Quantitative Proteomic Analysis of Drug-Induced Changes in

Mycobacteria

Minerva A. Hughes,† Jeffrey C. Silva,‡ Scott J. Geromanos,‡ and Craig A. Townsend*,†

Department of Chemistry, The Johns Hopkins University, 3400 North Charles Street,Baltimore Maryland 21218, and Waters Corporation, 34 Maple Street, Milford Massachusetts 01757-3696

Received July 29, 2005

A new approach for qualitative and quantitative proteomic analysis using capillary liquid chromatog-raphy and mass spectrometry to study the protein expression response in mycobacteria followingisoniazid treatment is discussed. In keeping with known effects on the fatty acid synthase II pathway,proteins encoded by the kas operon (AcpM, KasA, KasB, Accd6) were significantly overexpressed, aswere those involved in iron metabolism and cell division suggesting a complex interplay of metabolicevents leading to cell death.

Keywords: isoniazid • tuberculosis • protein profiling • liquid chromatography • mass spectrometry

Introduction

Tuberculosis (TB) continues to be a major cause of diseaseand mortality with an estimated 2 million deaths annually.1

Approximately one-third of the world’s population is thoughtto be infected with Mycobacterium tuberculosis, the etiologicalagent of TB. Despite the reported success of directly observedtreatments, short course (DOTS), noncompliance is one of thefactors leading to the emergence of multi-drug resistant strains(MDR-TB). MDR-TB refers to those strains resistant to two ormore of the five first line anti-TB drugs (isoniazid, rifampin,pyrazinamide, ethambutol, and streptomycin). Patients infectedwith an MDR-TB strain have a mortality rate of 60-90%,equivalent to those without treatment.2 The matter is furthercomplicated by a rapid progression of the disease and increasedrisk of reactivation of latent TB in individuals co-infected withHuman Immunodeficiency Virus (HIV).3 The growing globalburden of TB creates an urgent need to define new classes oftherapeutics effective against MDR strains and with improvedsterilizing activity.

An incomplete understanding of the physiology of myco-bacteria and the mechanisms associated with drug sensitivityhas been the greatest impediment toward significant advancesin drug-development. Recently the completed genome se-quences for M. tuberculosis H37Rv and other mycobacterialstrains have become available.4-6 They have made it possibleto apply existing bioinformatic, genomic, and proteomictechniques to obtain a better understanding of the pathophysi-ology of mycobacteria as well as pathways of heightenedsensitivity exploitable for rational drug-discovery efforts. Forexample, comparative genomic studies using DNA microarraytechnology have identified ‘core’ genes of the M. tuberculosiscomplex that could provide highly selective drug targets.7

Additional gene expression profiling studies offered furtherinsights into the metabolism of M. tuberculosis defining adap-tive responses to intracellular phagocytosis,8 heat shock,9

oxidative stress,10 nutrient depletion,11 and responses to variouschemotherapeutics.12

Though informative, genomic analysis alone provides onlya limited view of the dynamics associated with cellular re-sponses to a particular stimulus or at steady state. A systemicanalysis of the proteomic and metabolic fluctuations is neededto complement existing genomic studies. Compared to mRNAstudies, proteomic analysis provides a more accurate assess-ment of the conditional changes because the measurementfocuses on the functionally relevant species. Furthermore, adirect correlation between mRNA expression and changes inthe protein population at either steady-state or in response toa stimulus does not exist due in part to post-translationalcontrol mechanisms.13 Since proteins are the target for mostdrugs, our understanding of drug-related responses at the levelof the proteome will undoubtedly unravel the importantdynamics of a drug’s mechanism of action and define newpathways for drug discovery. While our primary interests arein understanding drug related effects in mycobacteria, themethodology applied in this study is applicable to studying awide array of adaptive responses.

Traditionally, quantitative proteomics relied on the resolvingpower of two-dimensional gel electrophoresis coupled withmass spectrometry (2DE-MS) for qualitative and quantitativeprotein identification. Despite its popularity, the accuracy ofprotein quantitation using this method can be ambiguous dueto post-translational modifications resulting in multiple spotsfor a single protein or multiple proteins in a single spot, proteindegradation, presence of protein isoforms and variability inprotein recovery from in-gel digests. To compensate for thelimitations of 2DE-MS, standardized gel-free methods havebeen developed. They involve the combination of stable-isotope labeling during sample preparation coupled with

* To whom correspondence should be addressed. Tel: (410) 516-7444.Fax: (410) 261-1233. E-mail: ctownsend@jhu.edu.

† Department of Chemistry, The Johns Hopkins University.‡ Waters Corporation, Milford, MA.

54 Journal of Proteome Research 2006, 5, 54-63 10.1021/pr050248t CCC: $33.50 2006 American Chemical SocietyPublished on Web 12/02/2005

automated liquid chromatography (LC) and subsequent massspectrometry (MS) and/or tandem mass spectrometry (MS/MS). Labels are introduced either by chemical modification,enzymatic derivatization, or metabolic labeling.14,15 Of thesetechniques, the isotope-coded affinity tag (ICAT) strategy hasachieved some popularity.16,17 Although these labeling strategieshave provided an alternative to 2DE-MS, they require severalsteps for sample preparation. Methods that entail multiple sam-ple preparation steps can lead to an increase in the quantitativevariability and decrease the accuracy of the experiment.

