Abstract
Most bacteria coordinately regulate gene expression as an adaptive response to a variety of environmental changes. One key environmental cue is the carbon source necessary for central metabolism. We used microarray analysis to monitor the global transcriptional response of the obligate intracellular pathogen Chlamydia trachomatis to the presence of glycolytic and gluconeogenic carbon sources. In contrast to free-living bacteria, changing the carbon source from glucose to glutamate or α-ketoglutarate had little effect on the global gene transcription of C. trachomatis.
Chlamydiae are unusual among bacteria in that they are obligate intracellular organisms with a complex developmental cycle. Chlamydiae develop and grow within an intracellular vacuole, called an inclusion, that is distinct from other intracellular vacuolar compartments (13). The developmental cycle begins with a metabolically inactive infectious form called the elementary body (EB) that, after entry into the target cell, differentiates into a metabolically active form called the reticulate body (RB). After multiple rounds of division, the RB then differentiates back into the EB developmental form. Once the host cell finally lyses, the infectious EBs are released to initiate new rounds of infection (3).
Many organisms have developed sophisticated mechanisms that enable them to sense the nutritional status of their environment and adjust their catabolic capacities by regulatory responses at the transcriptional level (1). Bacteria coordinately regulate genes depending on the carbon source present through a process known as catabolite repression (17, 18). When glucose becomes limiting, bacteria exhibit a transcriptional shift in which genes that are initially repressed by glucose become induced. Such adaptive processes have been intensely investigated in model organisms, such as Escherichia coli and Bacillus subtilis (12, 18). Recently, with the use of DNA microarray technology, these studies have been expanded to the genomic level where it was found that a conglomeration of gene families are involved in these adaptive processes (9, 15).
When glucose is limiting in the environment, bacteria may utilize a wide variety of substrates, including various sugars, amino acids, and dicarboxylic acids that can serve as gluconeogenic carbon sources (1, 17). Analysis of the chlamydial genome suggested that host-derived glucose-6-phosphate is the primary carbon and energy source used to support chlamydial growth (16). It was also noted that the chlamydial genome contains key gluconeogenic enzymes, suggesting that host-derived glutamate or dicarboxylic acids may support chlamydial growth (16). This hypothesis has been tested experimentally by Iliffe-Lee et al. (4). They demonstrated that gluconeogenic substrates, such as glutamate and α-ketoglutarate, support chlamydial growth and showed that the expression patterns of six genes involved in central metabolism that remained unaltered in response to changes in the type of carbon source available; however, global gene families were not tested.
Microarrays containing 875 validated chlamydial genes and 8 genes of the chlamydial plasmid (8) were used to monitor the global transcriptional response of Chlamydia trachomatis to the presence of glucose, glutamate, or α-ketoglutarate as the sole carbon source available in the medium provided to infected L929 cells. To characterize the transcriptional response of C. trachomatis grown with these carbon sources, we compared the Chlamydia-specific RNA from infected cells cultivated in 20 mM glutamate or 20 mM α-ketoglutarate to Chlamydia-specific RNA from cells cultivated in 10 mg of glucose per ml. Previous data have demonstrated that all chlamydial genes are expressed by 24 h postinfection (hpi) (8); therefore, it was decided to culture C. trachomatis-infected cells for 24 hpi prior to changing the available carbon source. L929 cells were infected with C. trachomatis L2/434/Bu at a multiplicity of infection of 1 and cultured for 24 hpi, at which time the infected cells were washed three times with sterile phosphate-buffered saline and suspended in glucose-free Dulbecco's modified Eagle medium supplemented with 5 mM pyruvate and either 10 mg of glucose per ml, 20 mM glutamate, or 20 mM α-ketoglutarate.
To ensure that a 6-h incubation in the various carbon sources tested would be enough time to produce a measurable biologic effect, the number of infectious EB progeny present at this time was determined by infectivity titration assays. Briefly, C. trachomatis EBs isolated from each carbon source were titrated onto fresh, confluent monolayers grown on coverslips. After 24 h, cells were fixed and stained with an antichlamydia monoclonal antibody (Syva; MicroTrak), and the number of inclusions was determined by fluorescence microscopy. After only 6 h of exposure to an alternative carbon source, we found a 48% reduction in the number of inclusion-forming units (IFUs) recovered from the cells cultured in glutamate and a 33% reduction in the number of IFUs recovered from cells cultured in α-ketoglutarate compared to the IFUs from cells cultured in the presence of glucose. These results suggest that exposure to the carbon sources tested should have an effect on gene expression by 6 h; therefore, infected L929 cells were cultured in either 10 mg of glucose per ml, 20 mM glutamate, or 20 mM α-ketoglutarate for 6 h at which time chlamydia-specific RNA was isolated as previously described (8). For each hybridization, equal quantities of cDNA generated from cells grown with glutamate or α-ketoglutarate were compared to equal quantities of cDNA generated from cells grown with glucose. At least three independent infections in which chlamydiae were harvested from infected cells grown with each specific carbon source were performed. Gene expression data were then normalized to 16S rRNA.
