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• W2004361822 abstract "The differential transcriptional response of Mycobacterium tuberculosis to drugs and growth-inhibitory conditions was monitored to generate a data set of 430 microarray profiles. Unbiased grouping of these profiles independently clustered agents of known mechanism of action accurately and was successful at predicting the mechanism of action of several unknown agents. These predictions were validated biochemically for two agents of previously uncategorized mechanism, pyridoacridones and phenothiazines. Analysis of this data set further revealed 150 underlying clusters of coordinately regulated genes offering the first glimpse at the full metabolic potential of this organism. A signature subset of these gene clusters was sufficient to classify all known agents as to mechanism of action. Transcriptional profiling of both crude and purified natural products can provide critical information on both mechanism and detoxification prior to purification that can be used to guide the drug discovery process. Thus, the transcriptional profile generated by a crude marine natural product recapitulated the mechanistic prediction from the pure active component. The underlying gene clusters further provide fundamental insights into the metabolic response of bacteria to drug-induced stress and provide a rational basis for the selection of critical metabolic targets for screening for new agents with improved activity against this important human pathogen. The differential transcriptional response of Mycobacterium tuberculosis to drugs and growth-inhibitory conditions was monitored to generate a data set of 430 microarray profiles. Unbiased grouping of these profiles independently clustered agents of known mechanism of action accurately and was successful at predicting the mechanism of action of several unknown agents. These predictions were validated biochemically for two agents of previously uncategorized mechanism, pyridoacridones and phenothiazines. Analysis of this data set further revealed 150 underlying clusters of coordinately regulated genes offering the first glimpse at the full metabolic potential of this organism. A signature subset of these gene clusters was sufficient to classify all known agents as to mechanism of action. Transcriptional profiling of both crude and purified natural products can provide critical information on both mechanism and detoxification prior to purification that can be used to guide the drug discovery process. Thus, the transcriptional profile generated by a crude marine natural product recapitulated the mechanistic prediction from the pure active component. The underlying gene clusters further provide fundamental insights into the metabolic response of bacteria to drug-induced stress and provide a rational basis for the selection of critical metabolic targets for screening for new agents with improved activity against this important human pathogen. Despite the introduction of directly observed therapy, short course, in 1995, millions of tuberculosis patients continue to perish, and fully one-third of the world's population is infected with the causative agent of this disease, Mycobacterium tuberculosis. New drugs are urgently needed to shorten the duration of tuberculosis chemotherapy and treat the increasing number of infections with drug-resistant organisms. Target selection is critical to the development of new drugs but is hampered by a lack of understanding of the dynamics of the metabolic response 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 the physiology of the bacilli and its ability to adapt to disruption of metabolism.An organism responds to changes in its environment by altering the level of expression of critical genes that transduce such signals into metabolic changes favoring continued growth and survival. Analysis of the transcriptional response by microarray can, in theory, provide clues to such adaptive responses, but thus far gene expression profiles have only been used to contrast the mechanisms of action of a small number of related drugs (1Agarwal A.K. Rogers P.D. Baerson S.R. Jacob M.R. Barker K.S. Cleary J.D. Walker L.A. Nagle D.G. Clark A.M. J. Biol. Chem. 2003; 278: 34998-35015Abstract Full Text Full Text PDF PubMed Scopus (172) Google Scholar, 2Betts J.C. McLaren A. Lennon M.G. Kelly F.M. Lukey P.T. Blakemore S.J. Duncan K. Antimicrob. Agents Chemother. 