In this study, we apply a simple, gel-free, label-free approachto qualitative and quantitative proteomic analyses using LC-MS.18 In light of well-defined biochemical19-24 and supportinggenomic12,25-29 data, we chose to examine the protein expres-sion profile of M. bovis var. BCG (a member of the M.tuberculosis complex) in response to isoniazid (INH) treatmentas a model system. The choice of INH for this study allows fora robust cross-validation of this new LC-MS method to itsextensive biochemistry of action, and provides a vital compo-nent to the comprehensive analysis of the drug’s mechanismof action. Though widely accepted as a cell wall biosynthesisinhibitor targeting enzymes of the fatty acid synthase (FAS) typeII system, the complete cellular dynamics surrounding INHtoxicity are still unclear and remain a topic of active research.

Experimental ProceduresM. bovis BCG Growth Conditions and Protein Expression.

Mycobacterium bovis BCG (Pasteur strain, ATCC 35734) wasgrown to mid-log phase OD600 0.4-0.5 in Middlebrook 7H9 me-dium supplemented with 10% ADC (0.5% BSA, 0.2% dextrose,0.085% NaCl), 0.025% Tween 80. One liter cultures were treatedwith 0.4 mL diluent (DMSO) or INH (1 µg/mL final) andincubated at 37 °C. At 6 h post addition, the cells were collectedby centrifugation, washed with PBS (1×), ambic buffer (50 mMNH4HCO3, 5 mM EDTA) (1×) and suspended in lysis buffer [50mM NH4HCO3, 5 mM EDTA, 0.05% RapiGest (Waters Corpora-tion, Milford, MA)]. An equal volume of 0.1 mm zirconia/silicabeads was added to the bacteria suspension and the cells weredisrupted with a Mini Bead-beater 8 (BioSpec Bartlesville, OK).Protein concentrations were estimated using the One Plus 2Dquantitation kit (Amersham Biosciences, Piscataway, NJ).

Pre-fractionation with Ammonium Sulfate. Crude lysatesof M. bovis BCG containing approximately 50 mg of totalprotein per condition were fractionated into five portions usingammonium sulfate according to established protocols: 0-30%,30-40%, 40-45%, 45-50%, and 50-90% cuts. The concentra-tions used to fractionate the proteins were chosen to produceapproximately the same size protein pellet. The resulting pelletswere suspended in the minimal amount of buffer for completeresolubilization (50 mM NH4HCO3, 5 mM EDTA, 1 M urea) anddialyzed overnight against the same buffer. Protein concentra-tions were estimated using the Coomassie Plus Protein Assayreagent (Pierce, Rockford, Il).

Liquid Chromatography and Mass Spectrometry. Aliquotsof M. bovis BCG lysates containing approximately 200 µg totalprotein were reduced in the presence of 5 mM DTT at 60 °Cfor 30 min followed by alkylation with 15 mM iodoacetamidefor 30 min in the dark at room temperature. Proteolyticdigestion was initiated with the addition of modified trypsin(Sequencing Grade, Promega, Madison, WI) in an equal volumeof ambic buffer to a final concentration of 1:75 (w/w trypsin:total protein) and incubated at 37 °C overnight. An equalvolume of 50 mM NH4HCO3 was added to reduce the concen-tration of RapiGest to 0.025%.

LC-MS data sets were acquired with a Waters CapLC/WatersCapLC autosampler equipped with a Waters NanoEase AtlantisC18, 300 µm × 15 cm reversed-phase column configured ontoa modified Waters/Micromass Q-TOF Ultima API. Chromatog-raphy was performed using an aqueous mobile phase (mobilephase A) containing 1% acetonitrile in water with 0.1% formicacid and an organic mobile phase (mobile phase B) containing80% acetonitrile in water with 0.1% formic acid. Peptides wereloaded onto the column with 6% mobile phase B and elutedwith a gradient of 6-40% mobile phase B over 100 min at 4.4µL/min followed by a 10 min rinse with 99% mobile phase Bthen equilibration with 6% mobile phase B for 20 min. Massspectrometry was performed using positive mode ESI fittedwith a NanoLockSpray source. The mass spectrometer wascalibrated with a [Glu-1]-fibrinopeptide (GFP) solution (100fmol/µL) delivered through the reference sprayer of theNanoLockSpray source. The doubly charged ion [(M + 2H)2+]was used for initial single point calibration (Lteff), and MSE

fragment ions of GFP were used to obtain the final instrumentcalibration. Full scan mass spectra were acquired from 300 to2000 m/z at a frequency of 1.8 s with an interscan delay timeof 0.2 s. Accurate mass LC-MS and LC-MSE data werecollected using 10 eV for MS and 28-35 eV for MSE such thatone cycle of MS and MSE data was acquired every 4.0 s.30

Peptide Clustering and Data Normalization. Each samplewas analyzed in triplicate. Data were acquired in a continuousfashion by alternating low and elevated energies as prescribedby the Waters Protein Expression System (PLGS v2.2 build 39,Waters Corporation). All ion detections from both the low andelevated energy channels have been de-isotoped and charge-state reduced. Coincident fragmentation data were assignedto each detected precursor peptide obtained from the lowenergy channel by aligning the ion detections from the elevatedenergy channel (y-, b-ions, neutral losses, immonium ions) witha retention time tolerance of approximately (0.05 min. Identi-cal peptide components from each of the replicate injectionsfor both conditions were clustered together by mass precision(typically <10 ppm) and a retention time tolerance (typically<0.25 min) using the PLGS software. The clustered data setwas exported from PLGS and further evaluated with Excel andSpotfire. Ion detections with a replication rate of one wereconsidered noise and discarded. The observed intensity mea-surements were adjusted for injection variability within eachcondition and also for variation in protein load between eachcondition based on those components which replicated through-out the entire experiment (6 out of 6 injections). The summedintensity measurements (within one standard deviation) wereused to generate the appropriate scaling factors to adjust forinjection variability and protein load, respectively.31

