Effects of Subminimum Inhibitory Concentrations of Antibiotics on the Pasteurella multocida Proteome Bindu Nanduri,† Mark L. Lawrence,*,† Carolyn R. Boyle,† Mahalingam Ramkumar,‡ and Shane C. Burgess† College of Veterinary Medicine and Department of Computer Science and Engineering, Mississippi State University, Mississippi State, Mississippi 39762 Received October 25, 2005
Subminimum inhibitory concentrations (sub-MICs) of antibiotics can be therapeutically effective, but the underlying molecular mechanisms are not well-characterized. We analyzed the Pasteurella multocida proteome response to sub-MICs of amoxicillin, chlortetracycline, and enrofloxacin using isotope-coded affinity tags (ICAT). There were parallel effects on inhibition of growth kinetics and suppression of protein expression by clusters of orthologous groups (COG) categories. Potential compensatory mechanisms enabling antibiotic adaptation were identified, including increased RecA expression caused by enrofloxacin. Keywords: isotope-coded affinity tag • ICAT • antibiotic • subminimum inhibitory concentration • enrofloxacin • chlortetracycline • amoxicillin • bovine respiratory disease • clusters of orthologous groups • RecA
Introduction Treating bacterial infections with antibiotics and other antimicrobial drugs is based on achieving the minimum inhibitory concentration (MIC) of the antimicrobial drug, or combination of drugs, in the affected tissues for a sufficient time.1 However, emergent multidrug-resistant bacteria challenge this paradigm, and there are no significant strides in the development of new classes of antimicrobial drugs.2 For now, treating bacterial infections must rely on available antimicrobials. Furthermore, intentional release of multidrug-resistant bacteria as biowarfare agents is now considered a real threat.3 Subminimum inhibitory concentrations (sub-MICs) of antibiotics can treat bacterial infections to reduce disease and improve growth in animals.4,5 However, the molecular mechanisms responsible are ill-defined. We recently used nonisotopic quantitative proteomics to determine that leukotoxin expression is significantly decreased in the bovine respiratory disease pathogen Mannheimia haemolytica after sub-MIC treatment with chlortetracycline and chlortetracycline/sulfamethazine.6 Here, we use an isotope-coded affinity tag (ICAT) proteomics approach to identify molecular mechanisms responsible for the sub-MIC effects of antibiotics in the human and animal respiratory pathogen Pasteurella multocida.7,8 We investigated three different classes of antibiotics with different modes of action: amoxicillin (AMX) (β-lactam class) inhibits cross-linking of peptidoglycan in formation of the bacterial cell wall, chlortetracycline (CTC) (tetracycline class) inhibits protein synthesis * To whom correspondence should be addressed. College of Veterinary Medicine, Box 6100, Mississippi State, MS 39762. Phone: (662) 325-1195. Fax: (662) 325-1031. E-mail:
[email protected]. † College of Veterinary Medicine. ‡ Department of Computer Science and Engineering.
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at the 30S ribosomal subunit, and enrofloxacin (ENR) (fluoroquinolone class) interferes with DNA metabolism by inhibiting DNA gyrase and topoisomerase IV.9 Resistance to all three antibiotics is documented in Gram-negative and Gram-positive bacteria.10-13 Penicillin- and tetracycline-resistant strains of P. multocida exist.14 Resistance to ENR has not been documented for P. multocida; however, a number of other Gram-negative pathogens are resistant to fluoroquinolones. P. multocida causes bovine respiratory disease (BRD), fowl cholera, atrophic rhinitis in swine, and pneumonia in sheep,7,15-17 and because of this, P. multocida is one of the species targeted by sub-MIC antibiotics in production of animal feeds. Human disease caused by P. multocida usually results from bite and scratch wounds from cats or dogs, although human infection can also occur through nonbite animal exposure.8,18 Over 1 million Americans are bitten by animals annually, with consequences ranging from mild discomfort to life-threatening infection.19 Historically, P. multocida is significant as the first live-attenuated vaccine.20,21 In 2002, the P. multocida genome was sequenced.22 ICAT is an accepted method for differentially labeling proteins (with 12C or 13C) at cysteine residues for quantitative proteomics.23-26 Here, we grew P. multocida under sub-MIC antibiotic and control in vitro conditions, extracted and labeled the proteins with heavy or light ICAT reagents, purified the ICAT-labeled peptides, and then identified these peptides using two-dimensional-liquid chromatography-electrospray ionization-tandem mass spectrometry (2D-LC-ESI-MS2) and the Sequest algorithm. We identified proteins with significantly altered expression, some of which could be predicted based on the antibiotic mechanism of action, but many of which would not have been predicted based on knowledge at the time our work began. 10.1021/pr050360r CCC: $33.50
2006 American Chemical Society
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Sub-MIC Antibiotics and Pasteurella multocida