2003; 47: 2903-2913Crossref PubMed Scopus (116) Google Scholar, 3Wilson M. DeRisi J. Kristensen H.H. Imboden P. Rane S. Brown P.O. Schoolnik G.K. Proc. Natl. Acad. Sci. U. S. A. 1999; 96: 12833-12838Crossref PubMed Scopus (492) Google Scholar). Coordinately regulated sets of genes (regulons) are often controlled by single transcriptional regulators that function as genetic master switches, committing the bacterium to a major alteration in metabolism. In M. tuberculosis, examples of such regulatory mechanisms have been reported recently from studies using genetic approaches, including the dormancy regulon (4Voskuil M.I. Schnappinger D. Visconti K.C. Harrell M.I. Dolganov G.M. Sherman D.R. Schoolnik G.K. J. Exp. Med. 2003; 198: 705-713Crossref PubMed Scopus (765) Google Scholar) and the stringent response (5Dahl J.L. Kraus C.N. Boshoff H.I. Doan B. Foley K. Avarbock D. Kaplan G. Mizrahi V. Rubin H. Barry III, C.E. Proc. Natl. Acad. Sci. U. S. A. 2003; 100: 10026-10031Crossref PubMed Scopus (290) Google Scholar). The complexity of the cellular transcriptional response to drug-induced stress makes it very difficult to derive this sort of information solely from microarray analysis of a limited number of agents affecting the same metabolic pathway (6Huang P. Feng L. Oldham E.A. Keating M.J. Plunkett W. Nature. 2000; 407: 390-395Crossref PubMed Scopus (744) Google Scholar, 7Savoie C.J. Aburatani S. Watanabe S. Eguchi Y. Muta S. Imoto S. Miyano S. Kuhara S. Tashiro K. DNA Res. 2003; 10: 19-25Crossref PubMed Scopus (47) Google Scholar). However, by analyzing a wide variety of conditions, groups of genes have been identified that appear co-regulated under many different conditions in yeast (8Segal E. Shapira M. Regev A. Pe'er D. Botstein D. Koller D. Friedman N. Nat. Genet. 2003; 34: 166-176Crossref PubMed Scopus (1289) Google Scholar). In this study, we applied genome-wide expression profiling to diverse environmental changes, including many different drug types, to begin to map the adaptability of the bacilli to interruption of specific arms of metabolism. This allowed us to identify clusters of coordinately regulated genes both diagnostic for drug mechanism of action and useful for a more rational approach to the selection of critical drug targets.EXPERIMENTAL PROCEDURESM. tuberculosis Growth Conditions, RNA Isolation, and Hybridization—M. tuberculosis (H37Rv, ATCC 27294) was grown in Middlebrook 7H9 supplemented with albumin/dextrose/NaCl/glycerol/Tween 80, Dubos medium, or defined minimal medium as previously described (9Schnappinger D. Ehrt S. Voskuil M.I. Liu Y. Mangan J.A. Monahan I.M. Dolganov G. Efron B. Butcher P.D. Nathan C. Schoolnik G.K. J. Exp. Med. 2003; 198: 693-704Crossref PubMed Scopus (1129) Google Scholar). Carbon sources were either 10 mm glucose, 10 mm succinate, or 0.05 mm sodium palmitate, and cultures were grown from an A650 of 0.005-0.3 before RNA isolation. Cultures grown under a self-depleted oxygen gradient (NRP-1) were set up as described (10Wayne L.G. Hayes L.G. Infect. Immun. 1996; 64: 2062-2069Crossref PubMed Google Scholar), and RNA was isolated after 3-6 days. Nutrient starvation cultures were set up as previously described (5Dahl J.L. Kraus C.N. Boshoff H.I. Doan B. Foley K. Avarbock D. Kaplan G. Mizrahi V. Rubin H. Barry III, C.E. Proc. Natl. Acad. Sci. U. S. A. 2003; 100: 10026-10031Crossref PubMed Scopus (290) Google Scholar) in phosphate- or Tris-buffered saline containing 0.05% Tween 80 (PBST or TBST). The organic extract of Eudistoma amplum was prepared as follows: the frozen, ground invertebrate was extracted with water at 4 °C, and the pellet was freeze-dried and then extracted at room temperature with methanol/methylene chloride (1:1). The solvent was evaporated and the extract dissolved in Me2SO to 9 mg/ml. S-Nitrosoglutathione (GSNO) 1The 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 chlorophenylhydrazone; DNP, dinitrophenol; CCO, cytochrome c oxidase; EMB, ethambutol. was used as a NO source. Cultures were grown from an A650 of 0.07-0.3 before adding either drug or solvent (1000-fold dilutions from Me2SO, ethanol, or water), and RNA was isolated at selected intervals thereafter (11Boshoff H.I. Reed M.B. Barry III, C.E. Mizrahi V. Cell. 2003; 113: 183-193Abstract Full Text Full Text PDF PubMed Scopus (327) Google Scholar). For each drug-treated culture, a parallel culture was treated with an equivalent amount of vehicle (Me2SO, ethanol, or water) for the same amount of time. RNA from the latter culture was used as the reference sample to which the drug-treated sample was compared. Each treatment condition and each drug concentration was repeated a minimum of two independent times.M. tuberculosis carrying an integrated copy of the Mycobacterium smegmatis amidase pzaA, which hydrolyzes several aromatic amides (12Boshoff H.I. Mizrahi V. J. Bacteriol. 