Protein Identification and Quantitation. Processed ionswere sequenced mapped against the M. bovis database (NCBI:NC 002945) using PLGS and MASCOT V1.9 (Matrix Science,Boston, MA). The PLGS search parameters were defined by thesoftware (automatic setting used). Peptides were restricted totrypsin fragments with up to one missed cleavage and cysteinecarbamidomethylation. The MASCOT search parameters wererestricted to tryptic peptides (up to one missed cleavage andcysteine carbamidomethylation) with a mass tolerance of (25ppm and fragments within a mass tolerance of (0.03 Da. Allprotein identifications were manually assessed for fidelity.

The relative quantitation was performed using the precursorintensity measurements available in the clustered output file.The redundant quantitative measurements provided by the

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multiple tryptic peptides from each protein were used todetermine an average relative fold-change. A 95% confidenceinterval was determined for each average fold-change from thestandard deviation of the observed quantitative measurementsand the total number of observed tryptic peptides.

Results

Sample Complexity and Ion Detection. Standard 2-DE wasperformed on cell-free extracts of control and INH-treatedcultures to assess the protein population using an MS-friendlybuffer system and to illustrate how INH treatment affected theprotein profile of the model bacterium, Figure S1 in SupportingInformation. Tryptic peptides generated for each sample wereanalyzed by LC-MS/MSE in triplicate. Precursor ions and theirassociated fragment ions were obtained in a continuousfashion, alternating low and elevated energies, and alignedbased on their chromatographic attributes. The inventory ofdetected peptide components was converted to a text file con-taining all of the mass spectrometric and retention time data,easily importable into external programs for data quality assess-ment. Each detected component is referred to as an AMRT(accurate-mass, retention time pair). An appreciation for thecomplexity of the sample is readily obtained by visualizing thetotal number of extracted accurate-mass-retention-time-pairs,(AMRTs) detected across both conditions, Figure 1.

After obtaining an inventory of the detected peptide com-ponents from the replicates of each sample, the individual

peptide component lists were assembled into a single matrixsuch that each peptide component was associated across theentire experiment. The ion detections which occurred once outof the six injections were considered background noise andremoved. These discarded components corresponded to 7.8-13.5% of the total number of detected peptide components,but only represent 1.5-3.0% of the total detected intensity foreach replicate injection, Table 1. The peptide detection ef-ficiency (defined as those peptide components which replicatedat least two out of the three replicate injections for eithersample) was 70%, constituting the majority of detected AMRTs.More importantly, these components accounted for 98% of thetotal detected intensity from each condition.

Data Quality Assessment. Before conducting quantitativecomparisons between the two conditions, the observed inten-sity measurements were normalized for injection (volume) andsample (protein load) variability by multiplying each intensitymeasurement by the appropriate scaling factor obtained asoutlined in the Experimental Procedures. This process wasperformed for both conditions. The scaling factors used for thereplicate injections of the control sample were 1.0243, 1.0005and 1.0000, respectively. The scaling factors used for thereplicate injections of the INH-treated sample were 1.0083,1.0000, and 1.0094, respectively. These results indicate that amaximum deviation of 2.5% was observed during the replicateanalysis of these two conditions, which is within the acceptedanalytical deviation of the auto injector. The scaling factors

Figure 1. LC-MS/MSE analysis of control and INH-treated BCG cultures. (A) LC-MS/MSE analysis of a tryptic digest of soluble proteinsfrom the control (left) and INH-treated (right) taken at a timepoint of 6 h. The base peak intensity chromatograms for the tryptic precursorsand associated fragment ions are provided in each of the two acquisition channels [low energy (top, LC-MS) and elevated energy(bottom, LC-MSE)]; (B) An overlaid scatter plot of the monoisotopic mass (MH+) and retention time measurements from the replicatesof each condition detected throughout the entire LC-MS/MSE analysis and within a restricted mass and retention time window.

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used to normalize against the total protein load for the controland the INH sample were 1.0337 and 1.0000, respectively.

A variety of quality control measures was performed on thereplicates of each condition to determine the reproducibilityof the analytical method. The mass precision of the extractedpeptide components was typically within (5 ppm of the meanmass measurement. These data are illustrated in Figure 2A anddemonstrate the stability of the mass measurement instru-mentation. The variability of the quantitative intensity mea-surements among the replicate injections is summarized inFigure 2B. These results indicate that the average and mediancoefficient of variation (Cv) among the replicate injections was11.7% and 8.2%, respectively. Figure 2C illustrates the repro-ducibility of the chromatography collected during this study.The average and median Cv was 0.3% and 0.1%, respectively.These observations are within the typical measurements ob-tained from previous reports with the same instrument andan earlier version of the software.18

The intensity variation observed with the analytical methodcan be appreciated by conducting binary comparisons of theintensity measurements of the matched peptide componentsfor each replicate injection. Figure 3 illustrates the scatter plotof the resulting three binary comparisons for each condition.Under ideal conditions, the binary comparisons would yield aperfect diagonal line that intersected through zero and woulddisplay a minimum degree of deviation throughout the signaldetection range. The data do illustrate a diagonal line wherethe minimum deviation between matched peptide componentsis seen with peptide components of higher intensity. This plotalso illustrates what one would expect to see if there were noapparent changes between the two conditions.