The expression profiles for all the genes involved in carbon metabolism and transport are listed in Table 1. Minimal or no changes in central metabolism gene expression levels were found when cells were cultivated in the presence of glutamate as the sole carbon source versus glucose (Table 1). Additionally, minimal transcriptional changes were also found on a global scale (data not shown). Statistical significance was assessed using the significance analysis of microarray program by Tusher et al. (19). Of the entire genome, only four genes were found to be significantly upregulated (q value of 0.04) (19): CT084 (+1.94-fold), CT198 oppA (+1.91-fold), CT451 cdsA (+1.84-fold), and CT057 gcpE (+1.74-fold). Although these genes were reproducibly and reliably upregulated, each gene displayed low fold changes (i.e., less than twofold) in gene expression. Conversely, no genes were significantly downregulated (q value of 0.04) (19). C. trachomatis has a conserved 7.5-kb plasmid (2, 11, 14), which has been proposed to play a role in glycogen accumulation, since strains of C. trachomatis lacking the plasmid no longer accumulate glycogen (6). Plasmid-specific genes had no changes in gene transcription when cells were cultivated in the presence of glucose compared to glutamate as the sole carbon source (data not shown).
TABLE 1.
Changes in expression of central carbohydrate metabolism genes of C. trachomatisa
Gene category, ORF no., and gene nameb | Putative functionb | Change in expression (fold change)c
|
|
---|---|---|---|
Glutamate | α-Ketoglutarate | ||
Aerobic | |||
CT278 nqr2 | NADH-ubiquinone oxidoreductase | 1.48 ± 0.01 | −1.33 ± 0.02 |
CT279 nqr3 | NADH-ubiquinone oxidoreductase | 1.33 ± 0.01 | −1.56 ± 0.02 |
CT280 nqr4 | NADH-ubiquinone oxidoreductase | 1.26 ± 0.02 | −1.45 ± 0.02 |
CT281 nqr5 | NADH-ubiquinone oxidoreductase | 1.05 ± 0.01 | −1.70 ± 0.03 |
CT634 nqrA | NADH-ubiquinone oxidoreductase | 1.30 ± 0.01 | −1.08 ± 0.01 |
CT714 gpdA | Glycerol-3-phosphate dehydrogenase | 1.26 ± 0.03 | −1.46 ± 0.01 |
CT740 nqr6 | NADH-ubiquinone oxidoreductase | 1.21 ± 0.01 | −1.59 ± 0.02 |
ATP biogenesis and metabolism | |||
CT065 adt | ADP/ATP translocase | 1.59 ± 0.02 | −1.04 ± 0.04 |
CT304 atpK | V-type ATP synthase subunit K | 1.31 ± 0.02 | −1.34 ± 0.04 |
CT305 atpI | V-type ATP synthase subunit I | 1.17 ± 0.01 | −2.11 ± 0.06 |
CT306 atpD | V-type ATP synthase subunit D | 1.37 ± 0.03 | −1.43 ± 0.02 |
CT307 atpB | V-type ATP synthase subunit B | 1.03 ± 0.05 | −1.81 ± 0.03 |
CT308 atpA | V-type ATP synthase subunit A | 1.56 ± 0.03 | −1.63 ± 0.03 |
CT310 atpE | V-type ATP synthase subunit E | 1.27 ± 0.01 | −1.63 ± 0.03 |
CT495 adt | ADP/ATP translocase | 1.47 ± 0.02 | −1.17 ± 0.01 |
CT719 fliF | Flagellar M-ring-like protein | 1.18 ± 0.03 | −1.48 ± 0.01 |
Electron transport chain | |||
CT013 cydA | Cytochrome oxidase subunit I | 1.18 ± 0.05 | −1.30 ± 0.02 |
