of
Haemophilus in fluenzae Rd. Science 269:496–512.
Florens L, Washburn MP, Raine JD, Anthony RM, Grainger M, Hayness JD,
Moch JK, Muster N, Sacci JB, Tabb DL, Witney AA, Wolters D, Wu Y,
Gardner MJ, Holder AA, Sinden RE, Yates JR, Carucci DJ. 2002. A
proteomic view of the Plasmodium falciparum life cycle. Nature
419:537.
Gale EF, Cundliffe E, Reynolds PE, Richmond MH, Waring MJ. 1981.
The molecular basis of antibiotic action. 2nd edition. London, UK:
Wiley.
Glass JI, Belanger AE, Robertson GT. 2002. Streptococcus pneumoniae as a
genomics platform for broad-spectrum antibiotic discovery. Curr OpinMicrobiol 5:338– 342.
Gmuender H, Kuratli K, Di Padova K, Gray CP, Keck W, Evers S. 2001. Gene
expressionchanges triggered by exposure of Haemophilus influenzae to
novobiocin or ciprofloxacin: Combined transcription and translation
analysis. Genome Res 11:28– 42.
Godovac-Zimmermann J, Brown LR. 2001. Perspectives for mass spectro-
metry and functional proteomics. Mass Spectrom Rev 20:1– 57.
Gomes SL, Juliani MH, Maia JC, Silva AM. 1986. Heat shock protein
synthesis during development in Caulobacter crescentus. J Bacteriol
168:923–930.
Graves PR, Haystead TAJ. 2002. Molecular biologist’s guide to proteomics.
Microbiol Mol Biol Rev 66:39– 63.
GrayCP,Keck W. 1999.Bacterialtargets and antibiotics:Genome-based drug
discovery. Cell Mol Life Sci 56:779–787.Guay DR. 2001. An update on the role of nitrofurans in the management of
urinary tract infections. Drugs 61:353– 364.
Guina T, Purvine SO, Yi EC, Eng J, Goodlett DR, Aebersold R, Miller SI.
2003a. Quantitative proteomic analysis indicates increased synthesis of
a quinolone by Pseudomonas aeruginosa isolates from cystic fibrosis
airways. Proc Natl Acad Sci USA 100:2771–2776.
Guina T, WuM, MillerSI, Purvine SO,Yi EC,Eng J, Goodlett DR,Aebersold
R, Ernst RK, Lee KA. 2003b. Proteomic analysis of Pseudomonas
aeruginosa grown under magnesium limitation. J Am Soc Mass
Spectrom 14:742– 751.
Gygi SO, Rist B, Gerber SA, Turecek F, Gelb MH, Aebersold R. 1999.
Quantitative analysis of complex protein mixtures using isotope-coded
affinity tags. Nat Biotechnol 17:994– 999.
PROTEOMICS AND ANTIBACTERIAL DRUG DISCOVERY &
563
8/13/2019 Bacterial Proteomics
http://slidepdf.com/reader/full/bacterial-proteomics 16/17
Gorg A, Postel W, Gunther S. 1988. The current state of two-dimensional
electrophoresis with immobilized pH gradients. Electrophoresis 9:
531–546.
Gorg A, Obermaier C, Boguth G, Weiss W. 1999. Recent developments in
two-dimensional gel electrophoresis with immobilized pH gradients:
Wide pH gradients up to pH 12, longer separation distances and
simplified procedures. Electrophoresis 20:712–717.Gorg A, Obermaier C, Boguth G, Harder A, Scheibe B, Wildgruber R, Weiss
W. 2000. The current state of two-dimensional electrophoresis with
immobilized pH gradients. Electrophoresis 21:1037–1053.
Haas M, Beyer D, Gahlmann R, Freiberg C. 2001. YkrB is the main peptide
deformylase in Bacillus subtilis, a eubacterium containing two func-
tional peptide deformylases. Microbiology 147:1783–1791.
HamdanM, Righetti PG.2002.Modern strategiesfor protein quantificationin
proteome analysis: Advantages and limitations. Mass Spectrom Rev
21:287–302.
Hecker M. 2003. A proteomic view of cell physiology of Bacillus subtilis—
Bringing the genome sequence to life. Adv Biochem Eng Biotechnol
83:57–92.