2000; 182: 5479-5485Crossref PubMed Scopus (39) Google Scholar), was used for cultures treated with pyrazinamide, 5-chloropyrazinamide, nicotinamide, or benzamide. This strain was also used to investigate the transcriptional response during extracellular pH stress. Treatment was done in Middlebrook 7H9-based medium adjusted to the required pH (4.8, 5.2, or 5.6) with H3PO4 and referenced to cells grown in the same acidified medium without amide addition. Since pyrazinamide is only effective at low cell densities, cultures were grown to an A650 of 0.05 before treatment was initiated. MICs were measured using the microbroth dilution technique (13Lee R.E. Protopopova M. Crooks E. Slayden R.A. Terrot M. Barry III, C.E. J. Combinatorial Chem. 2003; 5: 172-187Crossref PubMed Scopus (206) Google Scholar) using H37Rv or M. tuberculosis ΔmbtB (14De Voss J.J. Rutter K. Schroeder B.G. Su H. Zhu Y. Barry III, C.E. Proc. Natl. Acad. Sci. U. S. A. 2000; 97: 1252-1257Crossref PubMed Scopus (462) Google Scholar). Iron preloading of cells was done for 3 h in 7H9-based medium containing a 10-fold excess of Fe3+. RNA labeling and hybridization was as previously described (11Boshoff H.I. Reed M.B. Barry III, C.E. Mizrahi V. Cell. 2003; 113: 183-193Abstract Full Text Full Text PDF PubMed Scopus (327) Google Scholar).Microarray Preparation and Data Analysis—Microarray preparation is described under GEO accession number GSE1642. Expression ratios were calculated as the feature pixel median minus background pixel median for one color channel divided by the same for the other channel. In cases where more than 10% of the feature pixels were saturated, the feature pixel mean was used instead of the median. When the feature pixel mean did not exceed the background pixel mean by more than two S.D. values (calculated from the background pixel distribution), the feature pixel median is used in the ratio without background subtraction. In cases where both color channels were near background (same criterion), the ratio value was set to “missing.” Expression ratios were transformed to the log base 2 for all further calculations.Standardized gene expression ratio patterns were calculated by subtracting the mean expression ratio and dividing by the S.D. statistics calculated from all ratios (all microarrays) for that gene. Standardizing in this way corrected for scale differences between the response patterns for different genes. The resulting z-scores were averaged according to the drug treatment name, resulting in a single value for each drug name for each gene (see Supplementary Data). These gene patterns where then clustered using a K-means algorithm (SAS Proc Fastclus) using the Euclidean distance as the dissimilarity metric. Two rounds of K-means clustering were conducted. The first with the subset of genes showing the highest treatment-dependent variation in expression as judged by one-way analysis of variance (SAS Proc ANOVA) on the original log ratio vectors, using treatment name as the class variable. The second round used all genes, but without allowing the cluster number to increase or the cluster centroids to drift (assigning the remaining genes to the existing clusters formed in the first round of clustering). We arrived at the Fastclus “maxclusters” parameter, the maximum number of clusters to define, value of 150 clusters, by multiplying the number of class levels (treatments), 75, by 2. The number of genes selected for the first round of clustering (1650) was limited to 11 times the number of clusters and were those with the most statistically significant one-way analysis of variance score. A single pattern of response for each gene cluster was calculated as the mean of all standardized gene patterns assigned to the cluster by Proc Fastclus. These cluster centroids were themselves clustered using average linkage algorithm calculated in Microsoft Excel VBA using a one minus the Pearson correlation coefficient for the distance metric (15Kaufman L. Rousseeuw P.J. Finding Groups in Data: An Introduction to Cluster Analysis. 