Quantitative Analysis and Protein Identification. Quantita-tive analysis was performed using the same set of experiments.Figure 4A illustrates the binary comparison for the averageintensity measurements between control and INH-treatedreplicate injections. The average standard deviation associatedwith the relative intensity ratio of matched peptide componentsbetween the control and INH-treated was approximately 0.53,roughly two-times that observed from internal pairwise com-parisons of the analytical replicates (Figure 3). The naturallogarithm of the ratio of the average intensities from eachcondition, INH-treated (numerator) versus control (denomina-tor), was determined and plotted against the average intensityof the 6699 matched peptide components between the INH-

treated and control samples, Figure 4B. Once the matchedpeptide components were plotted according to their observedrelative fold-change, the quantitative comparison of the matchedpeptide components provided a useful filter to identify peptidecomponents of interest. A set of peptides within a determinedfold-change, and within a specified measurement tolerance,as determined by the variability of the analytical method,should originate from a limited subset of the proteins in thenatural proteome. In essence, the filter allows one to performa more restrictive peptide-mass-fingerprint (PMF) search witha reduced subset of peptide components. The coupling of therelative quantitation along with the PMF search will be referredto as a Q-PMF search (quantitative-peptide-mass-fingerprint)throughout this manuscript.

Three sets of peptide components have been highlighted toillustrate the utility of the Q-PMF methodology, Figure 4B.There are four red peptide components which exhibit a ln-(minimum) and ln(maximum) fold change of 0.80 and 1.04,respectively. A total of 204 peptide components lie within thisobserved fold-change range. When the subset of 204 peptidecomponents is submitted for a Q-PMF search against the M.bovis protein database without allowing for any missed cleav-ages, the search results indicate that the 4 red peptidecomponents match to AcpM (meromycolate extension acylcarrier protein), Rv2244, to within a 10 ppm mass toleranceand comprise 43% total protein sequence coverage. Each ofthe four peptide components has been validated with support-ing sequence information from the associated fragment ionscollected in the alternating elevated energy acquisitions. TheSupporting Information for the IPDEDLAGLR peptide (1098.5795MH+) can be seen in Figure 5. The four peptide identificationsto AcpM illustrate an average fold-change of 2.41 with astandard deviation of 0.27, which is within the establishedanalytical variability. Eight peptides from a subset of 384 wereidentified with Icl (isocitrate lyase), Rv0467, to within a 10 ppmmass tolerance and comprise 20% total protein sequencecoverage. Seven peptides from a subset of 563 were identifiedwith Ino1 (myo-inositol-1-phosphate synthase) to within a 10ppm mass tolerance and comprise 18% total protein sequencecoverage. The scatter plot in Figure 4C has converted theredundant quantitative peptide information for the 103 char-acterized proteins to summarize the overall effect of the proteinin a single average measurement with the appropriate 95%confidence interval as dictated by the number of individual

Table 1. Summary of Extracted Peptide Components and Peptide Detection Efficiency

AMRT inventorya

Control Isoniazid-treatment

inj 1 inj 2 inj 3 Cv inj 1 inj 2 inj 3 Cv

total AMRTs 7387 7081 7337 2.1% 7192 7101 7145 3.8%s•intensity 4.12 × 107 4.19 × 107 4.25 × 107 1.5% 5.10 × 107 5.06 × 107 5.04 × 107 0.6%

Peptide Detection Efficiency

rep rate 1 2 3 1 2 3total AMRTs 3276 1827 5840 3292 1889 5654f•peptides 29.9% 16.7% 53.4% 30.4% 17.4% 52.2%s•intensity 9.96 × 105 2.53 × 106 4.04× 107 1.12 × 106 2.99 × 106 4.74 × 107

f•intensity 2.3% 5.8% 92.0% 2.2% 5.8% 92.0%

a Ion detections filtered for a replication rate of 2-6 across both conditions and intensities have been normalized to account for injection variability withineach sample and protein load between both conditions; abbreviations: s•intensity, summed intensity of the peptide components associated with the indicatedinjection or replication rate; rep rate, number of times an AMRT was detected for each condition; f•peptides, fraction (%) of peptides associated with theindicated replication rate; f•intensity, fraction (%) of intensity associated with the indicated replication rate; Cv, coefficient of variance.

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peptide measurements. The natural logarithm of the averagerelative fold-change for AcpM, Icl and Ino1 were 0.91, 0.69, and0.38, with a 95% confidence interval of (0.11, (0.07, and (0.05,respectively. A differential expression of (-0.39) g ln(average

relative fold change) g (0.39) was the criteria used for an AMRTto be considered significantly differentially expressed.

Validation by Fractionation Enrichment. The results ob-tained from the un-fractionated M. bovis BCG extracts provideda list of protein identifications based on corresponding peptidefragments validated not only by Q-PMF, but also by supportingprimary sequence coverage from the data obtained in theelevated energy channel. To further validate those peptide/protein identifications which contained Q-PMF data withoutsufficient supporting sequence information, the original proteinextracts were fractionated by ammonium sulfate precipitationto produce five protein fractions as outlined in the Experimen-tal Procedures. Each of the five fractions was analyzed by LC-MS/MSE in triplicate and the data processed as previouslydescribed. By simplifying the protein mixture and reducing thecomplexity and dynamic range of the samples, increasedpeptide coverage was obtained for those protein identificationsmade with only Q-PMF data. To cross-correlate the processedLC-MS and LC-MSE data from the fractionated samples withthe un-fractionated data set, a database containing the mass,retention time, and intensity measurement for each sequence-validated tryptic peptide along with the associated proteinidentification for the fractionated samples was created (proteinion maps). The ionization efficiencies of the characterizedtryptic peptides also allowed us to predict which subset ofpeptides was expected to ionize at low levels in the un-fractionated fractions. The protein ion maps added additionalvalidation for those peptides/proteins identified in the un-fractionated samples which were not of sufficient abundanceto produce adequate sequence information.