Hecker M, Engelmann S, Cordwell SJ. 2003. Proteomics of Staphylococcus
aureus—Current state and future challenges. J Chromatogr B Analyt
Technol Biomed Life Sci 787:179– 195.
Hecker M, Schumann W, Volker U. 1996. Heat shock and general stress
proteins in Bacillus subtilis. Mol Microbiol 19:417–428.
Hecker M, Volker U. 2001. General stress response of Bacillus subtilis and
other bacteria. Adv Microb Physiol 44:35 –91.
Henzel WJ, Billeci TM, Stults JT, Wong SC, Grimley C, Watanabe C. 1993.
Identifying proteins from two-dimensional gels by molecular mass
searching of peptidefragments in protein sequencedatabases. Proc Natl
Acad Sci USA 90:5011–5015.
Hiramatsu K, Cui L, Kuroda M, Ito T. 2001. The emergence and evolution of
methicillin-resistant Staphylococcus aureus. Trends Microbiol 9:486–
493.
Hoener B, Noach A, Andrup M, Yen TS. 1989. Nitrofurantoin produces
oxidative stress and loss of glutathione and protein thiols in the isolated
perfused rat liver. Pharmacology 38:363–373.Johnson KW, Lofland D, Taylor S, Burli R, Gross M, Ayscough A, Moser H,
Waller A, East S, Keavey K, Hu W, Girish S, Difuntorum S, Chen H,
GarciaM, Hoch U, Clements J. 2003. SecondgenerationPDFinhibitors
for respiratory tractinfections.Abstract F-1481, 43rd ICAAC, Chicago,
IL, 2003.
Kaderbhai NN, Broadhurst DI, Ellis DI, Goodacre R, Kell DB. 2003.
Functionalgenomics via metabolic footprinting:Monitoringmetabolite
secretion by E. coli tryptophan metabolism mutants using FT-IR and
direct injection electrospray mass spectrometry. Comp Funct Genome
4:376–391.
Klose J. 1975. Protein mapping by combined isoelectric focusing and
electrophoresis of mouse tissues. Humangenetik 26:231–243.
Kolker E, Purvine S, Galperin MY, Stolyar S, Goodlett DR, Nesvizhskii AI,
Keller A,Xie T, EngJK, YiE, Hood L,PiconeAF, ChernyT,TjadenBC,
Siegel AF, Reilly TJ, Makarova KS, Palsson BO, Smith AL. 2003.Initial proteome analysis of model microorganism Haemophilus
influenzae strain Rd KW20. J Bacteriol 185:4593– 4602.
Kornilovska I, Nilsson I, Utt M, Ljungh A, Wadstrom T. 2002. Immunogenic
proteins of Helicobacter pullorum, Helicobacter bilis, and Helicobac-
ter hepaticus identified by two-dimensional gel electrophoresis and
immunoblotting. Proteomics 2:775–783.
Kosower NS, Kosower EM. 1995. Diamide: An oxidant probe for thiols.
Methods Enzymol 251:123– 133.
Krueger JH,WalkerGC. 1984. groEL and dnaK genes of Escherichia coli are
induced by UV irradiation and nalidixic acid in an htpRþ-dependent
fashion. Proc Natl Acad Sci USA 81:1499–1503.
Kuroda M, Ohta T, Uchiyama I, Baba T, Yuzawa H, Kobayashi I, Cui L,
Oguchi A, Aoki K, Nagai Y, Lian J, Ito T, Kanamori M, Matsumaru H,
Maruyama A, Murakami H, Hosoyama A, Mizutani-Ui Y, Takahashi
NK, Sawano T, Inoue R, Kaito C, Sekimizu K, Hirakawa H, Kuhara S,
Goto S, Yabuzaki J, Kanehisa M, Yamashita A, Oshima K, Furuya K,
Yoshino C, Shiba T, Hattori M, Ogasawara N, Hayashi H, Hiramatsu
K. 2001. Whole genome sequencing of methicillin-resistant Staphylo-
coccus aureus. Lancet 357:1225–1240.
Lahm HW, Langen H. 2000. Mass spectrometry: A tool for the identificationof proteins separated by gels. Electrophoresis 21:2105–2114.
Langen H, Takacs B, Evers S, Berndt P, Lahm HW, Wipf B, Gray C,
Fountoulakis M. 2000. Two-dimensional map of Haemophilus in-
fluenzae. Electrophoresis 21:411–429.