9th Ed. John Wiley & Sons, Inc., New York1990Crossref Google Scholar) to arrive at the ordering of rows in Fig. 3 (for details, see Supplementary Data). Patterns of response to each treatment were clustered using the same method to arrive at the column order in Fig. 3. The array data have been deposited in the Gene Expression Omnibus at NCBI (GEO; available on the World Wide Web at www.ncbi.nlm.nih.gov/geo) with GEO accession number GSE1642.Real Time, Quantitative Reverse Transcription-PCR Assay—The expression of iniB, Rv3161, kasA, efpA, fadE23, rplJ, rplN, dnaE2, radA, mbtB, csd, narH, narG, cydA, and ald was quantitated after normalization of RNA levels to the expression of the sigA gene as previously described (11Boshoff H.I. Reed M.B. Barry III, C.E. Mizrahi V. Cell. 2003; 113: 183-193Abstract Full Text Full Text PDF PubMed Scopus (327) Google Scholar), and results are available in Supplementary Data.Enzyme Assays—Enzyme assays were done on proteins from M. smegmatis. Purification and determination of NADH dehydrogenase and succinate dehydrogenase activities was performed as previously described (16Miesel L. Weisbrod T.R. Marcinkeviciene J.A. Bittman R. Jacobs Jr., W.R. J. Bacteriol. 1998; 180: 2459-2467Crossref PubMed Google Scholar).Oxygen Consumption Assays—The effect of drugs on oxygen consumption 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 of oxygen consumption was measured in M. smegmatis using midlog stage cultures treated with drug for 1 h before adding 0.01% methylene blue and monitoring decolorization at 665 nm.Sugar Analysis—Cell walls were prepared, and glycosyl compositions were determined by the alditol acetate methods as described (17Deng L. Mikusova K. Robuck K.G. Scherman M. Brennan P.J. McNeil M.R. Antimicrob. Agents Chemother. 1995; 39: 694-701Crossref PubMed Scopus (133) Google Scholar).NADH/NAD+ Determination—NADH and NAD+ levels were determined by a sensitive cycling assay (18San K.Y. Bennett G.N. Berrios-Rivera S.J. Vadali R.V. Yang Y.T. Horton E. Rudolph F.B. Sariyar B. Blackwood K. Metab. Eng. 2002; 4: 182-192Crossref PubMed Scopus (222) Google Scholar). Briefly, M. tuberculosis cultures were grown to an A650 of 0.3 and treated with drugs for 3 h. At this stage, cells were rapidly harvested (two 2-ml samples) and resuspended in 0.2 m HCl (NAD+ determination) or 0.2 m NaOH (NADH determination). Nucleotide extraction was further facilitated by bead beating of the suspensions with 0.2-ml glass beads (40 s, maximum speed). Extracts were further prepared, and enzyme assays were performed as previously described (18San K.Y. Bennett G.N. Berrios-Rivera S.J. Vadali R.V. Yang Y.T. Horton E. Rudolph F.B. Sariyar B. Blackwood K. Metab. Eng. 2002; 4: 182-192Crossref PubMed Scopus (222) Google Scholar). All determinations were repeated in at least three 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 and menaquinol were extracted as described (19Krivankova L. Dadak V. Methods Enzymol. 1980; 67: 111-114Crossref PubMed Scopus (11) Google Scholar) and quantified by liquid chromatography-mass spectrometry (Hewlett-Packard 1100) using a C18 column with detection by DAD at UV 266 nm. All extractions were repeated at least three independent times.Intracellular ATP Determinations—These assays were done on cultures of M. tuberculosis containing an integrated luciferase gene from pMV306-groELluc grown to an A650 of 0.1-0.2 and treated with drug for 20-120 min. ATP levels were determined by bioluminescence as previously described (20Slayden R.A. Lee R.E. Barry III, C.E. Mol. Microbiol. 2000; 38: 514-525Crossref PubMed Scopus (136) Google Scholar).MTT Assays—M. smegmatis at an A650 of 0.2 was treated with drug or vehicle alone for 15 min (100 μl/well in 96-well plates in quadruplicate) before the addition of 25 μl of 2 mg/ml MTT. The reaction was stopped after 30 min by the addition of 25 μl of 10% SDS, and the absorbance at 595 nm was recorded. The assay was repeated two independent times.RESULTSDe Novo Analysis of Expression Data Results in Mechanism of Action-based Clustering of Transcriptional Responses—Transcriptional profiling of M. tuberculosis was performed using 430 whole-genome microarrays to measure the effects of 75 different drugs, drug combinations, or different growth conditions at various times relative to a sample of logarithmically growing M. tuberculosis. The drug concentrations and time points (see Supplementary Data) were chosen such that 10% or less of the total number of genes were differentially (2-fold or more) regulated and through consulting previously published studies (2Betts J.C. McLaren A. Lennon M.G. Kelly F.M. Lukey P.T. Blakemore S.J. Duncan K. Antimicrob. Agents Chemother. 