A subset of peptides is indicated in Table 2 with theircorresponding MASCOT scores before and after fractionationto illustrate the utility of the fractionation protocol. TheMASCOT search results from the un-fractionated controlprovided 21proteins with a MASCOT score at or above 50, whilethe results from the un-fractionated INH-treated sampleprovided 32 proteins with a MASCOT score at or above 50. Afterconsolidating the search results from the fractioned controlsample, a total of 177 nonredundant proteins were identifiedwith a MASCOT score at or above 50; 91 of the 177 proteinidentifications were obtained with a protein score at or above100. Similarly, the consolidated MASCOT search results fromthe fractionated INH-treated samples provided a total of 154nonredundant protein identifications with a protein score ator above 50; 88 of the 154 protein identifications were obtainedwith a protein score at or above 100.

Discussion

In this study, we sought to validate a newly developed LC-MS method capable of simultaneously providing qualitative andquantitative information to study differential protein expressionwithin the complex mycobacterial proteome following INHtreatment. Using an MS-friendly buffer system, the solubleprotein extracts were digested and loaded directly onto an LCcolumn to minimize losses due to sample manipulation,precipitation and/or fractionation, simplifying the overallsample preparation process. Only proteins identified underconditions of high stringency compiled for at least two out ofthree parallel analyses and by at least three sequenced peptidefragments were considered positive identifications. The lowanalytical variability associated with the mass and retentiontime measurements of the detected replicating AMRTs sup-ported the efficient clustering of identical peptide components.

Figure 2. Assessment of the analytical reproducibility. (A) Themass precision measurements from the 9903 replicating AMRTs(in at least 2 out of 6 injections) across the entire experiment.The median and average mass errors were (2.9 and (3.4 ppm,respectively; (B) Error distribution associated with the intensitymeasurements for the 7543 replicating AMRTs detected in theINH-treated BCG sample (similar profile observed for the control).The median and average intensity errors were 11.7% and 8.2%,respectively. (C) The relative standard deviation of the 9903replicating AMRTs detected for the entire experiment. Themedian and average retention time error was 0.1% and 0.3%,respectively.

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For those low abundant peptide components incapable ofgenerating sufficient fragmentation data with the instrumenta-tion used in this study, the quantitative comparison allowedfor a more stringent PMF search for subsequent peptide/protein identifications by restricting the number of AMRTs inany given search. The quantitative analysis did not incorporateany labeling or enrichment strategy, which is an ideal approachto minimize sample variation and allow for multiple (more thantwo) conditions to be cross correlated (e.g. time courses, drugconcentrations, different antibiotics).

Although a PMF protein identification strategy was used forprecursor ions of insufficient intensity to produce fragmentions, supporting structural validation was obtained by parti-tioning the crude protein mixture into various fractions, therebyreducing the complexity and dynamic range of the proteinswithin each analytical sample. Using a similar strategy to Smith

et al.,31 the ion inventory of accurate mass measurements forthe precursors and associated fragment ions obtained from asingle LC-MS/MSE analysis was used to construct a morecomprehensive list of protein identifications present in the un-fractionated sample. These protein results were used to gener-ate protein ion maps that were compared with the Q-PMFidentifications obtained from the un-fractionated experimentsin order to validate the identifications with supporting frag-mentation data. Though similar to the approach of Smith etal.,31 the method in this study uses a single experiment toprovide the qualitative and quantitative information neededto assemble the protein ion maps as opposed to performingthe analysis on two different instruments.

A total of 103 proteins were confidently identified from 956of the total 6699 matched peptide components, 14%, based onthe stringent search parameters employed. Common modifica-

Figure 3. Comparison of the log intensity measurements obtained for each matched set of AMRTs from each of the replicate injectionsfor the control: (A) injection 1 vs 2; (B) injection 1 vs 3; (C) injection 2 vs 3. The standard deviations associated with the relativeintensity ratios of matched AMRTs were 0.25, 0.27, and 0.26, respectively. Comparison of the log intensity measurements obtained foreach matched set of AMRTs from each of the replicated injections for the INH-treated cultures: (D) injection 1 vs 2; (E) injection 1 vs3; (F) injection 2 vs 3. The standard deviations associated with the relative intensity ratio for the matched AMRTs were 0.27, 0.28, and0.25, respectively.

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tions such as glycosylation, acetylation, methylation, phospho-rylation and methionine oxidation were not considered ascriteria for the characterized protein identifications. Since atotal inventory of precursors and fragments was generated, theremaining AMRTs are available with their correspondingfragmentation data to pursue additional identifications in thefuture. The proteins deemed to show significant differentialexpression support the extensively characterized biochemicaleffects of mycobacteria following INH exposure, Table 3.

