J. Biol. Chem.-2004-Boshoff-40174-84

The Transcriptional Responses of Mycobacterium tuberculosis toInhibitors of MetabolismNOVEL INSIGHTS INTO DRUG MECHANISMS OF ACTION*□S

Received for publication, June 17, 2004, and in revised form, July 9, 2004Published, JBC Papers in Press, July 9, 2004, DOI 10.1074/jbc.M406796200

Helena I. M. Boshoff‡§, Timothy G. Myers¶, Brent R. Copp�, Michael R. McNeil**,Michael A. Wilson¶, and Clifton E. Barry III‡

From the ‡Tuberculosis Research Section, NIAID, National Institutes of Health, Rockville, Maryland 20852, the ¶ResearchTechnologies Branch, NIAID, National Institutes of Health, Bethesda, Maryland 20892, the �Department of Chemistry,University of Auckland, Auckland 1020, New Zealand, and the **Department of Microbiology, Colorado State University,Ft. Collins, Colorado 80523

The differential transcriptional response of Mycobac-terium tuberculosis to drugs and growth-inhibitory con-ditions was monitored to generate a data set of 430 mi-croarray profiles. Unbiased grouping of these profilesindependently clustered agents of known mechanism ofaction accurately and was successful at predicting themechanism of action of several unknown agents. Thesepredictions were validated biochemically for two agentsof previously uncategorized mechanism, pyridoacri-dones and phenothiazines. Analysis of this data set fur-ther revealed 150 underlying clusters of coordinatelyregulated genes offering the first glimpse at the fullmetabolic potential of this organism. A signature subsetof these gene clusters was sufficient to classify allknown agents as to mechanism of action. Transcrip-tional profiling of both crude and purified naturalproducts can provide critical information on bothmechanism and detoxification prior to purificationthat can be used to guide the drug discovery process.Thus, the transcriptional profile generated by a crudemarine natural product recapitulated the mechanisticprediction from the pure active component. The un-derlying gene clusters further provide fundamentalinsights into the metabolic response of bacteria todrug-induced stress and provide a rational basis forthe selection of critical metabolic targets for screeningfor new agents with improved activity against thisimportant human pathogen.

Despite the introduction of directly observed therapy, shortcourse, in 1995, millions of tuberculosis patients continue toperish, and fully one-third of the world’s population is infectedwith the causative agent of this disease, Mycobacterium tuber-culosis. New drugs are urgently needed to shorten the durationof tuberculosis chemotherapy and treat the increasing numberof infections with drug-resistant organisms. Target selection iscritical to the development of new drugs but is hampered by alack of understanding of the dynamics of the metabolic re-sponse to interruption of target function even by current

agents. Predicting targets that would manifest a cidal activity,therefore, is limited by our incomplete understanding of thephysiology of the bacilli and its ability to adapt to disruption ofmetabolism.

An organism responds to changes in its environment byaltering the level of expression of critical genes that transducesuch signals into metabolic changes favoring continued growthand survival. Analysis of the transcriptional response by mi-croarray can, in theory, provide clues to such adaptive re-sponses, but thus far gene expression profiles have only beenused to contrast the mechanisms of action of a small number ofrelated drugs (1–3). Coordinately regulated sets of genes (regu-lons) are often controlled by single transcriptional regulatorsthat function as genetic master switches, committing the bac-terium to a major alteration in metabolism. In M. tuberculosis,examples of such regulatory mechanisms have been reportedrecently from studies using genetic approaches, including thedormancy regulon (4) and the stringent response (5). The com-plexity of the cellular transcriptional response to drug-inducedstress makes it very difficult to derive this sort of informationsolely from microarray analysis of a limited number of agentsaffecting the same metabolic pathway (6, 7). However, by an-alyzing a wide variety of conditions, groups of genes have beenidentified that appear co-regulated under many different con-ditions in yeast (8). In this study, we applied genome-wideexpression profiling to diverse environmental changes, includ-ing many different drug types, to begin to map the adaptabilityof the bacilli to interruption of specific arms of metabolism.This allowed us to identify clusters of coordinately regulatedgenes both diagnostic for drug mechanism of action and usefulfor a more rational approach to the selection of critical drugtargets.

EXPERIMENTAL PROCEDURES

M. tuberculosis Growth Conditions, RNA Isolation, and Hybridiza-tion—M. tuberculosis (H37Rv, ATCC 27294) was grown in Middlebrook7H9 supplemented with albumin/dextrose/NaCl/glycerol/Tween 80, Du-bos medium, or defined minimal medium as previously described (9).Carbon sources were either 10 mM glucose, 10 mM succinate, or 0.05 mM

sodium palmitate, and cultures were grown from an A650 of 0.005–0.3before RNA isolation. Cultures grown under a self-depleted oxygengradient (NRP-1) were set up as described (10), and RNA was isolatedafter 3–6 days. Nutrient starvation cultures were set up as previouslydescribed (5) in phosphate- or Tris-buffered saline containing 0.05%Tween 80 (PBST or TBST). The organic extract of Eudistoma amplumwas prepared as follows: the frozen, ground invertebrate was extractedwith water at 4 °C, and the pellet was freeze-dried and then extractedat room temperature with methanol/methylene chloride (1:1). The sol-vent was evaporated and the extract dissolved in Me2SO to 9 mg/ml.

* The costs of publication of this article were defrayed in part by thepayment of page charges. This article must therefore be hereby marked“advertisement” in accordance with 18 U.S.C. Section 1734 solely toindicate this fact.

□S The on-line version of this article (available at http://www.jbc.org)contains supplemental data.

§ To whom correspondence should be addressed: Twinbrook II, Rm.239, 12441 Parklawn Dr., Rockville, MD 20852. Tel.: 301-4519438; Fax:301-4020993; E-mail: [email protected].

THE JOURNAL OF BIOLOGICAL CHEMISTRY Vol. 279, No. 38, Issue of September 17, pp. 40174–40184, 2004Printed in U.S.A.