Materials and Methods P. multocida Culture. P. multocida strain Pm70 is a serotype A:1 poultry isolate that has a fully sequenced genome.22 Pm70 was cultivated in brain heart infusion broth (BHI) at 37 °C with rotary aeration. Minimum inhibitory concentrations (MICs) of AMX, CTC, and ENR for Pm70 were determined to be 0.5, 4, and 0.031 µg/mL, respectively, using a macrobroth dilution method.27 Growth kinetics of Pm70 in the presence of 1/4 MIC of the three antibiotics were determined as described.27 For isolating total protein, stationary phase cultures of Pm70 were used to inoculate 50 mL of BHI to an initial A540 of 0.05. Experimental cultures contained 1/4 MIC of AMX, CTC, or ENR, and control cultures were grown without antibiotics. All cultures were grown in triplicate to mid-log phase (A600 of 0.8) and harvested by centrifugation (10 000g, 10 min, 4 °C). Pellets were stored at -80 °C Protein Extraction, ICAT Labeling, and 2D-LC-ESI-MS2. Protein extraction and quantification was conducted as described.6 Protein labeling with ICAT reagents was done using Cleavable ICAT methods development kit following manufacturer’s protocol (Amersham, Piscataway, NJ). Briefly, untreated control and antibiotic-treated proteins (100 µg each) were denatured and reduced at 50 °C for 30 min. The control proteins were transferred to a vial of reconstituted light (12C) ICAT reagent, while the antibiotic-treated proteins were transferred to a vial of reconstituted heavy (13C) ICAT reagent and incubated for 2 h at 37 °C. Combined light- and heavy-labeled proteins for each replicate of each antibiotic treatment were diluted 4× in 50 mM Tris buffer, pH 8.0, and trypsin-digested for 16 h at 37 °C. Prior to the purification of ICAT-labeled peptides, clean up of tryptic peptides was achieved by using a cation-exchange cartridge. Biotinylated ICAT-labeled peptides were then purified by using an avidin affinity column. The eluate from the avidin column was dried, and the biotin tag from the ICAT reagent was subjected to acid cleavage by the addition of a 95:5 solution of cleaving reagent A/cleaving reagent B and 2 h incubation at 37 °C. The peptides were vacuum-dried and analyzed by 2D-LC-ESI-MS2 as described,6 using LCQ Deca XP Plus (Thermo Electron) that has 1 amu resolution. The strong cation-exchange gradient was modified from our published method and was applied in steps of 0, 10, 23, 37, 51, 70, 99, 300, and 700 mM ammonium acetate in 5% ACN and 0.1% formic acid. Peptide Identification and Quantification. The MS2 spectra for all peptides were analyzed using TurboSEQUEST (Bioworks Browser 3.1 SR1; ThermoElectron). For peptide identifications, the nonredundant protein database was downloaded from National Center for Biotechnology Institute (NCBI) (Aug 19, 2004), and a P. multocida protein subset was created. The search parameters allowed for two missed cleavages for trypsin, a static modification of +227.13 (light ICAT reagent) and a differential modification of +9 (heavy ICAT reagent) for cysteine and differential modification of +16 for methionine oxidation. Protein identifications were considered positive when they had peptides of more than five amino acids in length with Xcorr g 1.5, 2.0, and 2.5 for +1, +2, and +3 charged ions, respectively, and ∆Cn values of g0.1 as described.23,28 Peptide quantification was done using the XPRESS algorithm in Bioworks. XPRESS computes the 12C/13C ratio for the matched peptide doublet from the reconstituted ion chromatogram, and these were then confirmed by visual inspection. Protein identifications have been submitted to the PRoteomics IDEntifications (PRIDE) database (accession numbers pending).29 Details of protein
identifications in the PRIDE database follow the proposed guidelines for the documentation of peptide and protein identifications.30 Statistical Analysis. For growth curve comparisons, absorbance data were analyzed using mixed model analysis of variance (ANOVA) for a repeated measures design with one between-subjects factor, treatment group (AMX, CTC, ENR, and control), and one within-subject factor, time (30, 60, 90, 120, 150, 180, 210, 240, 270, and 300 min). The interaction between the factors was also included in the model. When the interaction term was significant, comparisons were made among treatments at each time using the least significant difference test. The clinical importance of statistically significant differences was assessed using 95% confidence intervals.31 For analysis of peptide abundance ratios, data were logarithmically transformed and analyzed using mixed model analysis of variance (ANOVA) for a randomized complete block design with subsampling. In this model, the fixed effect was protein, and the random effects were bacterial culture and peptide fraction. Because the variance component associated with bacterial culture was negligible, this term was pooled with the experimental error term. The ANOVA model was then used to construct 95% confidence intervals for the geometric mean abundance ratio. If the confidence interval contains 1, then there is no statistically significant difference between the light and heavy version of the protein. For both analyses, the normality and homogeneity of variances assumptions needed for ANOVA were validated by using stem-and-leaf plots of the model residuals and by Levene’s test, respectively. Statistical analyses were performed using the MIXED, GLM, and UNIVARIATE procedures of the SAS System for Windows, Version 9.1 (SAS Institute, Inc.).31,32 The level of significance was 5%. Proteome Coverage and Functional Modeling. To quantify the true proteome coverage we achieved in this work, we developed a computational tool called Cys mapper. For the predicted 2015 protein proteome of P. multocida (or any other proteome), Cys mapper determines the number of proteins that do not have a cysteine in the primary sequence. For proteins that contain a cysteine in the sequence, Cys mapper performs in silico trypsin digestion and reports proteins with a cysteinecontaining tryptic peptide of more than five amino acids in length. Transmembrane protein predictions were conducted with SOSUI.33 The identified Pm70 proteome was modeled using the clusters of orthologous group (COG) categories.34,35 Some P. multocida protein functions were inferred from Escherichia coli homologues.36