Lipid Metabolism. Four of the five proteins encoded by thekas operon, (FabD-AcpM-KasA-KasB-AccD6), were identified

with Accd6 exhibiting the largest deviation with a ln(I/C) ( 95%CI of 1.41 ( 0.23 (4.1-fold). Previous proteomic analysis using2DE-MS only identified two of the five proteins involved inthis operon (AcpM and KasA).21 These enzymes are involvedin the synthesis of mycolic acids through a type II FAS system.Mycolic acids are R-alkyl, â-hydroxy acids comprising anywherefrom 30 to 60% dry weight of the cell and can range in lengthbetween C70-C90. Differential mRNA analysis and DNA mi-croarray studies have pointed to this gene cluster as a diag-nostic response for drugs exerting a primary affect on fatty acidbiosynthesis in mycobacteria.12

Additional proteins involved in fatty acid biosynthesis show-ing differential expression include FadD26 and DesA2. FadD26shows homology to fatty acid CoA ligases and is up-regulated1.5-fold with INH treatment, a property consistent with ge-nomic data.28 DesA2 is a potential desaturase possibly involvedin the synthesis of mycolic acids. M. tuberculosis encodes forthree potential desaturases DesA1, DesA2, DesA3. Biochemicalstudies have shown that desA3 is involved in the synthesis ofoleic acid (cis-∆9-C18:1) and is likely not involved in modificationof mycolic acids.33 DesA2 is down-regulated during INH treat-ment both at the level of the protein, -1.5 fold, and transcript.28

The known inhibition of mycolic acid synthesis by INH seemsto suggest a functional role for DesA2 in the biosyntheticpathway.

Intermediary Metabolism and Respiration. The metabolicswitch isocitrate lyase, Icl, was upregulated following exposure

Figure 4. Relative quantitation. (A) Binary comparisons of thelog intensity measurements obtained from the 6699 matchedAMRTs for INH-treatment vs control. (B) The natural log of theintensities (INH-treated vs control) plotted against the natural logof the replicating ions detected in the INH-treated sample.Colored peptides illustrate the redundancy associated withsubsequent protein identifications: red, AcpM (4 peptides, 43%sequence coverage); blue: Icl (8 peptides, 20% sequence cover-age); green, Ino1 (7 peptides, 18% sequence coverage). (C)Summarized overall effect for the 103 proteins identified in thisstudy as a single average measurement with the appropriate 95%confidence interval: red, up; green, down; gray, slight to nochange.

Figure 5. LC-MS/MSE identification of AcpM. (A) Illustration ofthe chromatographic alignment for the precursor and fourassociated fragment ions to the IPDEDLAGLR peptide to AcpMobtained during the LC-MS/MSE acquisition. Five selected ionchromatograms from the raw continuum data of the precursorand four consecutive y-ions indicate that the apex retention timesare within a single scan (0.03 min) of the originating precursor;(B) The time resolved, de-isotoped and charge-state reducedfragmentation data associated with the precursor mass of1098.5873 (MH+) at 46.00 min. The corresponding y-,b-ions andneutral losses associated with the IPDEDLAGLR peptide of AcpMare highlighted in the MSE spectrum.

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to INH. Icl is the branch point between the Krebs and glyoxylatecycles. Activation of Icl switches on the glyoxylate cycle yieldingsuccinate and glyoxylate from isocitrate. In M. tuberculosis, iclis up-regulated in response to phagasomal uptake8 and growthon fatty acids.34 It is required for intracellular survival andpersistence in mice.35 Unlike the E. coli system, in which Iclrequires induction based on the available carbon sources, aconstitutive level of activity exists in mycobacteria.34,36 Thegenome of M. tuberculosis and M. bovis encodes for twodifferent isocitrate lyases. In M. tuberculosis H37Rv, the geneencoding the other form, termed aceA, has a single-base pairoverlap resulting in two ORFs (aceAa and aceAb) whereas in

M. bovis, a single ORF is encoded with the protein productbeing 100% identical to AceAb. It is unclear if functional proteinarises in H37Rv, however in M. tuberculosis CSU93, bothenzymes are active with AceA being the less efficient of thetwo.34 Peptide ions associated with AceA were detected in ourstudies (Table S1 in Supporting Information), however, onlythe isocitrate lyase encoded by icl was differentially expressed.This would suggest that the primary isocitrate lyase in M. bovisBCG, much like CSU93, is Icl with AceA contributing negligibleactivity.

Conflicting reports exist in the literature for the transcrip-tional response of icl following INH treatment. This observation

Table 2. MASCOT Scores for 10 Peptides before and after Ammonium Sulfate Fractionationa

INH

sample protein

total

peptides Mrb ∆Mr

c Rt

Rt

(Cv) (%)