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S-Nitrosoglutathione (GSNO)1 was used as a NO source. Cultures weregrown from an A650 of 0.07–0.3 before adding either drug or solvent(1000-fold dilutions from Me2SO, ethanol, or water), and RNA wasisolated at selected intervals thereafter (11). For each drug-treatedculture, a parallel culture was treated with an equivalent amount ofvehicle (Me2SO, ethanol, or water) for the same amount of time. RNAfrom the latter culture was used as the reference sample to which thedrug-treated sample was compared. Each treatment condition and eachdrug concentration was repeated a minimum of two independent times.

M. tuberculosis carrying an integrated copy of the Mycobacteriumsmegmatis amidase pzaA, which hydrolyzes several aromatic amides(12), was used for cultures treated with pyrazinamide, 5-chloropyrazi-namide, nicotinamide, or benzamide. This strain was also used toinvestigate the transcriptional response during extracellular pH stress.Treatment was done in Middlebrook 7H9-based medium adjusted to therequired pH (4.8, 5.2, or 5.6) with H3PO4 and referenced to cells grownin the same acidified medium without amide addition. Since pyrazina-mide is only effective at low cell densities, cultures were grown to anA650 of 0.05 before treatment was initiated. MICs were measured usingthe microbroth dilution technique (13) using H37Rv or M. tuberculosis�mbtB (14). Iron preloading of cells was done for 3 h in 7H9-basedmedium containing a 10-fold excess of Fe3�. RNA labeling and hybrid-ization was as previously described (11).

Microarray Preparation and Data Analysis—Microarray preparationis described under GEO accession number GSE1642. Expression ratioswere calculated as the feature pixel median minus background pixelmedian for one color channel divided by the same for the other channel.In cases where more than 10% of the feature pixels were saturated, thefeature pixel mean was used instead of the median. When the featurepixel mean did not exceed the background pixel mean by more than twoS.D. values (calculated from the background pixel distribution), thefeature pixel median is used in the ratio without background subtrac-tion. In cases where both color channels were near background (samecriterion), the ratio value was set to “missing.” Expression ratios weretransformed to the log base 2 for all further calculations.

Standardized gene expression ratio patterns were calculated by sub-tracting the mean expression ratio and dividing by the S.D. statisticscalculated from all ratios (all microarrays) for that gene. Standardizingin this way corrected for scale differences between the response pat-terns for different genes. The resulting z-scores were averaged accord-ing to the drug treatment name, resulting in a single value for eachdrug name for each gene (see Supplementary Data). These gene pat-terns where then clustered using a K-means algorithm (SAS ProcFastclus) using the Euclidean distance as the dissimilarity metric. Tworounds of K-means clustering were conducted. The first with the subsetof genes showing the highest treatment-dependent variation in expres-sion as judged by one-way analysis of variance (SAS Proc ANOVA) onthe original log ratio vectors, using treatment name as the class vari-able. The second round used all genes, but without allowing the clusternumber to increase or the cluster centroids to drift (assigning theremaining genes to the existing clusters formed in the first round ofclustering). We arrived at the Fastclus “maxclusters” parameter, themaximum number of clusters to define, value of 150 clusters, by mul-tiplying the number of class levels (treatments), 75, by 2. The numberof genes selected for the first round of clustering (1650) was limited to11 times the number of clusters and were those with the most statisti-cally significant one-way analysis of variance score. A single pattern ofresponse for each gene cluster was calculated as the mean of all stan-dardized gene patterns assigned to the cluster by Proc Fastclus. Thesecluster centroids were themselves clustered using average linkage al-gorithm calculated in Microsoft Excel VBA using a one minus thePearson correlation coefficient for the distance metric (15) to arrive atthe ordering of rows in Fig. 3 (for details, see Supplementary Data).Patterns of response to each treatment were clustered using the samemethod to arrive at the column order in Fig. 3. The array data havebeen deposited in the Gene Expression Omnibus at NCBI (GEO; avail-able on the World Wide Web at www.ncbi.nlm.nih.gov/geo) with GEOaccession number GSE1642.

Real Time, Quantitative Reverse Transcription-PCR Assay—The ex-pression of iniB, Rv3161, kasA, efpA, fadE23, rplJ, rplN, dnaE2, radA,

mbtB, csd, narH, narG, cydA, and ald was quantitated after normal-ization of RNA levels to the expression of the sigA gene as previouslydescribed (11), and results are available in Supplementary Data.

Enzyme Assays—Enzyme assays were done on proteins fromM. smegmatis. Purification and determination of NADH dehydrogenaseand succinate dehydrogenase activities was performed as previouslydescribed (16).

Oxygen Consumption Assays—The effect of drugs on oxygen con-sumption by M. tuberculosis was done in parafilm-sealed, glass screw-cap tubes that were filled with midlog phase culture containing 0.001%methylene blue. Decolorization typically occurred after 12 h. The rate ofoxygen consumption was measured in M. smegmatis using midlog stagecultures treated with drug for 1 h before adding 0.01% methylene blueand monitoring decolorization at 665 nm.

Sugar Analysis—Cell walls were prepared, and glycosyl composi-tions were determined by the alditol acetate methods as described (17).

NADH/NAD� Determination—NADH and NAD� levels were deter-mined by a sensitive cycling assay (18). Briefly, M. tuberculosis cultureswere grown to an A650 of 0.3 and treated with drugs for 3 h. At thisstage, cells were rapidly harvested (two 2-ml samples) and resuspendedin 0.2 M HCl (NAD� determination) or 0.2 M NaOH (NADH determina-tion). Nucleotide extraction was further facilitated by bead beating ofthe suspensions with 0.2-ml glass beads (40 s, maximum speed). Ex-tracts were further prepared, and enzyme assays were performed aspreviously described (18). All determinations were repeated in at leastthree independent experiments.

Menaquinol/Menaquinone Analysis—Cultures were grown to an A650

of 0.3 and treated for 3 h with drug or solvent alone. Menaquinone andmenaquinol were extracted as described (19) and quantified by liquidchromatography-mass spectrometry (Hewlett-Packard 1100) using aC18 column with detection by DAD at UV 266 nm. All extractions wererepeated at least three independent times.

Intracellular ATP Determinations—These assays were done on cul-tures of M. tuberculosis containing an integrated luciferase gene frompMV306-groELluc grown to an A650 of 0.1–0.2 and treated with drug for20–120 min. ATP levels were determined by bioluminescence as previ-ously described (20).