Results and Discussion P. multocida Culture. AMX had the least effect on PM70 growth, and ENR had the greatest (Figure 1). Compared to no antibiotic control, 1/4 MIC of AMX significantly decreased Pm70 growth starting at 210 min post-inoculation, while 1/4 MIC of CTC significantly inhibited the growth rate of Pm70 compared to control at all time points until 240 min post-inoculation. At this and subsequent time points, there was no significant difference, indicating that CTC did not reduce the final culture density but slowed the rate at which it was achieved (similar to sub-MIC CTC-induced growth inhibition in the related species M. haemolytica and Haemophilus somnus27). Onefourth MIC of ENR caused a prolonged lag phase, a decreased growth rate in log phase, and the lowest final density. Journal of Proteome Research • Vol. 5, No. 3, 2006 573
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Figure 2. Analysis of P. multocida proteome coverage by COG categories. Open bars represent the theoretical percentage each COG category makes up of the total predicted Pm70 proteome, and solid bars represent the percentage the actual identified proteins in each COG category makes up of the total proteins identified in the current study. COG categories are as follows: A, RNA processing and modification; C, energy production and conversion; D, cell cycle control; E, amino acid transport and metabolism; F, nucleotide transport and metabolism; G, carbohydrate transport and metabolism; H, coenzyme transport and metabolism; I, lipid transport and metabolism; J, translation; K, transcription; L, DNA replication, recombination, and repair; M, cell wall/membrane biogenesis; O, post-translational modification and protein turnover; P, inorganic ion transport and metabolism; R, general function prediction only; S, function unknown; and T, signal transduction.
Figure 1. Growth kinetics of P. multocida Pm70 in the presence of 1/4 MICs of AMX, CTC, and ENR at 37 °C compared to control culture in the absence of antibiotic. Each value represents the mean optical density (OD) readings from three cultures.
Proteome Coverage. The total number of proteins identified with all three antibiotics was 245, and 33% of these were singlepeptide identifications. This compares favorably with nonisotopically labeled MudPIT experiments, which have up to 80% single-peptide identifications.37 The identified 245 proteins represent ∼12% of the 2015 proteins in the P. multocida proteome.22 However, this estimate takes no account of the inherent limitations of ICAT labeling or the protein extraction method. For isotopic protein labeling with ICAT reagents, the protein should have a cysteine. Furthermore, as a unique protein identifier in the P. multocida proteome, the cysteine-containing tryptic peptide should be more than five amino acids long.23 The P. multocida proteome has 179 proteins that contain no cysteines. Of the 1836 that contain at least one cysteine, 42 have the cysteine only in peptides less than six amino acids long. Thus, 221 of the 2015 predicted Pm70 proteins (11%) are undetectable using ICAT. It is also possible that a significant proportion of the proteins that have cysteine are lost during ICAT labeling and purification. In addition, our protein extraction method does not enrich for membrane proteins and could result in incomplete representation of the membrane proteome. Our results confirmed this: only 18 of the P. multocida proteins we identified are predicted to have hydrophobic regions that could be transmembrane domains (out of 549 total predicted P. multocida membrane proteins). All of these 18 are predicted proteins with no experimental evidence of being membrane proteins. In total, 770 proteins (37% of the predicted proteome) have no cysteines, have tryptic peptides less than six amino acids long with cysteine, or are predicted to be membrane proteins; therefore, they would not reasonably be expected to be identified. Taking this into account, we achieved ∼20% cover574