CT014 cydB | Cytochrome oxidase subunit II | 1.17 ± 0.04 | −1.33 ± 0.01 |
CT059 fer | Ferredoxin | 1.44 ± 0.01 | −1.41 ± 0.01 |
CT312 fer | Predicted ferredoxin | 1.20 ± 0.05 | −1.24 ± 0.04 |
Glycogen metabolism | |||
CT042 glgX | Glycogen hydrolase | 1.54 ± 0.03 | −1.35 ± 0.05 |
CT087 malQ | 4-α-Glucanotransferase | 1.30 ± 0.02 | −1.08 ± 0.06 |
CT248 glgP | Glycogen phosphorylase | 1.09 ± 0.03 | −1.52 ± 0.01 |
CT489 glgC | Glucose-1-phosphate adenylyltransferase | −1.03 ± 0.02 | −1.06 ± 0.09 |
CT710 pckA | Phosphoenolpyruvate carboxykinase | 1.21 ± 0.03 | −1.57 ± 0.01 |
CT715 | UDP glucose pyrophosphorylase | 1.18 ± 0.01 | −1.38 ± 0.01 |
CT798 glgA | Glycogen synthase | 1.04 ± 0.02 | −1.22 ± 0.01 |
CT866 glgB | 1,4-α-Glucan branching enzyme | 1.32 ± 0.03 | −1.13 ± 0.02 |
Glycolysis and gluconeogenesis | |||
CT205 pfkA | Pyrophosphate-fructose-6-phosphate 1-phosphotransferase | 1.32 ± 0.05 | −1.65 ± 0.18 |
CT207 pfkA | Pyrophosphate-fructose-6-phosphate 1-phosphotransferase | 1.49 ± 0.10 | −1.36 ± 0.16 |
CT215 dhnA | Predicted aldolase | 1.56 ± 0.08 | −1.61 ± 0.16 |
CT295 yhxB | Phosphomannomutase | 1.19 ± 0.04 | −1.77 ± 0.01 |
CT328 tpiA | Triosephosphate isomerase | 1.28 ± 0.01 | −1.24 ± 0.01 |
CT332 pykF | Pyruvate kinase | 1.32 ± 0.02 | −1.17 ± 0.01 |
CT378 pgi | Glucose-6-phosphate isomerase | 1.13 ± 0.03 | −1.26 ± 0.02 |
CT505 gapA | Glyceraldehyde-3-phosphate dehydrogenase | 1.25 ± 0.01 | −1.73 ± 0.01 |
CT587 eno | Enolase | 1.23 ± 0.05 | −1.44 ± 0.08 |
CT693 pgk | Phosphoglycerate kinase | −1.06 ± 0.01 | −1.63 ± 0.01 |
CT722 pgm | Phosphoglycerate mutase | 1.39 ± 0.01 | −1.37 ± 0.01 |
CT815 mrsA | Phosphoglucomutase | 1.13 ± 0.02 | −1.33 ± 0.01 |
Pentose-phosphate pathway | |||
CT063 gnd | 6-Phosphogluconate dehydrogenase | 1.21 ± 0.02 | −1.45 ± 0.01 |
CT121 araA | Ribulose-3-phosphate epimerase | 1.27 ± 0.03 | −1.45 ± 0.01 |
CT185 zwf | Glucose-6-phosphate dehydrogenase | 1.42 ± 0.01 | −1.76 ± 0.03 |
CT186 devB | Glucose-6-phosphate dehydrogenase | 1.26 ± 0.01 | −1.67 ± 0.02 |
CT213 rpiA | Ribose-5-phosphate isomerase A | 1.12 ± 0.05 | 1.11 ± 0.38 |
CT313 tal | Transaldolase | −1.01 ± 0.01 | −1.42 ± 0.01 |
CT331 dxs | 1-Deoxy-d-xylulose-5-phosphate synthase | 1.65 ± 0.04 | −1.70 ± 0.01 |
CT750 tktB | Transketolase | 1.25 ± 0.03 | −1.61 ± 0.03 |
Pyruvate dehydrogenase | |||
CT245 pdhA | Pyruvate dehydrogenase E1 alpha | 1.40 ± 0.01 | −1.24 ± 0.01 |
CT246 pdhB | Pyruvate dehydrogenase E1 beta | 1.36 ± 0.03 | −1.64 ± 0.01 |
CT247 pdhC | Dihydrolipoamide acetyltransferase | 1.32 ± 0.01 | −1.54 ± 0.01 |
CT285 lplA | Lipoate protein ligase | 1.48 ± 0.01 | −1.35 ± 0.04 |
CT340 pdhA | Pyruvate dehydrogenase alpha and beta fusion | 1.35 ± 0.01 | −1.34 ± 0.01 |
CT499 lplA | Lipoate protein ligase | 1.24 ± 0.05 | −1.54 ± 0.02 |
CT557 lpdA | Lipoamide dehydrogenase | 1.47 ± 0.01 | −1.59 ± 0.04 |
Tricarboxylic acid cycle | |||
CT054 sucA | 2-Oxoglutarate dehydrogenase E1 | 1.20 ± 0.01 | −1.53 ± 0.01 |