peptide

score

protein

score expectd sequence

F04 GlnA1 9 2101.90 -0.02 56.06 1.0 98 456 6.6 × 10-11 DGAPLMYDETGYAGLSDTARCrude GlnA1 5 2101.92 -0.01 55.51 10 64 4.5 × 10-2 DGAPLMYDETGYAGLSDTARF05 FadA 3 1565.74 -0.01 41.51 1.7 35 52 3.4 × 10-4 FCASGLEAVNTAAQKCrude FadA 1 1565.75 0.00 40.79 17 17 2.1 × 10-2 FCASGLEAVNTAAQKF05 Mdh 7 1476.70 -0.02 32.73 -2.1 88 215 1.9 × 10-9 GASSAASAASATIDAARCrude Mdh 6 1476.71 -0.01 33.44 35 111 5.1 × 10-4 GASSAASAASATIDAARF01 AtpD 13 2841.42 0.01 82.02 0.7 44 205 3.7 × 10-5 GIFPAVDPLASSSTILDPSVVGDEHYRCrude AtpD 3 2842.42 0.01 81.46 9 44 1.1 × 10-1 GIFPAVDPLASSSTILDPSVVGDEHYRF05 KasA 6 1520.72 -0.01 46.89 1.2 59 167 1.3 × 10-6 IVESYDLMNAGGPRCrude KasA 3 1520.72 -0.01 46.32 19 19 1.6 × 10-2 IVESYDLMNAGGPRF03 DnaK 18 1644.85 -0.02 58.12 1.3 47 443 3.2 × 10-5 IVNEPTAAALAYGLDKCrude DnaK 16 1644.86 -0.01 57.38 11 330 1.3 × 10-1 IVNEPTAAALAYGLDKF03 Tig 8 1799.91 -0.01 64.86 0.3 70 122 1.9 × 10-7 LIAGLDDAVVGLSADESRCrude Tig 4 1799.92 -0.01 64.69 21 37 1.4 × 10-2 LIAGLDDAVVGLSADESRF01 AtpA 9 1884.99 -0.02 68.72 0.4 37 116 3.4 × 10-4 LSDDLGGGSLTGLPIIETKCrude AtpA 2 1884.99 -0.01 68.45 1 19 1.40 LSDDLGGGSLTGLPIIETKF01 MoxR1 14 2243.18 -0.02 84.59 0.3 96 375 3.8 × 10-10 LVLTYDALADEISPEIVINRCrude MoxR1 2 2243.19 -0.02 84.31 38 54 2.4 × 10-4 LVLTYDALADEISPEIVINRF03 RpoA 11 1060.52 -0.02 46.11 0.8 59 438 1.9 × 10-6 TESDLLDIRCrude RpoA 9 1060.53 -0.01 45.72 9 221 2.3 × 10-1 TESDLLDIR

a Abbreviations: Rt, retention time b Experimental monoisotopic mass measurement c Difference between experimental and calculated monoisotopicmasses d MASCOT expectation value: number of times it is expected to obtain an equal or higher score purely by chance

Table 3. Differentially Expressed Proteins with Associated Relative Fold Changea

Rv no. protein description pep SC (%) ln(I/C) ( 95% CI fn. cat.

Rv0058 DNAB Probable replicative DNA helicase 13 16 0.48 ( 0.02 2Rv0183 Rv0183 Possible lysophospholipase 7 28 -0.39 ( 0.04 7Rv0467 ICL Isocitrate lyase 8 20 0.69 ( 0.07 7Rv0475 HBHA Heparin binding hemagglutinin 4 30 0.53 ( 0.20 3Rv0788 PURQ Probable phosphoribosylformylglycinamidine

synthase I6 35 0.59 ( 0.04 7

Rv0860 FADB Probable fatty oxidation protein 6 11 0.54 ( 0.06 1Rv1094 DESA2 Possible acyl-ACP desaturase 4 21 -0.39 ( 0.07 1Rv1160 MUTT2 Probable mutator protein 3 19 1.90 ( 0.11 2Rv1437 PGK Probable phosphoglycerate kinase 9 22 0.55 ( 0.07 7Rv2145c WAG31 Conserved hypothetical protein 4 14 1.31 ( 0.21 3Rv2244 ACPM Meromycolate acyl carrier protein 4 43 0.91 ( 0.11 1Rv2245 KASA â-ketoacyl synthase 1 3 23 1.20 ( 0.17 1Rv2246 KASB â-ketoacyl synthase 2 8 28 0.57 ( 0.14 1Rv2247 ACCD6 Acetyl CoA carboxylase 4 19 1.41 ( 0.23 1Rv2405 Rv2405 Conserved hypothetical protein 9 42 0.68 ( 0.02 10Rv2449c Rv2449c Conserved hypothetical protein 7 22 0.52 ( 0.03 10Rv2493 Rv2493 Conserved hypothetical protein 3 61 -0.77 ( 0.09 10Rv2522c Rv2522c Conserved hypothetical protein 3 8 -1.68 ( 0.12 10Rv2744c Rv2744c Conserved 35 kDa alanine rich protein 14 49 0.43 ( 0.07 10Rv2930 FADD26 Fatty acid-CoA ligase 9 21 0.39 ( 0.08 1Rv2981c DDLA Probable D-alanine-D-alanine ligase 3 11 -1.49 ( 0.12 3Rv3778c Rv3778c Possible aminotransferase 7 20 0.43 ( 0.07 7Rv3841 BFRB Bacterioferritin 7 37 0.41 ( 0.08 7Rv3858c GLTD Putative NADH-dependent glutamate synthase 6 20 -0.39 ( 0.06 7

a Abbreviations: Pep, peptides detected; SC, protein sequence coverage; ln(I/C), the natural log of the average relative intensity measurement for associatedpeptide ions from INH-treatment divided by the average relative intensity measurement for the same ions found in the control; Fn. Cat., functional category(TubercuList): 1, lipid metabolism; 2, information pathways; 3, cell wall and cell processes; 7, intermediary metabolism and respiration; 10, conservedhypotheticals.