2003; 47: 2903-2913Crossref PubMed Scopus (116) Google Scholar, 3Wilson M. DeRisi J. Kristensen H.H. Imboden P. Rane S. Brown P.O. Schoolnik G.K. Proc. Natl. Acad. Sci. U. S. A. 1999; 96: 12833-12838Crossref PubMed Scopus (492) Google Scholar). Expression of highly responsive genes within certain drug groups was confirmed by quantitative reverse transcription-PCR (see Supplementary Data). The quality of these data were tested using Pearson rank tests, which verified that arrays within each treatment group were highly correlated (Fig. 1). Log-transformed expression ratios were standardized according to the pan-array distribution for each gene and then averaged according to treatment. To analyze these data, unsupervised clustering methods were applied to the 345 expression profiles. Expression data sets of genes that were up or down-regulated at least 3-fold in four or more experiments were analyzed by agglomerative hierarchical clustering method (21Sherlock G. Curr. Opin. Immunol. 2000; 12: 201-205Crossref PubMed Scopus (321) Google Scholar) to identify drug groupings based on gross analysis of coordinately regulated genes (Fig. 2). This revealed that groups of drugs clustered separately based on known mechanisms of action. Thus, protein synthesis inhibitors, transcriptional inhibitors, aromatic amides, cell wall synthesis inhibitors and agents that damage DNA fell into distinct groups. These observations were further supported by a prediction matrix using the Pearson correlation over the entire array of genes to predict the treatment group (Fig. 1). This showed that the individual profiles of drug treatments were accurately classified to groups of agents with similar predicted modes of action. The majority of apparent “mispredictions” of well characterized inhibitors in this matrix, usually correlated with agents within the same treatment group.Fig. 1Prediction matrix using Pearson correlation over the entire array of genes to assess accuracy of classification of individual arrays. Individual arrays from each treatment group (columns, n = 430) were compared with the collection of averaged array representation 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 treatment group. Major treatment groups (class of inhibitor) are color-coded as follows: violet, growth in minimal medium with palmitate or succinate as carbon sources as compared with glucose as carbon source; lavender, growth in acidified medium; blue, aromatic amides that can be hydrolyzed intracellularly; light blue, agents that inhibit cell wall synthesis; pale blue, agents that affect DNA integrity or topology; green, inhibitors of protein synthesis; yellow, growth under conditions that were associated with expression of the dosR regulon; light green, agents besides NO that inhibit respiration, and TRC; pink, transcriptional inhibitors; orange, nutrient starvation in PBST or TBST; red, pyridoacridones and iron scavengers. The black boxes are correct assignments within the treatment class. DIPED, diisopropylethylenediamine; DTNB, dithiobis(nitrobenzoic) acid.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Fig. 2Cellular transcriptional responses cluster by drug mechanism of action. Average linkage clustering of expression profiles of genes that were up- or down-regulated at least 3-fold in four or more experiments. Profiles were clustered using a modified uncentered Pearson correlation coefficient as the similarity metric. The major drug groups are color-coded as in Fig. 1. Pyridoacridone clusters 1 and 2 correspond to low (5× MIC) and high (10× MIC) concentrations, respectively. CPZ and TRZ profiles correspond to concentrations of 1-2× MIC (1) and 2-3× MIC (2).View Large Image Figure ViewerDownload Hi-res image Download (PPT)Identification of Co-regulated Genes Reveals Validated Regulons—To identify biologically meaningful groups of genes, profiles were partitioned into 75 drug groups with each drug group corresponding to a single type of treatment, and genes were analyzed by K-means