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age of the accessible proteome. Most of these proteins were previously only conceptual translations from the genome sequence; thus, we have demonstrated the existence of translated proteins for a large number of predicted P. multocida open reading frames. Proteome coverage by COG category is shown in Figure 2. ICAT representation was calculated for each COG category as the percentage of the total number of proteins identified and compared to the corresponding genome representation of each COG category. Proteins with “housekeeping” functions involved in energy production and conversion (COG C) and translation (COG J) were relatively over-represented in our dataset compared to the genome, while proteins with poorly characterized functions (COG R, general function prediction only, and COG S, unknown function) were relatively under-represented. These differences in protein representation by COG category could indicate that our isolation method selectively enriched certain proteins, or it could simply indicate that proteins in some COG categories are relatively abundant, while proteins in other COG categories are relatively scarce. It could reasonably be predicted that proteins involved in housekeeping functions would be abundant in the bacterial cell, while proteins in COG categories R and S are poorly characterized simply because they are scarce and/or because their expression may only be required in specialized contexts. Functional Analysis. We used COG categories to identify patterns of P. multocida protein expression and to identify the general physiological effects of sub-MICs of the three antibiotics on P. multocida. For each of the three control versus antibiotic treatment comparisons, the mean ICAT ratios and their confidence intervals were calculated (R ) 0.05) (Figure 3). The expected ratio if there is no change in abundance is 1:1. The quantitative effects of antibiotics on COG categories paralleled the effects seen on growth kinetics: the greater the effect on growth kinetics, the more COG categories had significantly decreased protein expression relative to control (i.e., the protein expression ratios in the controls versus antibiotic-treated cultures were significantly increased) (Figure 3). Sub-MIC of AMX only had a significant effect on growth
Sub-MIC Antibiotics and Pasteurella multocida
Figure 3. Changes in protein expression by COG category in response to AMX (A), CTC (B), and ENR (C). Open circles represent the mean ICAT ratios of all the identified proteins within the designated COG category for each of the control vs antibiotic comparisons. Plus and minus signs represent the upper and lower limits, respectively, of the calculated confidence intervals (R ) 0.05). Asterisks indicate COG categories that had a significant change in protein expression (the calculated confidence interval does not include one). COG categories are as follows: A, RNA processing and modification; C, energy production and conversion; D, cell cycle control; E, amino acid transport and metabolism; F, nucleotide transport and metabolism; G, carbohydrate transport and metabolism; H, coenzyme transport and metabolism; I, lipid transport and metabolism; J, translation; K, transcription; L, DNA replication, recombination, and repair; M, cell wall/membrane biogenesis; O, post-translational modification and protein turnover; P, inorganic ion transport and metabolism; R, general function prediction only; S, function unknown; and T, signal transduction.
kinetics in late log and stationary phase, and protein expression was only significantly decreased in one COG category (I; lipid transport and metabolism). Notably, the proteome analysis was done at mid-log phase (A600 of 0.8) when there was no discernible difference in growth. Therefore, proteomics detected a change in bacterial physiology earlier than growth kinetics, indicating that it is a more sensitive indicator of adverse effects on bacterial physiology than traditional growth curves. Sub-MIC of CTC caused significant effects on growth during lag and log phase compared to control; correspondingly, three COG categories had significantly decreased protein expression (C, F, and J; energy production and conversion, nucleotide transport and metabolism, and translation, respectively). SubMIC of ENR had the greatest effects on Pm70 growth; all phases in the growth curve were significantly affected compared to control. It caused a significant decrease in protein expression in five COG categories (C, I, K, L, and P; energy production and conversion, lipid transport and metabolism, transcription, DNA replication and repair, and inorganic ion transport and metabolism, respectively). Notably, the two antibiotics that caused significantly decreased growth rate during lag and log
research articles phase (CTC and ENR) also caused significantly decreased protein expression in category C (energy production and conversion), which could logically slow the growth rate. The two antibiotics that significantly decreased the final density of the cultures in stationary phase (AMX and ENR) also caused significantly decreased protein expression in category I (lipid transport and metabolism); the reason for this effect is currently not known. In addition to the suppressive effects of the antibiotics on protein expression in some COG categories, the antibiotics also appeared to induce compensatory responses, which were reflected as significant increases in protein expression in some categories (i.e., the protein expression ratios in the controls versus antibiotic-treated cultures were significantly decreased) (Figure 3). AMX caused significant increases in protein expression for categories F and K (nucleotide transport and metabolism, and transcription, respectively). These increases appear to be a compensatory response by the bacteria to increase transcription. CTC also caused a significant increase in