MTT Assays—M. smegmatis at an A650 of 0.2 was treated with drugor vehicle alone for 15 min (100 �l/well in 96-well plates in quadrupli-cate) before the addition of 25 �l of 2 mg/ml MTT. The reaction wasstopped after 30 min by the addition of 25 �l of 10% SDS, and theabsorbance at 595 nm was recorded. The assay was repeated twoindependent times.

RESULTS

De Novo Analysis of Expression Data Results in Mechanismof Action-based Clustering of Transcriptional Responses—Transcriptional profiling of M. tuberculosis was performed us-ing 430 whole-genome microarrays to measure the effects of 75different drugs, drug combinations, or different growth condi-tions at various times relative to a sample of logarithmicallygrowing M. tuberculosis. The drug concentrations and timepoints (see Supplementary Data) were chosen such that 10% orless of the total number of genes were differentially (2-fold ormore) regulated and through consulting previously publishedstudies (2, 3). Expression of highly responsive genes withincertain drug groups was confirmed by quantitative reversetranscription-PCR (see Supplementary Data). The quality ofthese data were tested using Pearson rank tests, which verifiedthat arrays within each treatment group were highly corre-lated (Fig. 1). Log-transformed expression ratios were stan-dardized according to the pan-array distribution for each geneand then averaged according to treatment. To analyze thesedata, unsupervised clustering methods were applied to the 345expression profiles. Expression data sets of genes that were up-or down-regulated at least 3-fold in four or more experimentswere analyzed by agglomerative hierarchical clusteringmethod (21) to identify drug groupings based on gross analysisof coordinately regulated genes (Fig. 2). This revealed thatgroups of drugs clustered separately based on known mecha-nisms of action. Thus, protein synthesis inhibitors, transcrip-tional inhibitors, aromatic amides, cell wall synthesis inhibi-tors and agents that damage DNA fell into distinct groups.

1 The abbreviations used are: GSNO, S-nitrosoglutathione; MIC,minimum inhibitory concentration; MTT, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide; GC, gene cluster; TRC, triclosan;CPZ, chlorpromazine; TRZ, thioridazine; CCCP, carbonyl cyanide chlo-rophenylhydrazone; DNP, dinitrophenol; CCO, cytochrome c oxidase;EMB, ethambutol.

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These observations were further supported by a predictionmatrix using the Pearson correlation over the entire array ofgenes to predict the treatment group (Fig. 1). This showed thatthe individual profiles of drug treatments were accurately clas-sified to groups of agents with similar predicted modes ofaction. The majority of apparent “mispredictions” of well char-acterized inhibitors in this matrix, usually correlated withagents within the same treatment group.

Identification of Co-regulated Genes Reveals Validated Regu-lons—To identify biologically meaningful groups of genes,profiles were partitioned into 75 drug groups with each druggroup corresponding to a single type of treatment, and geneswere analyzed by K-means clustering to uncover those with

similar expression patterns across these sets (Fig. 3). Many ofthese clusters contained genes that were functionally related,but many also contained genes that encoded proteins of un-known function.

Genes previously shown to be members of the DosR- andRecA-controlled regulons (4, 11, 22) were independently iden-tified by this process of gene clustering. Gene cluster 39(GC39), for example, contained 21 of the 48 members of thedormancy regulon, and three closely linked clusters (GC126,-56, and -137) contained 38 genes previously reported to beup-regulated by DNA damage.

The Metabolic Response to Inhibition of Translation—Anal-ysis of the cellular response to translational inhibition re-

FIG. 1. Prediction matrix using Pearson correlation over the entire array of genes to assess accuracy of classification ofindividual arrays. Individual arrays from each treatment group (columns, n � 430) were compared with the collection of averaged arrayrepresentation computed to be characteristic of each treatment group (rows). The treatment group with the top average score was taken as the“prediction” for that array. Matrix numbers are counts of individual arrays predicted to match with the averaged array representing a treatmentgroup. Major treatment groups (class of inhibitor) are color-coded as follows: violet, growth in minimal medium with palmitate or succinate ascarbon sources as compared with glucose as carbon source; lavender, growth in acidified medium; blue, aromatic amides that can be hydrolyzedintracellularly; light blue, agents that inhibit cell wall synthesis; pale blue, agents that affect DNA integrity or topology; green, inhibitors of proteinsynthesis; yellow, growth under conditions that were associated with expression of the dosR regulon; light green, agents besides NO that inhibitrespiration, and TRC; pink, transcriptional inhibitors; orange, nutrient starvation in PBST or TBST; red, pyridoacridones and iron scavengers. Theblack boxes are correct assignments within the treatment class. DIPED, diisopropylethylenediamine; DTNB, dithiobis(nitrobenzoic) acid.

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vealed a general down-regulation of macromolecular synthe-sis, as expected, although there was an evident attempt toincrease synthesis of the translational apparatus. Up-regu-lated genes included those implicated in ribosomal architec-ture and translation (e.g. GC28, -36, -70, -71, -90, and -118)whereas down-regulated genes included aspects of macromo-lecular metabolism similar to those responsive to starvation(e.g. ppk and relA). Regulation of these genes did not appearto be mediated by the stringent response through ppGpp,since relA was down-regulated. Interestingly, Rv1026 (encod-ing a possible pppGpp-5�-phosphohydrolase that would hy-drolyze any residual mediator of the stringent response (23))was up-regulated during translational inhibition and down-regulated during starvation. The observed up-regulation ofthe inorganic pyrophosphatase encoded by ppa would proba-bly slow ribosomal degradation (24) and also contrasts withthe down-regulation of this gene during starvation. Not sur-prisingly, ppa is part of a regulon (GC71) containing genesimplicated in translation, and, combined with the observeddown-regulation of ppk (a polyphosphate kinase), this sug-gests an important role for polyphosphate in mycobacterialadaption to translational inhibition (25).