clustering to uncover those with similar expression patterns across these sets (Fig. 3). Many of these clusters contained genes that were functionally related, but many also contained genes that encoded proteins of unknown function.Genes previously shown to be members of the DosR- and RecA-controlled regulons (4Voskuil M.I. Schnappinger D. Visconti K.C. Harrell M.I. Dolganov G.M. Sherman D.R. Schoolnik G.K. J. Exp. Med. 2003; 198: 705-713Crossref PubMed Scopus (765) Google Scholar, 11Boshoff H.I. Reed M.B. Barry III, C.E. Mizrahi V. Cell. 2003; 113: 183-193Abstract Full Text Full Text PDF PubMed Scopus (327) Google Scholar, 22Davis E.O. Dullaghan E.M. Rand L. J. Bacteriol. 2002; 184: 3287-3295Crossref PubMed Scopus (79) Google Scholar) were independently identified by this process of gene clustering. Gene cluster 39 (GC39), for example, contained 21 of the 48 members of the dormancy regulon, and three closely linked clusters (GC126, -56, and -137) contained 38 genes previously reported to be up-regulated by DNA damage.The Metabolic Response to Inhibition of Translation—Analysis of the cellular response to translational inhibition revealed a general down-regulation of macromolecular synthesis, as expected, although there was an evident attempt to increase synthesis of the translational apparatus. Up-regulated genes included those implicated in ribosomal architecture and translation (e.g. GC28, -36, -70, -71, -90, and -118) whereas down-regulated genes included aspects of macromolecular metabolism similar to those responsive to starvation (e.g. ppk and relA). Regulation of these genes did not appear to be mediated by the stringent response through ppGpp, since relA was down-regulated. Interestingly, Rv1026 (encoding a possible pppGpp-5′-phosphohydrolase that would hydrolyze any residual mediator of the stringent response (23Primm T.P. Andersen S.J. Mizrahi V. Avarbock D. Rubin H. Barry III, C.E. J. Bacteriol. 2000; 182: 4889-4898Crossref PubMed Scopus (275) Google Scholar)) was up-regulated during translational inhibition and down-regulated during starvation. The observed up-regulation of the inorganic pyrophosphatase encoded by ppa would probably slow ribosomal degradation (24Kuroda A. Nomura K. Ohtomo R. Kato J. Ikeda T. Takiguchi N. Ohtake H. Kornberg A. Science. 2001; 293: 705-708Crossref PubMed Scopus (290) Google Scholar) and also contrasts with the down-regulation of this gene during starvation. Not surprisingly, ppa is part of a regulon (GC71) containing genes implicated in translation, and, combined with the observed down-regulation of ppk (a polyphosphate kinase), this suggests an important role for polyphosphate in mycobacterial adaption to translational inhibition (25Zhang H. Ishige K. Kornberg A. Proc. Natl. Acad. Sci. U. S. A. 2002; 99: 16678-16683Crossref PubMed Scopus (169) Google Scholar).A gene cluster containing the gene encoding the regulatory protein of pyrimidine biosynthesis (pyrR) (GC69) was also up-regulated, consistent with the observed down-regulation of expression 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 to deoxyribonucleotides (nrdF1, nrdF2, nrdH, and nrdI) were up-regulated upon translational inhibition.Aminoglycosides were associated with an up-regulation of heat shock proteins (GC134), presumably resulting from mis-translation-induced aberrant peptides in the cytoplasm as has been observed for other bacteria (26Ng T.B. Lam S.K. Fong W.P. Biol. Chem. 2003; 384: 289-293Crossref PubMed Scopus (53) Google Scholar). Tetracycline and roxithromycin did not induce this response, consistent with the fact that they block release of the nascent peptide during translational inhibition.Our analysis also suggested that translational inhibition results in inhibition of DNA replication and the processing of replication forks. The down-regulation of several genes supports this hypothesis, including the following: Rv1708 (possible role in initiation of replication); the major replicative DNA polymerase (11Boshoff H.I. Reed M.B. Barry III, C.E. Mizrahi V. Cell. 2003; 113: 183-193Abstract Full Text Full Text PDF PubMed Scopus (327) Google Scholar); and DnaA, which plays a role in initiation of chromosomal replication. Likewise, genes implicated in turnover of DNA were up-regulated, including nth, recR, hupB, recF, and ssb. This did not result in a signal that was relayed as DNA damage, however, since recA and dnaE2 (11Boshoff H.I. Reed