expression of transcriptional proteins (category K) as well as proteins in category R (general function prediction). ENR caused a significant increase in expression of proteins in category F (nucleotide transport and metabolism), which contains the target of the antibiotic, DNA gyrase. These findings may indicate that upregulation of proteins in transcription and DNA metabolism represents a general compensatory mechanism against sub-MICs of antibiotics. Effects of Antibiotics on Individual Protein Expression. To increase the granularity of our analysis, and to identify possible future drug targets, we next focused on individual proteins with significantly altered expression. At the individual protein level, we used functional data from orthologues in other bacterial species to predict the function of P. multocida proteins for which there is no experimental data. In particular, we used functional data from E. coli based on the EcoCyc database.36 Of the 245 proteins identified, 50 had significantly altered expression in response to antibiotic administration; 11, 20, and 19 proteins had significantly altered expression in response to AMX, CTC, and ENR, respectively (Tables 2, 3, and 4). Proteins that were identified but did not show a significant quantitative change in response to sub-MICs of AMX, CTC, or ENR are listed in the Supplementary Table in Suppporting Information. The significant effects of antibiotics on protein expression are described in the following sections. Amoxicillin. AMX is a β-lactam antibiotic that inhibits cell wall biosynthesis by binding and inactivating the peptidoglycan cross-linking transpeptidases. Active site binding of β-lactams to penicillin binding proteins (Pbps; transpeptidases) results in the formation of a covalent acyl enzyme intermediate with a very long half-life (hours to days).9 β-Lactam-mediated bacterial killing is due to this sequestering of the transpeptidases into futile complexes for long periods of time. There are eight predicted Pbps in the Pm70 genome involved in peptidoglycan cross-linking.22 Of these, six are predicted to be membrane proteins (Pbp, PbpA, PbpB,PbpC, Pbp2, and Pbp3) and one (Pbp2) does not have a cysteine. Pbp4 and Pbp5 are predicted to be soluble proteins; however, the orthologous proteins in E. coli are both membrane-associated. We did not detect any of these known targets in our dataset, possibly because the protein isolation method used did not enrich for membrane proteins. We did not specifically enrich for membrane proteins as our aim was to study whole cell physiology rather than focusing on the already known AMX targets. Future Journal of Proteome Research • Vol. 5, No. 3, 2006 575
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Table 1. P. multocida Proteins with Significant Changes in Expression in Response to 1/4 MIC of AMX
a
gi no.
protein
COG
description
fold changea
15603407b 15601944b 15603156b 15602204b 15602979b 15603256b 12722122 15602977 15602868 15602084 15602686
PckA ThyA DeoD FabB Pnp RpS4 PM1712 DeaD PM1003 ArcA unknown
C F F I J J K L M T -
phosphoenolpyruvate carboxykinase thymidylate synthase purine-nucleoside phosphorylase 3-oxoacyl-(acyl-carrier-protein) synthase polynucleotide phosphorylase ribosomal protein S4 and related proteins transcriptional regulator/sugar kinase superfamily II DNA and RNA helicases UDP-N-acetyl-D-mannosaminuronate dehydrogenase response regulators consisting of a CheY-like domain unknown
-3.3 -8.0 -3.0 2.0 -10.5 -19.6 -20 33.3 10 -10 50
Mean ICAT expression ratio (antibiotic-treated/control). b Proteins identified with two or more peptides.
research with prefractionated total protein samples prior to the inline 2D-LC might improve membrane proteome coverage and may allow determination of AMX sub-MIC effects on expression of Pbps. Although the known targets of AMX were not detected, subMIC of AMX had a significant effect on expression of a protein involved in biosynthesis of cell wall precursors. There was a 20-fold decrease in the expression of NagC (Table 1), a transcriptional regulator that coordinates N-acetylglucosamine metabolism.38 Specifically, NagC represses expression of the N-acetyl-D-glucosamine (GlcNac) and D-glucosamine (GlcN) catabolic enzymes encoded by the divergent nagE and nagBACD operons, and it is predominantly an activator of the UDP-N-acetylglucosamine biosynthetic operon glmUS. Decreased expression of NagC due to sub-MIC of AMX could result in decreased synthesis of UDP-N-acetyl glucosamine from GlcNac and GlcN due to glmUS operon inactivation. UDP-N-acetylglucosamine is the precursor sugar that forms the backbone of peptidoglycan in Gram-negative bacteria.39 Derepression of nagE and nagBACD operons due to lower NagC levels would channel GlcNac and GlcN into catabolic pathways. Thus, the net effect of decreased NagC expression would be reduced cell wall component availability, which could enhance the inhibition of cell wall biosynthesis by AMX. There were also significant effects on expression of biosynthetic enzymes for surface polysaccharides. Expression of PM1003, which is similar to UDP-glucose/GDP-mannose dehydrogenase and is encoded by a gene located within an apparent O antigen biosynthesis gene cluster on the Pm70 chromosome, increased 10-fold. There was a 3.3-fold reduction in the expression of phosphoenolpyruvate carboxykinase, which catalyzes