A gene cluster containing the gene encoding the regulatoryprotein of pyrimidine biosynthesis (pyrR) (GC69) was also up-regulated, consistent with the observed down-regulation of ex-pression of several genes involved in pyrimidine biosynthesis,whereas genes involved in purine and pyrimidine salvage (apt,gmk, prsA, thyA, and cdd) and conversion of nucleotides todeoxyribonucleotides (nrdF1, nrdF2, nrdH, and nrdI) were up-regulated upon translational inhibition.

Aminoglycosides were associated with an up-regulation ofheat shock proteins (GC134), presumably resulting from mis-translation-induced aberrant peptides in the cytoplasm as hasbeen observed for other bacteria (26). Tetracycline and rox-ithromycin did not induce this response, consistent with thefact that they block release of the nascent peptide during trans-lational inhibition.

Our analysis also suggested that translational inhibitionresults in inhibition of DNA replication and the processing ofreplication forks. The down-regulation of several genes sup-ports this hypothesis, including the following: Rv1708 (possiblerole in initiation of replication); the major replicative DNApolymerase (11); and DnaA, which plays a role in initiation ofchromosomal replication. Likewise, genes implicated in turn-over of DNA were up-regulated, including nth, recR, hupB,recF, and ssb. This did not result in a signal that was relayed asDNA damage, however, since recA and dnaE2 (11) were notup-regulated.

The Metabolic Response to Inhibitors of DNA Transcriptionand Gyrase Function—Unsurprisingly, the mode of action oftranscriptional inhibitors such as rifamycins could best be de-scribed as a global down-regulation of most gene clusters, in-cluding the ribonucleotide reductase genes (GC49), heat shockproteins (GC134), and several ribosomal genes. Despite this,some transcript levels were elevated, but this was probably dueto differential mRNA stabilities.

Fluoroquinolones bind gyrase and topoisomerase IV on DNA,blocking transcription and replication and resulting in DNAdamage (27). DNA damage also results from treatment withUV irradiation, H2O2, and mitomycin C. All of these treat-ments resulted in the up-regulation of the previously charac-terized (11, 28) SOS gene clusters (GC56, -126, and -137) as

correspond to low (5� MIC) and high (10� MIC) concentrations, re-spectively. CPZ and TRZ profiles correspond to concentrations of 1–2�MIC (1) and 2–3� MIC (2).

FIG. 2. Cellular transcriptional responses cluster by drugmechanism of action. Average linkage clustering of expression pro-files of genes that were up- or down-regulated at least 3-fold in four ormore experiments. Profiles were clustered using a modified uncenteredPearson correlation coefficient as the similarity metric. The major druggroups are color-coded as in Fig. 1. Pyridoacridone clusters 1 and 2



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FIG. 3. Transcriptional responses to drug treatment reveal underlying logically associated gene clusters. Shown is a heat map-rendered table of gene expression changes. In A, each column is the average of several microarray experiments using the same drug/treatment,whereas each box within a row is the average for all genes assigned to the same gene cluster. Major drug classes are color-coded (bars in top



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well as several DNA repair-associated genes that were notcorrelated with this regulon. The gyrase inhibitor novobiocindoes not induce double-stranded breaks (29) and did not clusterwith those agents that did. Novobiocin affected the expressionof a more limited subset of DNA repair or structural mainte-nance genes including the up-regulation of the RecA-independ-ent, Y family polymerase member encoded by dinP. The effectsof fluoroquinolones (including novobiocin) could be distin-guished from the other forms of DNA damage employed in thisstudy by the unique up-regulation of the class Ib ribonucleotidereductase genes (GC49) as well as nrdF1.

Deoxyribonucleotide pools are regulated by the activity ofribonucleotide reductase and are intricately linked to DNAreplication (30). Repair of fluoroquinolone-induced double-stranded DNA breaks may provide the signal to elevate DNAsynthetic machinery, as has been reported in E. coli (31), orsuch a signal may be generated during the stalling of chromo-somal DNA replication. The up-regulation of nrdF1, the alter-nate nonessential ribonucleotide reductase small subunit (32),suggests that this subunit may play a role in supplying dNTPsduring DNA turnover or repair.

Gene Signatures for Inhibitors of Cell Wall Biosynthesis—Inhibition of cell wall biosynthesis by any known agent revealedthat there was one unique set of genes broadly responsive to thisinsult. These genes comprised two regulons (GC27 and -128).GC27 consists of secreted cell wall-associated proteins such asRv1987 encoding a putative glycohydrolase, lprJ, fbpC, murD,dacB1, and Rv3717 encoding a putative N-acetylmuramoyl-L-alanine amidase and may be regulated by SigD. GC128 includedthe iniBAC operon, an operon of unknown function that haspreviously been shown to be responsive to such inhibitors (3, 33)and several cell wall-associated genes. This gene cluster was alsolinked to two others (GC79 and -89) that contained several cellwall biosynthetic and turnover genes.

The �-lactam antibiotics induced unique genes consistentwith their known transpeptidase-inhibiting properties. Theseincluded Rv3717, a putative N-acetylmuramoyl-L-alanine am-idase that may correspond to the enzyme implicated in peni-cillin-induced autolysis in other bacteria (34).

Ethambutol (EMB), which inhibits the arabinosyltrans-ferases that decorate arabinogalactan and lipoarabinomannan(35, 36), has recently been extensively analogued with somesuccess (13). A comparison of the transcriptional profiles of twopotent analogs with EMB showed that, despite many similar-ities in transcriptional profiles, EMB differentially affected aregulon (GC82) containing genes within the FAS-II pathway aswell as a regulon (GC17) that contained genes implicated infatty acid modification. This mechanistic divergence was con-firmed by the observation that whereas M. tuberculosis cellstreated with EMB rapidly lost acid-fastness, cells treated withthe analogs did not (data not shown). Further, cells treatedwith EMB contained significantly less arabinose than controls,whereas cells treated with analogs of EMB did not (Fig. 4).