Lasonder E, Ishihama Y, Andersen JS, Vermunt A, Pain A, Sauerwein RW,
Eling WM, Hall N, Waters AP, Stunnenberg HG, Mann M. 2002.
Analysis of the Plasmodium falciparum proteome by high-accuracy
mass spectrometry. Nature 419:537–542.
Lee YM, Almqvist F, Hultgren SJ. 2003. Targeting virulence for anti-
microbial chemotherapy. Curr Opin Pharmacol 3:513–519.
Li J, Steen H, Gygi SP. 2003. Protein profiling with cleavable isotope-coded
affinity tag(ICAT) reagents: Theyeastsalinity stressresponse.Mol Cell
Proteomics 2:1198– 1204.
Lill J. 2003. Proteomic tools for quantitation by mass spectrometry. Mass
Spectrom Rev 22:182– 194.
Lilley KS, Razzaq A, Dupree P. 2002. Two-dimensional gel electrophoresis:
Recentadvancesin samplepreparation, detection and quantitation. Curr
Opin Chem Biol 6:46–50.
Limburg E, Gahlmann R, Kroll HP, Beyer D. 2004. Ribosomal alterations
contribute to bacterial resistance against the dipeptide antibiotic TAN
1057. AAC 48:619– 622.
Linn T, Losick R. 1976. The program of protein synthesis during sporulation
in Bacillus subtilis. Cell 8:103–114.
Livermore DM. 2003. Linezolid in vitro: Mechanism and antibacterial
spectrum. J Antimicrob Chemother 51(Suppl 2):ii9– ii16.
Lomovskaya O, Watkins WJ. 2001. Efflux pumps: Their role in antibacterial
drug discovery. Curr Med Chem 8:1699– 1711.
Mann M, Hendrickson RC, Pandey A. 2001. Analysis of proteins and pro-
teomes by mass spectrometry. Annu Rev Biochem 70:437– 473.
Mathesius U, Mulders S, Gao M, TeplitskiM, Caetano-Anolles G, Rolfe BG,
Bauer WD. 2003. Extensive and specific responses of a eukaryote to
bacterial quorum-sensing signals. Proc Natl Acad Sci USA 100:1444–
1449.
Mirgorodskaya OA,Kozmin YP, TitovMI, Korner R, SonksenCP,Roepstorff
P. 2000. Quantitation of peptides and proteins by matrix-assisted laser
desorption/ionization mass spectrometry using (18)O-labeled internal
standards. Rapid Commun Mass Spectrom 14:1226–1232.
Mollenkopf HJ, Mattow J, Schaible UE, Grode L, Kaufmann SH, Jungblut
PR. 2002. Mycobacterial proteomes. Methods Enzymol 358:242–256.
Molloy MP, Phadke ND, Maddock JR, Andrews PC. 2001. Two-dimensional
electrophoresis and peptide mass fingerprinting of bacterial outer
membrane proteins. Electrophoresis 22:1686–1696.
Naumann D, Labischinski H. 1990. Process and device for rapid testing of the effects of agents on micro-organisms. International Patent WO 90/
09454.
Neidhardt FC, Ingraham JL, Schaechter M. 1990. Physiology of the bacterial
cell: A molecular approach. Sunderland, MA: Sinauer Publishing.
pp 351–388.
Ng WL, Kazmierczak KM, Robertson GT, Gilmour R, Winkler ME. 2003.
Transcriptional regulation and signature patterns revealed by microarray
analyses of Streptococcus pneumoniae R6 challenged with sublethal
concentrations of translation inhibitors. J Bacteriol 185:359–370.
Nyman TA. 2001. Therole of massspectrometryin proteome studies.Biomol
Eng 18:221–227.
O’Farrell PH. 1975. High resolution two-dimensional electrophoresis of
proteins. J Biol Chem 250:4007–4021.
& BROTZ-OESTERHELT ET AL.
564
8/13/2019 Bacterial Proteomics
http://slidepdf.com/reader/full/bacterial-proteomics 17/17
Oda Y, Huang K, Cross FR, Cowburn D, Chait BT. 1999. Accurate
quantitation of protein expression and site-specific phosphorylation.
Proc Natl Sci USA 96:6591– 6596.