the rate-limiting step in gluconeogenesis, a pathway necessary for de novo polysaccharide synthesis. One advantage of our global proteomic analysis was that it allowed us to identify effects on the expression of “nontarget” proteins involved in different biochemical pathways. AMX caused a 33-fold increase in expression of a DEAD box RNA helicase, CsdA, that participates in cold-shock adaptation in E. coli by mediating 50S subunit assembly40 and modulating mRNA processing41 at low temperature. DNA synthesis and salvage could be inhibited by AMX due to an 8-fold reduction in the expression of thymidylate synthase. AMX also has an apparent adverse effect on RNA processing and mRNA degradation due to a 10.5-fold reduction in expression of polynucleotide phosphorylase (Pnp), which is involved in the RNA degradasome in E. coli.41 Assembly of the small subunit of the ribosome could be affected due to a 20-fold reduction in the expression of ribosomal protein S4, which binds to 16S RNA and nucleates the assembly of the 30S subunit.42 576
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Expression of transcriptional regulator ArcA was reduced 10fold. ArcA represses expression of operons involved in aerobic metabolism and activates expression of anaerobic metabolism operons.43 Therefore, decreased ArcA expression would favor aerobic metabolism. In Vibrio cholerae, it modulates virulence factor expression, and an arcA mutant was attenuated.44 On the basis of E. coli genes with ArcA responsive promoters, modulation of ArcA expression could also influence cellular processes such as detoxification by superoxide dismutase, transport of sugars such as trehalose and rhamnose, degradation of long chain fatty acids, and the glyoxylate cycle.36 Chlortetracycline. The P. multocida response to sub-MIC of translational inhibitor CTC included a potential compensatory response that featured a ∼2.5-fold increase in the expression of small ribosomal subunit protein RpS12 and protein chain release factor A (Table 2), which could result in improved translational accuracy and termination efficiency. In contrast, there was an 11-fold increase in glutamyl-tRNA synthetase expression, which could result in the mischarging of glutamine to su+3 tRNATyr.45 There was also a 14-fold reduction in GidA, a protein known to modify tRNA and reduce frameshift errors,46 which could also lower translational accuracy. The P. multocida response to sub-MIC of CTC was also characterized by significant effects on expression of proteins involved in central metabolism. In particular, it appears that metabolism of pyruvate, an important intermediate of respiration, was affected by the antibiotic (Figure 4). The levels of glucose-6-phosphate isomerase and pyruvate kinase were lower by 33- and 2.4-fold, respectively. The net effect of these reductions would be lower concentrations of pyruvate due to a block in its synthesis.47 At the same time, CTC appeared to cause an increase in pyruvate conversion to formate and R-ketoglutarate, which was reflected by an 11-fold increase in the expression of pyruvate formate lyase and a 2.5 -fold increase in pyruvate dehydrogenase (AceE), respectively. On the other hand, there was a 3.8-fold increase in the expression of enolase, which could cause an increase in the synthesis of the pyruvate precursor phosphoenolpyruvate. This effect in Pm70 is comparable to the decreased expression of enolase in M. haemolytica due to 1/4 MIC of CTC that we previously reported.6 Sub-MIC of CTC could result in higher concentrations of oxaloacetate (OAA) due to a reduction in the expression of malate dehydrogenase, an enzyme that utilizes OAA as a substrate. Higher concentrations of OAA in turn inhibit the enzyme fumarate reductase (Frd).48 As a potential compensation for the inhibition of Frd, P. multocida showed 2.1- and 4.5-fold increases in the expression of the A and B catalytic subunits of fumarate reductase, respectively.
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Sub-MIC Antibiotics and Pasteurella multocida Table 2. Significant Changes in Protein Expression in Response to 1/4 MIC of CTC
a
gi no.
protein
COG
description
fold changea
15602066b 15602415b 15602760b 15603056b 15601940b 15602065b 15603350 15602968b 15603155 15603775 15602060b 15602281 15602518b 15603736b 15602180b 15603110b 15602420b 15603219b 15602393b 15602647b
FrdA Mdh AceE Ppa PflB FrdB GidA AspA MetJ OppA PepA Pgi PykA Eno SuhB PM1245 PrfA RpSL GlnS PM0782
C C C C C C D E E E E G G G G G J J J O
succinate dehydrogenase/fumarate reductase malate/lactate dehydrogenases pyruvate dehydrogenase complex, (E1) component inorganic pyrophosphatase pyruvate-formate lyase succinate dehydrogenase/fumarate reductase NAD/FAD-utilizing enzyme involved in cell division aspartate ammonia-lyase transcriptional regulator of met regulon ABC-type oligopeptide transport system leucyl aminopeptidase glucose-6-phosphate isomerase pyruvate kinase enolase archaeal fructose-1,6-bisphosphatase and related enzymes putative L-xylulose-5-phosphate 3-epimerase protein chain release factor A ribosomal protein S12 glutamyl- and glutaminyl-tRNA synthetases glutaredoxin-related protein
2.1 -3.5 2.5 2.3 11.2 4.5 -14.3 2.8 20 9.1 13.7 -33.3 -2.4 3.8 3.6 4.2 2.6 2.7 11.1 -2.8
Mean ICAT expression ratio (antibiotic-treated/control). b Proteins identified with two or more peptides.