Isoniazid, ethionamide, and thiolactomycin are all thought toinhibit enzymes in the repetitive catalytic cycle of the FAS-IIpathway that elongates fatty to mycolic acids, and an analysis ofthe transcriptional response to all three drugs is very consistentwith that mechanism. These drugs were also found to clusterclosely with cerulenin, which inhibits both the FAS-II and FAS-Isystems (FAS-I is responsible for de novo synthesis of fatty acidsfrom acetate). The signature profile for effects on fatty acid syn-

thesis includes the acpM-kasA-kasB-accD6 operon as well asefpA. There were also effects on several polyketide synthases aswell as polyketide synthase-associated genes and proteins impli-cated in fatty acid transport that distinguished the fatty acidsynthesis inhibitors from other groups of drugs as well as tri-closan (TRC). Analysis of the gene clusters regulated by cerule-nin, ethionamide, thiolactomycin, and isoniazid indicated thatthe combined effects on GC27, -79, -120, -82, and -128 provide asignature profile diagnostic for inhibition of fatty acid synthesis.These gene clusters all contain genes involved in aspects of cellwall metabolism and secreted proteins. Notably, GC82 containsthe FAS-II operon (Fig. 3B) as well as pks16 and Rv0241c encod-ing a protein with homology to a fatty acid synthetase � subunit,as well as fadA2, suggesting that these gene products are in-volved in interlinked metabolic pathways. In addition, the closelylinked GC120 contains fas, efpA, accA3, accD4, pks13, andfadD32 (Fig. 3B). A recent report describing the role of pks13 inthe final step of mycolate condensation (37) is further evidencefor the metabolic relatedness of genes in many of the gene clus-ters such as GC82 and GC120. Differential expression of distinctsets of gene clusters distinguishes FAS-II from FAS-I inhibitors,whereas other gene clusters can distinguish the effects of isoni-azid and ethionamide from thiolactomycin, showing that thesevarious fatty acid synthesis inhibitors affect biochemical path-ways that are connected to distinct transcriptional regulators.

The Transcriptional Profile of TRC Suggests That the Pri-mary Mode of Action Is on Respiration—TRC, a potent inhibitorof the FAS-II system in vitro, did not appear to elicit a similarresponse in vivo. This broad spectrum antibacterial agent in-hibits the bacterial fatty acid biosynthetic enzyme, enoyl-(acyl-carrier protein) reductase in vitro (38). Counterintuitively,TRC apparently stimulates degradation of fatty acids, sinceenzymes corresponding to every step of �-oxidation are up-regulated. TRC concurrently up-regulated citrate synthase(gltA1), which controls flux through the tricarboxylic acid cycle,and the enzymes of the pyruvate dehydrogenase complex,which control levels of acetyl-CoA. Because TRC clustered withknown respiratory inhibitors (Fig. 2), we investigated the ac-tivity of components of the respiratory chain in vitro. TRC wasfound to cause a dose-dependent inhibition of the membrane-bound quinol reductase succinate dehydrogenase (Fig. 5A). Theeffect of TRC on the membrane-bound electron transport chainwas also manifested in a rapid drop in intracellular ATP levelsin a reporter strain of M. tuberculosis expressing the luciferasegene, which was not observed with other cell wall inhibitors

dendogram) as in Fig. 1. The inset shows a color scale for z-scores. By interrogating the frequency at which boxes appear to be regulated byindependent treatments, the specificity of the gene cluster for a series of stresses can be determined. The top dendogram shows relatedness of drugtreatments based on gene clusters. The right dendogram indicates relationship between gene clusters. Further detail is provided in SupplementaryData. B, detail of gene clusters 82 and 120 showing z-scores for individual genes for each drug group. Ami, amikacin; Cap, capreomycin; SM,streptomycin; Rox, roxithromycin; Tet, tetracycline.

FIG. 4. Sugar content of mycobacterial cell wall during treat-ment with various diamines. M. tuberculosis cell walls were pre-pared 45 h after drug addition, and glycosyl compositions were deter-mined by the gas chromatography analysis of alditol acetatederivatives. The bars indicate molar ratios of rhamnose (black), arabi-nose (gray), and galactose (hashed) residues after normalization torhamnose. The inset shows arabinose/galactose ratios.



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(Fig. 6). The effect of the rapid depletion of ATP levels wasevidenced by the concomitant up-regulation of relA, which canbe directly linked to the expected decrease in synthesis ofcharged tRNAs. In addition, TRC treatment resulted in a de-cline in the intracellular redox potential of M. smegmatis asmeasured by reduction of MTT by intracellular dehydrogenases(Fig. 7). The decline in the intracellular redox status was re-flected in a decrease in the NADH/NAD� ratio and an increasein the menaquinol/menaquinone ratio of the major isomer MK-9(H2) (39) (Table I), an effect that was not observed with iso-niazid. TRC did not, however, directly inhibit oxygen consump-tion as measured by the rate of methylene blue decolorizationin normally growing cells (results not shown).

Regulation of Respiratory Chain Components and the Modeof Action of Phenothiazines and Azoles—The phenothiazineschlorpromazine (CPZ) and thioridazine (TRZ) are thought todirectly affect respiration (40), whereas the azoles econazoleand clotrimazole have been proposed to inhibit growth viainteraction with cytochrome P450-containing monooxygenases(41). CPZ has been proposed to inhibit respiration (42, 43),although other mechanisms have been proposed (44). Azolesare known to bind to the heme iron in cytochrome P450s,specifically the CYP51 sterol demethylase in fungi and possiblythe CYP121 in M. tuberculosis as has been suggested (41, 45).

The phenothiazines and azoles shared many similarities inregulated gene clusters, including one that contained knowncomponents of the respiratory chain (GC149), which included the

alternative terminal oxidase encoded by the cydA and cydBgenes. To unambiguously demonstrate an effect on respiration,we performed methylene blue decolorization assays to measurethe rate of oxygen consumption in treated and untreated cells.The results (Fig. 5D) indicated that the phenothiazines inhibitedoxygen consumption in both M. tuberculosis and M. smegmatis(Fig. 5D), whereas TRC, azoles, carbonyl cyanide chlorophenyl-hydrazone (CCCP), dicyclohexylcarboxidiimide, KCN, and dini-trophenol (DNP) did not (results not shown). The activity of twoquinone reductases, type II NADH-ubiquinone dehydrogenaseand succinate dehydrogenase, were also assessed in membranepreparations in the presence of these drugs. The phenothiazineswere potent inhibitors of both the type II NADH-ubiquinonedehydrogenase and the integral membrane succinate dehydro-genase (Fig. 5, B and C), whereas the azoles were inhibitors ofsuccinate dehydrogenase activity (results not shown). The effectsof the phenothiazines on respiration were further manifested ina rapid drop in intracellular ATP levels in M. tuberculosis (Fig. 6)as well as a decline in intracellular redox potential in M. smeg-matis (Fig. 7), effects that were also observed with known mod-ulators of the proton motive force such as protonophores (DNP,CCCP, and nigericin).