Ohlmeier S, ScharfC, HeckerM. 2000. Alkaline proteins of Bacillus subtilis:
First steps towards a two-dimensional alkaline master gel. Electro-
phoresis 21:3701–3709.
Ong SE, Foster LJ, Mann M. 2003. Mass spectrometric-based approaches inquantitative proteomics. Methods 29:124–130.
Ong SE, Blagoev B, Kratchmarova I, Kristensen DB, Steen H, Pandey A,
Mann M. 2002. Stable isotope labeling by amino acids in cell culture,
SILAC, as a simple and accurate approach to expression proteomics.
Mol Cell Proteomics 1:376– 386.
Pasa-Tolic L, Lipton MS, Masselon CD, Anderson GA, Shen Y, Tolic N,
Smith RD. 2002. Gene expression profiling using advanced mass
spectrometric approaches. J Mass Spectrom 37:1185–1198.
Patton WF. 2000. A thousand points of light: The application of fluorescence
detection technologies to two-dimensional gel electrophoresis and
proteomics. Electrophoresis 21:1123–1144.
Payne DJ, Wallis NG, Gentry DR, Rosenberg M. 2000. The impact of
genomics on novel antibacterial targets. Curr Opin Drug Discov Dev
3:177–190.
Rabilloud T. 1999. Silver staining of 2-D electrophoresis gels. Methods Mol
Biol 122:297–305.
Reeh S, Pedersen S, Neidhardt FC. 1977. Transient rates of synthesis of five
aminoacyl-transfer ribonucleic acid synthetases during a shift-up of
Escherichia coli. J Bacteriol 129:702–706.
Schmid MB. 2001. Microbial genomics—New targets, new drugs. Expert
Opin Ther Targets 5:465– 475.
Schmidt F, Donahoe S, Hagens K, Mattow J, Schaible UE, Kaufmann SH,
Aebersold R, Jungblut PR. 2004. Complementary analysis of the
Mycobacterium tuberculosis proteome by two-dimensional electro-
phoresis and isotopecoded affinity tag technology. Mol Cell Proteomics
3:24–42.
Sechi S, OdaY. 2003. Quantitative proteomicsusing massspectrometry. Curr
Opin Chem Biol 7:70–77.
Shaw KJ, Morrow BJ. 2003. Transcriptional profiling and drug discovery.Curr Opin Pharmacol 3:508–512.
Shaw KJ, Miller N, Liu X, Lerner D, Wan J, Bittner A, Morrow BJ. 2003.
Comparison of the changes in global gene expression of Escherichia
coli induced by four bactericidal agents. J Mol Microbiol Biotechnol
5:105–122.
Sievert DM, Boulton ML, Stoltman G, Johnson D, Stobierski MG, Downes
FP, SomselPA, Rudrik JT, Brown W, HafeezW,LundstromT, Flanagan
E, Johnson R, Mitchell J, Chang S. 2002. Staphylococcus aureus
resistant to vancomycin-United States, 2002. MMWR Morb Mortal
Wkly Rep 51:565–567.
Silva JM, Khan S, O’Brien PJ. 1993. Molecular mechanisms of nitrofur-
antoin-induced hepatocyte toxicity in aerobic versus hypoxic condi-
tions. Arch Biochem Biophys 305:362– 369.
Singh VK, Jayaswal RK, Wilkinson BJ. 2001. Cell wall-active antibiotic
inducedproteins of Staphylococcus aureus identified using a proteomicapproach. FEMS Microbiol Lett 199:79– 84.
Steinberg T, Lauber W, Berggren K, Kemper C, Yue S, Patton W. 1999.
Fluorescence detection of proteins in sodium dodecyl sulfate-
polyacrylamide gels using environmentally benign, nonfixative, saline
solution. Electrophoresis 21:497–508.
Strahilevitz J, Rubinstein E. 2002. Novel agents for resistant Gram-positive
infections: A review. Int J Infect Dis 6(Suppl 1):S38–S46.
Suga H, Smith KM. 2003. Molecular mechanisms of bacterial quorum
sensing as a new drug target. Curr Opin Chem Biol 7:586– 591.
Sutton MD, Smith BT, Godoy VG, Walker GC. 2000. The SOS response:
Recent insights into umuDC-dependent mutagenesis and DNA damage
tolerance. Annu Rev Genet 34:479–497.