M.B. Barry III, C.E. Mizrahi V. Cell. 2003; 113: 183-193Abstract Full Text Full Text PDF PubMed Scopus (327) Google Scholar) were not up-regulated.The Metabolic Response to Inhibitors of DNA Transcription and Gyrase Function—Unsurprisingly, the mode of action of transcriptional inhibitors such as rifamycins could best be described as a global down-regulation of most gene clusters, including the ribonucleotide reductase genes (GC49), heat shock proteins (GC134), and several ribosomal genes. Despite this, some transcript levels were elevated, but this was probably due to differential mRNA stabilities.Fluoroquinolones bind gyrase and topoisomerase IV on DNA, blocking transcription and replication and resulting in DNA damage (27Drlica K. Curr. Opin. Microbiol. 1999; 2: 504-508Crossref PubMed Scopus (250) Google Scholar). DNA damage also results from treatment with UV irradiation, H2O2, and mitomycin C. All of these treatments resulted in the up-regulation of the previously characterized (11Boshoff H.I. Reed M.B. Barry III, C.E. Mizrahi V. Cell. 2003; 113: 183-193Abstract Full Text Full Text PDF PubMed Scopus (327) Google Scholar, 28Rand L. Hinds J. Springer B. Sander P. Buxton R.S. Davis E.O. Mol. Microbiol. 2003; 50: 1031-1042Crossref PubMed Scopus (117) Google Scholar) SOS gene clusters (GC56, -126, and -137) as well as several DNA repair-associated genes that were not correlated with this regulon. The gyrase inhibitor novobiocin does not induce double-stranded breaks (29Gormley N.A. Orphanides G. Meyer A. Cullis P.M. Maxwell A. Biochemistry. 1996; 35: 5083-5092Crossref PubMed Scopus (155) Google Scholar) and did not cluster with those agents that did. Novobiocin affected the expression of a more limited subset of DNA repair or structural maintenance genes including the up-regulation of the RecA-independent, Y family polymerase member encoded by dinP. The effects of fluoroquinolones (including novobiocin) could be distinguished from the other forms of DNA damage employed in this study by the unique up-regulation of the class Ib ribonucleotide reductase genes (GC49) as well as nrdF1.Deoxyribonucleotide pools are regulated by the activity of ribonucleotide reductase and are" @default.
• W2004361822 created "2016-06-24" @default.
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• W2004361822 date "2004-09-01" @default.
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• W2004361822 title "The Transcriptional Responses of Mycobacterium tuberculosis to Inhibitors of Metabolism" @default.
• W2004361822 cites W1000140396 @default.
• W2004361822 cites W1484179725 @default.
• W2004361822 cites W1566042048 @default.
• W2004361822 cites W1577945616 @default.
• W2004361822 cites W1600329457 @default.
• W2004361822 cites W1777183573 @default.
• W2004361822 cites W1876742335 @default.
• W2004361822 cites W1892799384 @default.
• W2004361822 cites W1970472404 @default.
• W2004361822 cites W1979767547 @default.
• W2004361822 cites W1984734453 @default.
• W2004361822 cites W2007097682 @default.
• W2004361822 cites W2008186432 @default.
• W2004361822 cites W2011128355 @default.
• W2004361822 cites W2012185637 @default.
• W2004361822 cites W2014578355 @default.
• W2004361822 cites W2023928413 @default.
• W2004361822 cites W2029149752 @default.
• W2004361822 cites W2036510321 @default.
• W2004361822 cites W2041680056 @default.
• W2004361822 cites W2057015838 @default.
• W2004361822 cites W2057319400 @default.
• W2004361822 cites W2059897759 @default.
• W2004361822 cites W2071604760 @default.
• W2004361822 cites W2077770882 @default.
• W2004361822 cites W2082520438 @default.
• W2004361822 cites W2085662298 @default.
• W2004361822 cites W2085684476 @default.
• W2004361822 cites W2091300474 @default.
• W2004361822 cites W2093574265 @default.
• W2004361822 cites W2099183065 @default.
• W2004361822 cites W2103282423 @default.
• W2004361822 cites W2106248803 @default.
• W2004361822 cites W2110215329 @default.
• W2004361822 cites W2116738290 @default.
• W2004361822 cites W2122171540 @default.
• W2004361822 cites W2123084649 @default.
• W2004361822 cites W2123233366 @default.
• W2004361822 cites W2128482141 @default.
• W2004361822 cites W2132866491 @default.
• W2004361822 cites W2137144551 @default.
• W2004361822 cites W2143971149 @default.
• W2004361822 cites W2147185188 @default.
• W2004361822 cites W2152472678 @default.
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