These respiratory inhibitors all up-regulated relA expres-sion, which can be ascribed to the expected decrease in chargedtRNAs due to ATP depletion. The decreased intracellular redoxpotential was reflected by a decrease in the intracellularNADH/NAD� ratio as well as a decrease in the cell-associated

FIG. 6. Effect of respiratory inhibi-tors and drugs on cellular ATP levelsin luciferase-expressing M. tubercu-losis. Cells in quadruplicate wells weretreated for the 20 min before measure-ment of bioluminescence after the addi-tion of luciferin. Shown is a typical resultof one of three independent experiments.

FIG. 5. Effect of phenothiazines and triclosan on respiratory enzymes and cellular respiration. Shown are integral membranesuccinate dehydrogenase (A and B) and type II NADH dehydrogenase (C) activities in the presence of 25 (�) and 50 (Œ) �g/ml TRC (A) and 25 (�)and 50 �g/ml (Œ) �g/ml TRZ (B and C) in comparison with control Me2SO (�)-treated assays. D, oxygen consumption of methylene blue-treatedM. smegmatis cultures in the absence (1) or presence (2) of 20 (�) and 50 (Œ) �g/ml TRZ. The inset shows oxygen consumption of methyleneblue-treated M. tuberculosis cultures in the absence (1) or presence (2) of 25 �g/ml TRZ.



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menaquinol/menaquinone ratio (Table I).To further define the effect of respiratory inhibitors such as

the phenothiazines on the regulation of respiratory chain com-ponents, we analyzed transcriptional profiles of cells treatedeither alone or with combinations of known respiratory modu-lators including cyanide, azide, dithiothreitol, ZnSO4, uncou-plers, redox cycling agents (menadione and clofazimine), andNO. We also explored the effect on respiration for cells grownon palmitate as the sole carbon source. This analysis revealedthat two distinct gene clusters (GC149 and -39) were independ-ently associated with alterations in electron flux through therespiratory chain (Fig. 8). One of these was the previouslydescribed NO-inducible dosR (4), whereas the other was acluster of genes containing the cyd operon (GC149). Inhibitionof both terminal oxidases, cytochrome c oxidase (CcO) andcytochrome bd oxidase, by NO (but not CcO-specific inhibitorslike cyanide or azide) or by depletion of oxygen during adaptionto hypoxic conditions resulted in up-regulation of the dosRregulon, as did growth on the reduced carbon source palmitate.The effect of NO on the dosR regulon could be fully reversed by

cyanide, menadione, and clofazimine, all of which could reoxi-dize the nitrosylferroheme of cytochromes (Fig. 8). Notably, allthree also were found to result in resumption of oxygen con-sumption (results not shown).

In contrast, the CcO-specific inhibitors (cyanide and azide)and agents that affect maturation of CcO (ZnSO4 and dithio-threitol) resulted in up-regulation of the cyd operon (cydA,cydB, cydD, and cydC) encoding the non-proton-pumping cyto-chrome bd oxidase (Fig. 2). Unlike other bacterial systems (54),we did not observe up-regulation of the cyd regulon duringH2O2 or menadione treatment. The cyd regulon was also up-regulated during adaption to hypoxic conditions as has beenreported for M. smegmatis (46) as well as during growth onpalmitate, indicating that up-regulation of the dosR regulonand cyd genes was not mutually exclusive. The cyd operon washighly responsive to alterations in the transmembrane protongradient by treatment with protonophores such as CCCP, DNP,and nigericin. Intracellular acidification would also affectmaintenance of the transmembrane proton gradient, and in-deed amides such as pyrazinamide also resulted in up-regula-tion of the cyd operon.

Since changes in the pyridine nucleotide or respiratory qui-nol redox poises are associated with modulation of expressionof respiratory components in a variety of bacteria (47–49), thereduced versus oxidized forms of these molecules were meas-ured with a variety of respiratory inhibitors that affected theexpression of the cyd operon or the dosR regulon (Table I). Thisindicated that the regulation of these gene sets could not besimply correlated with the redox state of these electroncarriers.

High Information Content Screening: Transcriptional Profil-ing for de Novo Mechanism of Action Determination—Ascidide-min is a marine pyridoacridone alkaloid that has cytotoxicactivity to tumor cells as well as showing antiparasitic activity(50). The mechanism of action of these compounds in eukary-otic cells has been attributed to inhibition of DNA topoisomer-ase and direct cleavage of DNA (51). Ascididemin has also beenreported to have potent antimycobacterial activity (Table II).By transcriptional profiling, ascididemin was shown to induceup-regulation of the mycobactin biosynthetic genes and af-fected transcription of several iron-associated genes. The my-cobactin genes and a few putative functionally related genesformed a gene cluster (GC108) that constituted a signatureprofile for this group of drugs that included other iron scaven-gers such as dipyridyl and desferoxamine.

The contribution of iron scavenging to the mode of action ofascididemin was confirmed by an increase in MIC when the

FIG. 7. M. smegmatis reductivepower during treatment with respi-ratory inhibitors and drugs. Cells inquadruplicate wells were treated withdrugs or vehicle alone for 15 min beforeinitiation of the MTT reduction assay.The reduced formazan product was meas-ured colorimetrically at 595 nm. Resultsshown are one of two similar independentexperiments.

TABLE IChanges in redox status of NADH/NAD� and the predominant

isoprenoid quinol/quinone pair (MK9H2/MK9) during treatment ofM. tuberculosis with various respiratory modulators

Values represent the averages and S.D. values of three independentexperiments. For values without S.D., only a single determination wasperformed.