CT055 sucB | Dihydrolipoamide succinyltransferase E2 | −1.16 ± 0.02 | −1.83 ± 0.02 |
CT376 mdh | Malate dehydrogenase | 1.16 ± 0.02 | −1.20 ± 0.01 |
CT390 aspC | Aspartate aminotransferase | 1.31 ± 0.02 | −1.45 ± 0.03 |
CT400 sucB | Dihydrolipoamide succinyltransferase E2 | 1.24 ± 0.04 | −1.39 ± 0.01 |
CT591 sdhB | Succinate dehydrogenase iron-sulfur protein | −1.19 ± 0.08 | −1.62 ± 0.05 |
CT592 sdhA | Succinate dehydrogenase flavoprotein | 1.19 ± 0.02 | −1.36 ± 0.04 |
CT593 sdhC | Succinate dehydrogenase cytochrome subunit | 1.21 ± 0.03 | −1.40 ± 0.09 |
CT821 sucC | Succinyl-CoAd synthetase beta chain | 1.13 ± 0.01 | −1.46 ± 0.03 |
CT822 sucD | Succinyl-CoA synthetase alpha chain | 1.11 ± 0.01 | −1.50 ± 0.02 |
CT855 fumC | Fumarate dehydratase class II | 1.14 ± 0.01 | −1.50 ± 0.04 |
At least three independent infections in which chlamydiae were harvested from infected cells from each specific carbon source were performed. Data for duplicate readings and each hybridization experiment were normalized based on the total percent intensity to eliminate slide-to-slide variation. Gene expression data were then normalized 16S rRNA. The statistical significance of the gene expression changes observed during the presence of glutamate versus glucose and α-ketoglutarate versus glucose were assessed by using the significant analysis of microarrays as previously described (19).
Gene category, open reading frame (ORF) number, gene name, and putative function were assigned by Stephens et al. (16).
Change in expression of cells grown with glutamate or α-ketoglutarate as the sole carbon source compared to expression in cells grown with glucose as the sole carbon source. Change in expression is shown as the mean fold change ± standard deviation.
CoA, coenzyme A.
When the expression profiles from cells cultured in the presence of 20 mM α-ketoglutarate as the sole carbon source were tested, very little change in global gene transcription was observed, including genes involved in central carbohydrate metabolism (Table 1). Additionally, the dicarboxylate-specific porin gene porB, which has been shown to transport α-ketoglutarate in vitro (5), remained unaffected. Four genes were found to be significantly upregulated (q value of 0.39) (19): CT576 lcrH (+2.36-fold), CT288 (+1.80-fold), CT181 (+1.79-fold), and CT814 (+1.69-fold). In contrast to the global transcriptional response seen in the presence of glutamate, the transcriptional response when cells were grown in the presence of α-ketoglutarate as the sole carbon source involved a high number of genes having a low negative fold change (data not shown), suggesting an overall lower chlamydial vitality. This conclusion is consistent with previous findings (4) and the results from the infectivity titration assays, showing a lower number of infectious EBs produced when chlamydia-infected cells were cultured in α-ketoglutarate as the sole carbon source than cells grown in glucose.