DNA-bound gyrase, leading to the formation of an inactive ternary complex and preventing DNA supercoiling and synthesis. Accumulation of this inactive quinolone-bound intermediate with double-stranded DNA breaks halts progression of the replication fork.9 Therefore, the adaptive response to ENR treatment could be predicted to entail recruitment of the DNA damage repair machinery to overcome this block.
Figure 4. Pyruvate metabolism in response to 1/4 MIC of CTC. Solid arrows represent steps where expression of the enzyme was increased; broken arrows indicate lowered expression. Enzymes in the pathway that were not represented in our dataset are shown with a single line, and a broken line is indicative of multiple enzymes in a pathway that were not detected in our study.
Other potential effects on central metabolism due to subMIC of CTC occurred. CTC caused a 14-fold increase in the expression of leucyl aminopeptidase, which maintains the overall efficiency of intracellular protein processing and turnover.49 There was also a potential increase in the uptake of oligopeptides, as reflected by a 9-fold increase in the expression of the oligopeptide transporter OppA.50 The P. multocida response to sub-MIC of CTC appeared to involve a potential thermodynamic pull for the synthesis of proteins and nucleic acids due to a 2.3-fold increase in the expression of inorganic pyrophosphatase.51 The ability of P. multocida to either utilize or synthesize glucose was apparently impaired, as the expression of glucose-6-phosphate isomerase was reduced 33-fold. Interconversion of aldoses to ketoses could be increased due to a 4-fold increase in expression of L-xylulose-5-phosphate 3-epimerase.52 Enrofloxacin. DNA gyrase introduces negative supercoils in closed circular double-stranded DNA in an ATP-dependent manner and is required for replication. It can catalyze the interconversion of double-stranded DNA concatamers and knotted rings. Quinolones, including ENR, covalently bind to
Our results support this hypothesized adaptive response: sub-MIC ENR-treated Pm70 showed a 13-fold increase in the expression of recombinase A (RecA) (Table 3). RecA is a multifunctional enzyme that catalyzes DNA strand exchange during homologous recombination, a process that is also used for double-stranded DNA break repair. RecA is also a part of the SOS response to massive DNA damage.53 It binds to singlestranded DNA regions of stalled replication and allows for the repair of damaged DNA. Overexpression of RecA could overcome the quinolone-mediated DNA replication block by doublestrand DNA break repair. ENR had other varied effects on nucleotide synthesis and metabolism. The salvage of nucleosides and nucleotides could be impaired due to a 4.7-fold decrease in the expression of purine nucleoside phosphorylase.54 The R-subunit of RNA polymerase (RpoA) initiates the assembly of RNA polymerase and plays a role in promoter recognition during transcription. RpoA expression increased 1.5-fold, which could increase the levels of messenger RNA. In addition, RNA degradasomemediated degradation of messenger RNAs could be impaired as a result of a 5.5-fold reduction in the expression of oligoribonuclease, which degrades small RNAs to mononucleotides.55 ENR appeared to have adverse effects on translation, including a 3.8-fold reduction in the expression of small subunit ribosomal protein S13 (RpS13) and a 2.4-fold reduction in protein chain release factor A (PrfA). RpS13 is involved in the initiation of translation as it binds to fMet-tRNA, and PrfA is necessary for efficient termination of protein synthesis.56 There was a 3.8-fold reduction in the expression of RNAse PH, which modulates transfer RNA processing and could affect translation.57 These effects could explain the decrease in growth kinetics due to 1/4 MIC of ENR. There was a 100-fold reduction in phosphomannose isomerase (Pgi) expression, which could severely impair P. multocida’s Journal of Proteome Research • Vol. 5, No. 3, 2006 577
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Table 3. Significant Effects of 1/4 MIC of ENR on Pm70 Protein Expression gi no.
protein
COG
description
fold changea
15602765 15602066b 15602184b 15602758b 15603318b 15603156b 15602281b 15603467b 15602694 15602204b 15602420b 15603007b 15603258b 15603270b 15603741b 15603255b 15603682 15603739 15603663
PM0900 FrdA IscU LpdA Adh2 DeoD Pgi Tal_1 Pmi FabB PrfA RpmE RpSM RpLN Rph RpoA RecA PM1874 unknown
A C C C C F G G G I J J J J J K L R -
oligoribonuclease (3′ f 5′ exoribonuclease Orn) succinate dehydrogenase/fumarate reductase NifU homologue involved in Fe-S cluster formation pyruvate/2-oxoglutarate dehydrogenase complex, (E3) component NAD-dependent aldehyde dehydrogenases purine-nucleoside phosphorylase glucose-6-phosphate isomerase transaldolase phosphomannose isomerase 3-oxoacyl-(acyl-carrier-protein) synthase protein chain release factor A ribosomal protein L31 ribosomal protein S13 ribosomal protein L14 RNase PH DNA-directed RNA polymerase, R-subunit/40 kD subunit recombinase A predicted aminomethyltransferase related to GcvT unknown
-6.0 5.7 3.8 2.1 -2.1 -4.7 -36.6 1.8 -109.9 1.6 -2.5 -2.2 -3.8 2.0 -3.9 1.6 12.9 -9.2 15.2
a
Mean ICAT expression ratio (antibiotic-treated/control). b Proteins identified with two or more peptides.