Drug treatment-Fold change relative to

control

NADH/NAD� MK9H2/MK9

-fold

10 �g/ml KCNa 0.7 � 0.1 1.95 � 0.0420 �g/ml CPZa 0.7 � 0.1 0.69 � 0.0220 �g/ml TRZa 0.4 � 0.2 0.68 � 0.0250 �M CCCPa 1.7 � 0.4 2.020.2 mM GSNO 1.4 � 0.1 1.3 � 0.150 �g/ml TRCa 0.8 � 0.1 1.12 � 0.0550 �M nigericina 0.5 � 0.3 1.16 � 0.015 �g/ml clofaziminea 1.6 � 0.2 0.840.5 mM DNPa 1.3 1.7410 �g/ml menadione 0.2 � 0.1 0.90 � 0.01NRP-1a 0.4 3.1 � 0.50.2 mM GSNO, 10 �g/ml menadione 0.4 � 0.1 0.930.2 mM GSNO, 20 �g/ml CPZa 0.9 � 0.1 0.73 � 0.030.2 mM GSNO, 10 �g/ml KCNa 0.7 � 0.1 1.75 � 0.051 mM NaN3

a 1.8 � 0.1 1.67 � 0.26100 �M dicyclohexylcarboxidiimidea 1.9 NDb

0.04 �g/ml isoniazid 1.0 0.98 � 0.01a Treatments that resulted in up-regulation of the cyd operon.b ND, not done.



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cells were preloaded with iron (Table II). Further, a mycobac-tin-deficient mutant (14), which is impaired for iron uptake,was found to be hypersusceptible to this compound (Table II).The up-regulation of the cyd operon was not due to an inhibi-tion of the respiratory dehydrogenases (results not shown) butprobably indicates decreased electron transport through therespiratory chain due to the decreased synthesis of maturecytochromes and other iron-containing respiratory proteins.Ascididemin could be distinguished from other iron scavengersby the up-regulation of at least two gene clusters (GC44 and-116) unique to this compound. To assess whether microarrayanalysis could be used to identify the major mode of action in acrude extract containing a natural product with antimycobac-terial activity, an organic extract of Eudistoma amplum whichproduces ascididemin, was used to treat M. tuberculosis andthe resulting transcriptional profile compared with that of as-cididemin. The crude extract produced an almost identical pro-file to that of the pure molecule.

High Information Content Screening: Predicting Detoxifica-tion—Molecules with aromatic character in general were po-tent inducers of monooxygenases, dioxygenases, certain methy-lases, efflux systems, and the associated carboxylic aciddegradation genes. There was, for example, a striking up-reg-ulation of potential drug detoxification and efflux mechanismsduring TRC treatment, and many of the gene clusters up-regulated in common between the phenothiazines, azoles,DNP, CCCP, clofazimine, and TRC included genes potentiallyinvolved in drug detoxification and efflux. Gene clusters 53 and51 consisted largely of potential detoxification mechanisms.Microarray analysis indicated that the diamine analogs werepotent inducers of potential drug detoxification mechanismsand that the apparent lack of effect on cell wall arabinogalac-tan composition could also be ascribed to detoxification of theanalogs.

DISCUSSION

In this study, we analyzed the transcriptional response ofM. tuberculosis to diverse environmental alterations. Analysisof this data allowed us to select clusters of genes that werecoordinately regulated across multiple different treatmentsand to examine the responses of these gene clusters to eachclass of agents. Not only did unbiased grouping successfullycluster inhibitors of known similar mechanisms of action, butthe underlying metabolic logic for the effect of these inhibitorswas consistent with historical studies of mechanism of action ofsuch agents. As expected, genes within operons were predom-inantly associated with the same gene cluster. The co-regula-tion of unknown genes with genes of known and related func-tion allows inference to be drawn about their putativemetabolic roles.

In general, inhibitors of protein translation induce the cellto attempt to synthesize more ribosomes, reduce the turnoverand degradation of existing ribosomes, and reduce the denovo synthesis of nucleotides while enhancing nucleotide re-cycling and salvage. Although somewhat similar to the ge-netic response to starvation (5) this response is unique andindependent of ppGpp regulation. Importantly, these studiessuggest an as yet unexplored role for polyphosphate metab-olism in determining the overall status of the mycobacterialtranslational apparatus.

The cellular response to interrupting DNA supercoiling wasdirectly related to the ability of the inhibitor to induce double-stranded breaks in the chromosome. Fluoroquinolones, whichinduce such damage, strongly induce the SOS response,whereas novobiocin, which does not induce such damage, doesnot. Disruption of DNA supercoiling levels by either fluoro-quinolones or novobiocin induces genes involved in DNA syn-thesis and the synthesis of DNA precursors like deoxyribo-nucleotides. The unique regulation of nrdF1 suggests either ageneral role of this subunit in the regulation of DNA synthesislevels or a specific role in DNA synthesis during DNA turnoveror repair.

Pyrazinamide-elicited transcriptional profiles clustered withother amides such as nicotinamide and benzamide, supportingthe hypothesis that these agents exert their antimycobacterialeffect by imposing stress on the intracellular pH homeostasismechanisms (12). These aromatic amide-elicited profiles werein turn distinct from the transcriptional responses of the orga-nism due to extracellular pH stress during growth in an acidicenvironment.

Inhibition of cell wall synthesis represents a major mecha-nism of action for many existing antituberculars and has been

FIG. 8. The cyd and dosR regulonsare independent cellular responsesto inhibition of respiration. Shown isthe transcriptional response of two geneclusters associated with respiration to alltreatments. The mean z-score over all ofthe genes within the dormancy regulon(GC39, gray) and GC149 (pink) is plottedfor each drug group. The dashed lines rep-resent the median expression of the regu-lons (GC39 (gray) and GC149 (pink)) overall of the drug treatments.

TABLE IIMinimum inhibitory concentration of ascididemin and the

ascididemin-containing natural product against M. tuberculosisstrains in the presence or absence of excess iron

Values are in �g/ml.