To confirm the array results, quantitative reverse transcriptase PCR (qRT-PCR) was used to assess key central metabolism genes as well as CT576 lcrH and CT084, which were upregulated in the microarray assays. CT084 is believed to be involved in pathogenesis with homology to a phospholipase D-like HKD superfamily-secreted protein, and CT576 lcrH is thought to be involved in pathogenesis by serving a role in type III secretion (16). The metabolic genes CT815 mrsA, CT489 glgC, CT798 glgA, and CT866 glgB are involved in glycogen synthesis, while CT042 glgX and CT248 glgP are glycogen-degrading enzymes. The key carbohydrate transporters analyzed by qRT-PCR include the following: CT544 uhpC, a glucose phosphate transporter; CT401 gltT, a glutamate transporter; CT204 sodTi, a dicarboxylate translocator which takes up oxaloacetate or α-ketoglutarate in return for malate; and two genes that have homology to glutamate transporters, CT216 xasA and CT230. CT290 ptsN has been proposed to encode a potential regulator of the glycolytic/gluconeogenic flux. We found a high correlation coefficient (0.93) between the qRT-PCR results and the microarray data. For example, both CT576 lcrH and CT230 were slightly upregulated in the presence of glutamate or α-ketoglutarate as the sole carbon source (Table 2). There was little to no change in gene transcription of the other genes assayed as measured by qRT-PCR, regardless of the type of carbon source available (Table 2). In summary, global gene expression in C. trachomatis was largely unaffected by changes in the availability of the carbon sources tested. These results are consistent with the report by Iliffe-Lee et al. (4), which demonstrated that six genes key to central metabolism were unaffected by carbon source availability.
TABLE 2.
qRT-PCR analysis of total RNA extracted from C. trachomatis L2-infected L929 cellsa
ORF no. and gene nameb | Change in expression (fold change)c
|
|
---|---|---|
Glutamate | α-Ketoglutarate | |
CT042 glgX | 1.40 ± 0.13 | −1.06 ± 0.04 |
CT084 | 2.13 ± 0.23 | 1.31 ± 0.14 |
CT129 glnP | 1.22 ± 0.17 | −1.63 ± 0.08 |
CT204 sodTi | 1.76 ± 0.15 | −1.46 ± 0.05 |
CT216 xasA | 1.97 ± 0.13 | −2.28 ± 0.06 |
CT230 | 2.00 ± 0.09 | 1.85 ± 0.04 |
CT290 ptsN | 1.89 ± 0.33 | −1.61 ± 0.11 |
CT401 gltT | 1.59 ± 0.17 | −1.34 ± 0.10 |
CT544 uhpC | 1.19 ± 0.08 | −1.55 ± 0.03 |
CT576 lcrH | 2.06 ± 0.29 | 2.18 ± 0.28 |
CT613 folP | 1.87 ± 0.24 | −2.69 ± 0.05 |
CT798 glgA | 1.73 ± 0.10 | −1.67 ± 0.04 |
At least three independent infections in which total RNA was harvested from infected cells from each specific carbon source were performed. Total RNA (1 μg) was used to generate cDNA using random hexamers. qRT-PCR was performed in an ABI 7700 sequence detection system using SYBR green master mix. Gene expression data were normalized to 16S rRNA.
Open reading frame (ORF) number and gene names are based on the naming system of Stephens et al. (16).
Change in expression of cells grown with glutamate or α-ketoglutarate as the sole carbon source compared to expression in cells grown with glucose as the sole carbon source. Change in expression is shown as the mean fold change ± standard deviation.
Chlamydiae, like other obligate pathogenic bacteria living within an eukaryotic host (7), were transcriptionally unresponsive or inflexible to nutrient-based environmental changes, such as carbon source availability. This is in striking contrast to the expression profiles for free-living bacteria, where the growth of E. coli in media rich in glucose compared to growth in media containing only gluconeogenic substrates leads to a massive switch in global gene expression affecting more than 700 genes (9, 10). The eukaryotic host contains a dynamic assortment of energy sources, such as ATP, amino acids, and various carbon sources. As chlamydiae bacteria grow and develop within their eukaryotic host, these energy sources are consumed and are ultimately depleted. An attractive hypothesis is that depletion of such energy stores could serve as the molecular switch behind the morphological changes that occur during the developmental cycle; however, carbon source depletion does not appear to serve as a signal for developmental regulation of chlamydiae.
Acknowledgments
We thank E. Banta for technical assistance and C. Lammel for critical comments during the preparation of the manuscript.
This work was supported in part by National Institutes of Health grants AI042156 and AI032943. T. L. Nicholson is the recipient of National Research Service Award AI050361.