Switzer RC, Merril CR, Shifrin S. 1979. A highly sensitive silver stain for
detecting proteins and peptides in polyacrylamide gels. Anal Biochem
98:231–237.
Tang CM, Moxon ER. 2001. The impact of microbial genomics on anti-
microbial drug development. Annu Rev Genomics Hum Genet 2:259–
269.
Tao WA, Aebersold R. 2003. Advances in quantitative proteomics via stableisotope tagging and mass spectrometry. Curr Opin Biotech 14:110–
118.
Thoren K, Gustafsson E, Clevnert A, Larsson T, Bergstrom J, Nilsson CL.
2002. Proteomic study of non-typable Haemophilus influenzae.
J Chromatogr B Analyt Technol Biomed Life Sci 782:219– 226.
Tonella L, Hoogland C, Binz PA, Appel RD, Hochstrasser DF, Sanchez JC.
2001. New perspectives in the Escherichia coli proteome investigation.
Proteomics 1:409– 423.
Ueberle B, Frank R, Herrmann R. 2002. The proteome of the bacterium
Mycoplasma pneumoniae: Comparing predicted open readingframes to
identified gene products. Proteomics 2:754– 764.
VanBogelen RA. 2003. Probing the molecular physiology of the microbial
organism, Escherichia coli using proteomics. Adv Biochem Eng
Biotechnol 83:27– 55.
VanBogelen RA, Neidhardt FC. 1990. Ribosomes as sensors of heat and
cold shock in Escherichia coli. Proc Natl Acad Sci USA 87:5589–
5593.
VanBogelen RA, Schiller E, Thomas JD, Neidhardt FC. 1999. Diagnosis of
cellular states of microbial organisms using proteomics. Electrophor-
esis 20:2149–2159.
Vandahl BB, Birkelund S, Demol H, Hoorelbeke B, Christiansen G,
Vandekerckhove J, Gevaert K. 2001. Proteome analysis of the
Chlamydia pneumoniae elementary body. Electrophoresis 22:1204–
1223.
Walsh C. 2003. Antibiotic resistance. In: Antibiotics—Actions, origins,
resistance. Washington: ASM Press. pp 89–155.
WashburnMP, WoltersD, Yates JR III. 2001. Large-scale analysis of theyeast
proteome by multi-dimensional protein identification technology. Nat
Biotechnol 19:242– 247.WashburnMP, Ulaszek R, Deciu C, Schieltz DM,YatesJR III. 2002. Analysis
of quantitative proteomic data generated via multi-dimensional protein
identification technology. Anal Chem 74:1650–1657.
Wasinger VC, Cordwell SJ, Cerpa-Poljak A, Yan JX, Gooley AA, Wilkins
MR, Duncan MW, Harris R, Williams KW, Humphrey-Smith I. 1995.
Progress with gene product mapping of the Mullicutes Mycoplasma
genitalium. Electrophoresis 16:1090–1094.
Weidenmaier C, Kristian SA, Peschel A. 2003. Bacterial resistance to
antimicrobial host defenses—An emerging target for novel antiinfec-
tive strategies? Curr Drug Targets 4:643–649.
WHO. 2001. Global strategy for containment of antimicrobial resistance.
WHO/CDS/CSR/DRS/2001.2. World Health Organization, Geneva,
who.int/emc/amrpdfs/WHO_Global_Strategy_English.pdf.
Wu CC, Yates JR III. 2003. The application of mass spectrometry to mem-
brane proteomics. Nat Biotechnol 21:262– 267.Yarmush ML, Jayaraman A. 2002. Advances in proteomic technologies.
Annu Rev Biomed Eng 4:349–373.
Yoshida M, Loo JA, Lepleya RA. 2001. Proteomics as a tool in the
pharmaceutical drug design process. Curr Pharm Des 7:291– 310.
Young FS, Neidhardt FC. 1978. Effect of inhibitors of elongation factor Tu
on the metabolic regulation of protein synthesis in Escherichia coli.
J Bacteriol 135:675– 686.
Zhang Y, Amzel LM. 2002. Tuberculosis drug targets. Curr Drug Targets.
3:131–154.
Ziebandt AK, Weber H, Rudolph J, Schmid R, Hoper D, Engelmann S,
Hecker M. 2001. Extracellular proteins of Staphylococcus aureus and
the role of SarA and sigma B. Proteomics 1:480–493.
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