Compound H37Rv H37Rv � Fe3� �mbtB �mbtB � Fe3�

�g/ml

Ascididemin 0.12 0.5 0.062 0.5

Organic extract 0.56 2.3 0.28 1.13



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historically a well studied area. Transcriptional profiles of allsuch inhibitors (except TRC) revealed a potent up-regulation ofcommon cell wall gene clusters (GC27 and -128) that includedthe iniBAC operon, previously shown to be responsive to suchinhibitors (3, 33). This and other genes regulated in common bythis class (interestingly, many are also up-regulated by starva-tion) are strong candidates for genes involved in cell wall turn-over, remodeling, or maintenance, possibly critical functions innonreplicating organisms. Cell wall turnover is thought to playa role during adaption to microaerophilia, and although GC27and GC128 were down-regulated during adaptation to oxygenlimitation, gene clusters implicated in fatty acid modificationand polyketide synthesis were up-regulated (GC17 and -66).TRC is the single outlier that does not appear to induce any ofthe cell wall-responsive genes that characterized the cell wallinhibitors, instead stimulating fatty acid degradation and clus-tering with respiratory inhibitors. This was confirmed by show-ing that TRC directly inhibits the membrane-bound succinatedehydrogenase but surprisingly this does not translate into aglobal effect on respiration since oxygen consumption appearsnormal in TRC-treated cells. Thus, despite evidence that TRChas a cell wall component to its mechanism of action, themechanism of toxicity is clearly more complex than previouslyappreciated (20).

The up-regulation of genes implicated in biogenesis of respi-ratory cytochromes and of the cydAB genes encoding the cyto-chrome bd quinol oxidase by CPZ, the azoles, and TRC sug-gested a common effect on electron transport. CYP121,suggested as the target of the azoles, seemed an unlikely targetdue to its absence from the susceptible M. smegmatis and itsnonessentiality in M. tuberculosis (52). For the phenothiazinesCPZ and TRZ, the similarities in transcriptional profiles withknown uncouplers and respiratory poisons suggested a directeffect on respiration. We were able to support this by directlydemonstrating that consumption of oxygen by M. tuberculosiswas in fact inhibited by these compounds and that two differentdehydrogenases of the respiratory chain were inhibited in vitro,strongly suggesting that respiratory inhibition is a major com-ponent of the mode of action of these agents. TRC and theazoles inhibited the respiratory succinate dehydrogenase butdid not inhibit oxygen consumption. The effect of these drugson respiration may be due to their hydrophobicity, which wouldtend to sequester them in the mycobacterial cell wall, consist-ent with their effect on the membrane-bound succinate dehy-drogenase complex. However, the effects of TRC and the azoleson respiratory and other membrane-associated proteins may benonspecific.

Our studies with various inhibitors also suggest some fun-damental principles underlying respiratory regulation. Thedormancy (dosR) regulon, induced by inhibition of both termi-nal oxidases by NO, is regulated by DosR and mediates adap-tation to hypoxia (4, 53). It has been suggested that the signaldetected by the cognate sensor kinase of DosR might be trans-duced by CcO (4). However, in our studies, we found that theCcO inhibitors cyanide and azide do not induce up-regulation ofthis response, whereas growth on reduced carbon sources did.This argues against CcO as the transducer of the dormancysignal and points to a sensor that detects the redox status of thecell. In some bacteria, the redox balance between quinones andquinols transduces signals to flavin-containing sensor kinases(47, 48), whereas in some other bacteria, the redox poise issensed through the NADH/NAD� ratio (49). However, wefound that neither the NADH/NAD� nor the menaquinol/menaquinone redox status was responsible for the differentialregulation of these gene sets in M. tuberculosis. These findingsdo not rule out the possibility that the redox poise of another

electron carrier is the signal that controls the regulation of atleast one of these sets of genes.

Inhibition of CcO specifically results in up-regulation of thecyd operon encoding the non-proton-pumping cytochrome bdoxidase (46). This operon was also up-regulated during adap-tion to hypoxic conditions as has been found in M. smegmatis(46) and during growth on palmitate. Such conditions would beexpected to alter the transmembrane proton gradient due tointracellular accumulation of organic acids and a protonophoreeffect of fatty acids. This effect was verified using known pro-tonophores such as CCCP, DNP, and nigericin, which specifi-cally disrupts the proton gradient of the transmembrane elec-trochemical potential. Intracellular acidification would beexpected to have a similar effect, and, in fact, amides such aspyrazinamide resulted in up-regulation of the cyd operon.

Thus, the data base of transcriptional profiles described herefor a very diverse set of drugs and growth-inhibitory conditionsprovides information that is highly consistent with historicalstudies. It has also proven useful for agents lacking historicalinformation. Transcriptional profiling of the pyridoacridone as-cididemin suggested that this compound interfered with ironacquisition, which we were able to validate directly. Moreover,transcriptional profiles were useful in highlighting key meta-bolic responses even in the face of the more complex responsesobserved with unpurified natural products. The signature ofascididemin, for example, was found to be entirely reproduciblein the crude extract of the tunicate from which it was obtained.Since a primary bottleneck in the discovery of new agents fromnatural sources lies in the resource-intensive process of isola-tion of the active principle, transcriptional profiling offers theopportunity to prioritize such extracts to those with novelmechanisms of action prior to such a commitment. This conceptof high information content screening also encompasses infor-mation regarding chemotypes that induce undesirable bacte-rial detoxification and efflux systems that could be used toprioritize hits from high throughput screening using responsesof a small number of responsive gene clusters.

The coordinately regulated gene clusters identified here rep-resent the most extensive set of regulons to date defining themetabolic potential of this important pathogen. Understandingthis potential and the plasticity of the pathogen’s response tochallenge, is critical to understanding pathogen biology to alevel sufficient to define targets against both actively replicat-ing and nonreplicating organisms. Highly responsive genes andthe list of those that are not suggest precise targets and inter-vention points for the development of a new generation ofantituberculosis agents.

Acknowledgments—We thank Mike Cashel, Valerie Mizrahi, andco-workers for stimulating discussions and Jose Ribeiro for providingthe M. tuberculosis data base. We gratefully acknowledge the assist-ance of Michael Goodwin with the menaquinol analyses. We acknowl-edge the NCI, National Institutes of Health, Natural Products Branchfor the sample from the Open and Active Repositories Program.

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