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Journal of Proteome Research • Vol. 5, No. 1, 2006 61

addresses a fundamental problem with the vast differencesamong reported DNA array data sets. These discrepancies arein part due to differences in sample preparation, the arraysthemselves, and statistical analysis methods. The effects ob-served in this study seem to support the work of Waddle et al.in which the gene icl was found to be affected by INHtreatment.25

Up-regulation of the bacterioferritin protein BfrB was ob-served upon INH treatment. This protein is involved in ironstorage in mycobacteria and the gene has previously beenshown to be up-regulated under conditions of excess iron.37

Iron is an essential cofactor for the INH activating enzymeKatG, a catalase-peroxidase. The differential expression of BfrBseems to suggest a connection between regulation/activity ofproteins involved in INH activation and iron metabolism.Although INH has been shown to primarily target the enoyl-reductase enzyme, InhA, in the FAS II pathway, the observedIC50 of 7.3 µM20 is much higher than the bactericidal potency(0.2-0.4 µM)38 suggesting additional affects beyond inhibitionof InhA.23 A disruption in iron metabolism could, in part, bemediated through formation of reactive NO species, as sug-gested by others.39,40

Cell Wall and Cell Processes. Ddla, D-alanine:D-alanineligase, is an ATP-dependent enzyme involved in peptidoglycanbiosynthesis. Peptidoglycan is one of the three major compo-nents of the mycobacterial cell envelope. It is covalently linkedto arabinogalactan chains, via phosphoryl-N-acetylglucosami-nosyl-rhamnosyl linkage units, which are in turn esterified tomycolic acids. In our study, this protein was markedly downregulated following INH treatment, -4.4-fold. This result isconsistent with disruptions in the cellular morphology previ-ously observed following INH treatment.41 Reduced expressionlevels for Ddla seems to suggest reduced substrate, D-alanine,levels which may be the result of inhibiting enzymes associatedwith D-alanine synthesis. The drug-induced effect on Ddlacould also suggest a common regulatory pathway betweenmycolic and peptidoglycan biosynthesis.

Although described as a conserved hypothetical, Wag31 (alsoknown as antigen 84) is grouped with this functional categorybecause it bears some homology (20-48% identity) to celldivision initiation proteins, DivIVA, from other gram-positivebacteria. The gene encoding Wag31, Rv2145c, is essential inM. tuberculosis based on Himar1-transposon mutagenesis.42 InBacillus subtillus, overexpression of divIVA is lethal to the celland is associated with a filamentous phenotype.43 The protein,DivIVA, is localized to the bacterial cell poles, the primary sitefor morphological alterations following INH treatment inmycobacteria.41 The possible involvement of Wag31 in cellulardivision and its response profile observed following INH-treatment highlights the involvement of non-FAS relatedproteins in the mechanisms leading to cell lysis, a knownbactericidal property of INH.

Conserved Hypotheticals. Conserved hypothetical proteinsaccount for more that 25% of the M. tuberculosis proteome.These proteins comprise hypothetical proteins of unknownfunctions but are shared between organisms. It is likely thatthey may prove to have important functions related to cellularhomeostasis and require further studies to define their rolesin mycobacteria. Several ORFs in this category respond to INHat the transcript level.12,25,28 In our study, several conservedhypothetical proteins were found to be differentially expressedas well (Rv2405, Rv2449c, Rv2493, Rv2522c, Rv2744c). It shouldbe noted that while protein Rv2493 was down-regulated 2-fold,

it was previously shown to be up-regulated 2.6-fold by tran-scriptional profiling.25 The effects observed at the protein levelseems to suggest possible post-translational control mecha-nisms and reiterates the need for a more dynamic view ofcellular responses to drug-related perturbations.

Since functional roles for unknown proteins are constantlybeing annotated, an attempt was made to postulate a metabolicrole for the conserved hypothetical proteins identified basedon primary sequence homology using the latest NCBI database(GenBank, last updated June 15, 2005). No homology withfunctionally characterized proteins could be determined forRv2493, Rv2405, or Rv2449c. Rv2522c was homologous toproteins within the peptidase family (30-57% identity), andRv2744c contained a phage shock protein A (PspA) signaturedomain. Interestingly, PspA from E. coli is thought to facilitatethe maintenance of the proton motive force and is expressedin response to a variety of environmental stressors, includinginhibitors of lipid biosynthesis.44

In conclusion, like transcriptional profiling, a great deal ofinformation is generated by proteome profiling. However, franktargets of drugs are not so obviously identified. The complexityof responses seen by either method is not surprising as thecells cope with imminent death. The connection between theinitial insult caused by INH, or any drug, and the sequence ofevents leading to cell death is an important unansweredquestion where time-dependent or concentration-dependentprofile effects might afford some insight. For example, Wag31,likely involved in cell division, was significantly overexpressedand presents a possible explanation for the morphologicalchanges associated with the bacterial cell poles following INHtreatment that cannot be explained by inhibition of mycolicacid synthesis alone. Similarly, D-Ala-D-Ala ligase, a key enzymeof bacterial cell wall biosynthesis, declines dramatically onexposure to INH. This decrease certainly correlates to the lyticevents that take place during cell death, but appear remotefrom the proposed primary action of the drug. Nonetheless,compelling clues can be seen in the changes in proteinexpression that correlate to known activities of INH. These datatake us one crucial step closer to the intimate workings of aliving cell carried out by its proteins, where a number ofmetabolic responses is occurring, but clear among them iselevation of the targeted mycobacterial FAS machinery. It isnoteworthy that this identification was so clearly discerniblesince only 103 proteins were considered, less than 1% of thetheoretical proteome of this organism, but their differentialexpression separates them from the vast majority of detectedproteins.

Acknowledgment. M.A.H. and C.A.T. are grateful to theNational Institutes of Health for partial financial support of thiswork (U01 AI054842).

Supporting Information Available: 2-DE gel imagesof the crude soluble protein samples for control and INH-treatment extracted with an MS-friendly buffer system and acomplete list of the 103 proteins identified with the number ofdetected peptides and average fold change (95% confidenceintervals. This material is available free of charge via theInternet at http://pubs.acs.org.

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