ability to utilize mannose as a carbon source. Pgi catalyzes mannose-6-phosphate conversion to fructose-6-phosphate to allow entry into glycolysis. Global Effects versus Individual Protein Effects. Many of the effects of sub-MIC antibiotics on individual protein expression were opposite to the effects on protein expression at the COG level. This demonstrates the need for, and the power of, increasing the granularity of the data analysis in large functional genomics datasets. Analysis at the COG level provided us a global overview of the effects of antibiotic sub-MICs on bacterial physiology. However, analyzing each protein individually highlights specific, underlying checkpoints in the physiological pathways that may be affected by the antibiotic sub-MIC or altered by the bacteria in response to treatment. For example, we identified an overall suppressive effect on COG category C (energy production and conversion) for both CTC and ENR (Figure 3). In contrast, several individual proteins that are in COG category C were significantly upregulated in response to these antibiotics (Tables 2 and 3). This indicates that certain proteins in this category are involved in a compensatory response to the overall suppressive effects on energy metabolism. These individual proteins are obvious targets for novel chemotherapeutic interventions.
Conclusions The lethal effects of antibiotics in bacteria are due to binding to specific targets in the bacterial cell, including Pbps (AMX), ribosomal small subunit proteins (CTC), or DNA gyrase (ENR). However, our work demonstrates that antibiotics cause secondary or “nontarget” effects in addition to these primary target effects. Some of these secondary effects, such as the effect of AMX on NagC, may enhance the primary activity of the antibiotic, while others, such as the increased expression of RecA in response to ENR, may be a bacterial compensatory mechanism against the antibiotic. However, the reason for many of the effects, such as the AMX effect on ArcA to favor anaerobic metabolism, cannot be immediately explained and suggests gaps in our current knowledge. All therapeutic antibiotics are derived from naturally occurring chemicals; thus, bacteria are exposed to low levels of natural antibiotics in the environment. It is logical that bacteria have physiological mechanisms to cope with the effects of 578
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antibiotics. It is likely that the effects that we have identified, including the general upregulation of transcriptional and nucleic acid metabolism proteins (COGs F and K) and individual protein effects, represent such compensatory mechanisms. The development of novel drugs targeting these compensatory mechanisms may allow the development of combination therapies that would be effective against bacterial strains that are resistant to antibiotics alone. Abbreviations: MIC, minimum inhibitory concentration; CTC, chlortetracycline; AMX, amoxicillin; ENR, enrofloxacin; BHI, brain heart infusion; COG, clusters of orthologous groups; NCBI, National Center for Biotechnology Institute; BRD, bovine respiratory disease; BLAST, basic local alignment search tool; ANOVA, analysis of variance; ICAT, isotope-coded affinity tag; ACN, acetonitrile; 2D, two-dimensional; LC, liquid chromatography; ESI, electrospray ionization; MS, mass spectrometry; MS2, tandem MS; Pbp, penicillin binding protein.
Acknowledgment. This work was supported by a competitive grant from the Mississippi State University Life Sciences and Biotechnology Institute (LSBI). We acknowledge Dr. Vivek Kapur for providing P. multocida Pm70 strain, Dr. Daniel Scruggs for valuable discussions, and Tibor Pechan for technical assistance in mass spectrometry. This paper is Mississippi Agricultural and Forestry Experiment Station publication J10566. Supporting Information Available: Table listing the proteins that were identified but did not show a significant quantitative change in response to sub-MICs of AMX, CTC, or ENR. This material is available free of charge via the Internet at http://pubs.acs.org. References (1) Rapp, R. P. Antimicrobial resistance in gram-positive bacteria: the myth of the MIC. Minimum inhibitory concentration. Pharmacotherapy 1999, 19, (8 Pt 2), 112S-119S; discussion 133S-137S. (2) Weber, J. T.; Courvalin, P. An emptying quiver: antimicrobial drugs and resistance. Emerging Infect. Dis. 2005, 11(6), 791-793. (3) Gilligan, P. H. Therapeutic challenges posed by bacterial bioterrorism threats. Curr. Opin. Microbiol. 2002, 5(5), 489-495. (4) Gaskins, H. R.; Collier, C. T.; Anderson, D. B. Antibiotics as growth promotants: mode of action. Anim. Biotechnol. 2002, 13(1), 2942.
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PR050360R