Editor: A. D. O'Brien
REFERENCES
- 1.Bruckner, R., and F. Titgemeyer. 2002. Carbon catabolite repression in bacteria: choice of the carbon source and autoregulatory limitation of sugar utilization. FEMS Microbiol. Lett. 209:141-148. [DOI] [PubMed] [Google Scholar]
- 2.Comanducci, M., S. Ricci, and G. Ratti. 1988. The structure of a plasmid of Chlamydia trachomatis believed to be required for growth within mammalian cells. Mol. Microbiol. 2:531-538. [DOI] [PubMed] [Google Scholar]
- 3.Hatch, T. P. 1999. Developmental biology, p. 29-67. In R. S. Stephens (ed.), Chlamydia: intracellular biology, pathogenesis, and immunity. American Society for Microbiology, Washington, D.C.
- 4.Iliffe-Lee, E. R., and G. McClarty. 2000. Regulation of carbon metabolism in Chlamydia trachomatis. Mol. Microbiol. 38:20-30. [DOI] [PubMed] [Google Scholar]
- 5.Kubo, A., and R. S. Stephens. 2001. Substrate-specific diffusion of select dicarboxylates through Chlamydia trachomatis PorB. Microbiology 147:3135-3140. [DOI] [PubMed] [Google Scholar]
- 6.Matsumoto, A., H. Izutsu, N. Miyashita, and M. Ohuchi. 1998. Plaque formation by and plaque cloning of Chlamydia trachomatis biovar trachoma. J. Clin. Microbiol. 36:3013-3019. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Moran, N. A. 2002. Microbial minimalism: genome reduction in bacterial pathogens. Cell 108:583-586. [DOI] [PubMed] [Google Scholar]
- 8.Nicholson, T. L., L. Olinger, K. Chong, G. Schoolnik, and R. S. Stephens. 2003. Global stage-specific gene regulation during the developmental cycle of Chlamydia trachomatis. J. Bacteriol. 185:3179-3189. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Oh, M. K., and J. C. Liao. 2000. Gene expression profiling by DNA microarrays and metabolic fluxes in Escherichia coli. Biotechnol. Prog. 16:278-286. [DOI] [PubMed] [Google Scholar]
- 10.Oh, M. K., L. Rohlin, K. C. Kao, and J. C. Liao. 2002. Global expression profiling of acetate-grown Escherichia coli. J. Biol. Chem. 277:13175-13183. [DOI] [PubMed] [Google Scholar]
- 11.Palmer, L., and S. Falkow. 1986. A common plasmid of Chlamydia trachomatis. Plasmid 16:52-62. [DOI] [PubMed] [Google Scholar]
- 12.Peng, L., and K. Shimizu. 2003. Global metabolic regulation analysis for Escherichia coli K12 based on protein expression by 2-dimensional electrophoresis and enzyme activity measurement. Appl. Microbiol. Biotechnol. 61:163-178. [DOI] [PubMed] [Google Scholar]
- 13.Scidmore, M. A., D. D. Rockey, E. R. Fischer, R. A. Heinzen, and T. Hackstadt. 1996. Vesicular interactions of the Chlamydia trachomatis inclusion are determined by chlamydial early protein synthesis rather than route of entry. Infect. Immun. 64:5366-5372. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Sriprakash, K. S., and E. S. Macavoy. 1987. Characterization and sequence of a plasmid from the trachoma biovar of Chlamydia trachomatis. Plasmid 18:205-214. [DOI] [PubMed] [Google Scholar]
- 15.Stanley, N. R., R. A. Britton, A. D. Grossman, and B. A. Lazazzera. 2003. Identification of catabolite repression as a physiological regulator of biofilm formation by Bacillus subtilis by use of DNA microarrays. J. Bacteriol. 185:1951-1957. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Stephens, R. S., S. Kalman, C. Lammel, J. Fan, R. Marathe, L. Aravind, W. Mitchell, L. Olinger, R. L. Tatusov, Q. Zhao, E. V. Koonin, and R. W. Davis. 1998. Genome sequence of an obligate intracellular pathogen of humans: Chlamydia trachomatis. Science 282:754-759. [DOI] [PubMed] [Google Scholar]
- 17.Stulke, J., and W. Hillen. 1999. Carbon catabolite repression in bacteria. Curr. Opin. Microbiol. 2:195-201. [DOI] [PubMed] [Google Scholar]
- 18.Titgemeyer, F., and W. Hillen. 2002. Global control of sugar metabolism: a gram-positive solution. Antonie Leeuwenhoek 82:59-71. [PubMed] [Google Scholar]
- 19.Tusher, V. G., R. Tibshirani, and G. Chu. 2001. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. USA 98:5116-5121. [DOI] [PMC free article] [PubMed] [Google Scholar]