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Proc Natl Acad Sci U S A. Feb 14, 2006; 103(7): 2374–2379.
Published online Feb 7, 2006. doi:  10.1073/pnas.0510995103
PMCID: PMC1413745
Microbiology

Guanosine 3′,5′-bispyrophosphate coordinates global gene expression during glucose-lactose diauxie in Escherichia coli

Abstract

Guanosine 3′,5′-bispyrophosphate (ppGpp), also known as “magic spot,” has been shown to bind prokaryotic RNA polymerase to down-regulate ribosome production and increase transcription of amino acid biosynthesis genes during the stringent response to amino acid starvation. Because many environmental growth perturbations cause ppGpp to accumulate, we hypothesize ppGpp to have an overarching role in regulating the genetic program that coordinates transitions between logarithmic growth (feast) and growth arrest (famine). We used the classic glucose-lactose diauxie as an experimental system to investigate the temporal changes in transcription that accompany growth arrest and recovery in wild-type Escherichia coli and in mutants that lack RelA (ppGpp synthetase) and other global regulators, i.e., RpoS and Crp. In particular, diauxie was delayed in the relA mutant and was accompanied by a 15% decrease in the number of carbon sources used and a 3-fold overall decrease in the induction of RpoS and Crp regulon genes. Thus the data significantly expand the previously known role of ppGpp and support a model wherein the ppGpp-dependent redistribution of RNA polymerase across the genome is the driving force behind control of the stringent response, general stress response, and starvation-induced carbon scavenging. Our conceptual model of diauxie describes these global control circuits as dynamic, interconnected, and dependent upon ppGpp for the efficient temporal coordination of gene expression that programs the cell for transitions between feast and famine.

Keywords: catabolite repression, stringent response, conceptual model

The fitness of free-living organisms depends on their ability to withstand environmental insults and grow as rapidly as possible when conditions allow. Consequently, the coordination of growth control processes constitutes a fundamental level of regulation in prokaryotes. For this reason, the bacterial existence is often thought to be one of “feast and famine” (1). In the laboratory, nutritional conditions that cause biphasic growth provide a unique opportunity to investigate this most basic of bacterial behaviors. When cultured on a mixture of glucose and lactose, Escherichia coli grows preferentially on glucose until the glucose is exhausted, resulting in growth arrest while the cells adjust to growth on lactose, i.e., diauxie. The genetic basis for biphasic sugar catabolism, elucidated by Jacob and Monod (2), is exemplified by lac operon induction, which is a textbook paradigm for illustrating genetic control. However, transcriptome analysis revealed that diauxie involves much more than induction of the lac operon, and that diauxie is accompanied by a global response to growth arrest that apparently ensures recovery when conditions allow growth to resume (3). The purpose of this study is to dissect the regulatory networks that govern diauxie as a means for understanding how the cell integrates the response to growth arrest.

We showed previously (3) that during steady-state logarithmic growth, gene expression in E. coli is quasi-steady state. In contrast, when glucose is exhausted and growth of the culture is arrested, a major component of the transcriptome’s adjustment to diauxie is the stringent response, which includes down-regulation of a large number of transcription and translation apparatus genes, inhibition of ribosome synthesis, and induction of amino acid biosynthesis genes (4,5). Also induced are general stress response and carbon scavenging genes, which apparently ensure survival during growth arrest and switching to alternative carbon sources. These genes are controlled primarily by the stationary phase sigma factor, RpoS (6,7), and the cAMP receptor protein (Crp), which governs catabolite repression, a response to sugar limitation (8,9). There is strong evidence for a connection between stringent control and the general stress response: guanosine 3′,5′-bispyrophosphate (ppGpp) is required for RpoS accumulation (10) and ppGpp-bound RNA polymerase (RNAP) preferentially binds alternative sigma factors (1113). Likewise, a connection between carbon scavenging and the general stress response is manifested as an RpoS-dependent tradeoff between induction of genes in the RpoS and Crp regulons (8). These published studies are indicative of a larger emerging theme in global gene regulation in prokaryotes: large-scale regulatory circuits do not function independently of one another but instead are finely calibrated to coordinate bacterial cell functions in response to environmental cues.

On the basis of the behavior of the transcriptome during diauxie and the dependence of the general stress response on ppGpp, we (3) and others (12) hypothesized that ppGpp controls not only the stringent response, but also the regulatory networks that coordinate survival during stationary phase and resumption of growth after growth arrest. To dissect the roles of individual regulators of this process, we now compare the transcription profiles of mutants lacking RelA, RpoS, and Crp across the diauxic time course. We show here that efficient induction of all genes that are significantly induced during diauxie, including primarily the Crp and RpoS regulons, is RelA-dependent, implying that ppGpp is at the apex of global regulation during times of carbon starvation. We incorporate these results into a conceptual model of glucose-lactose diauxie that places at the center of growth transitions the ppGpp-mediated balance between stringent-controlled repression of the translation apparatus and induction the general stress response and carbon scavenging regulons.

Results

Systematic Regulatory Mutant Analysis During Diauxie.

K-means cluster analysis of the transcriptome data set of wild-type E. coli during glucose-lactose diauxie (3) revealed three regulatory networks (RpoS, Crp, and RelA) that dominated the transcription profile (Fig. 5, which is published as supporting information on the PNAS web site). To further elucidate their role in diauxie, we cultured rpoS, crp, and relA mutants on minimal medium containing a mixture of glucose and lactose as sole carbon sources. Total RNA was isolated during logarithmic growth in the glucose phase of diauxie and at ≈10-min intervals during diauxie, and was analyzed in triplicate by using whole-genome E. coli MG1655 oligonucleotide glass microarrays. The RNA control for all microarrays was from an early logarithmic phase culture of wild-type E. coli MG1655 on minimal glucose medium. The data sets are available from the Oklahoma University Bioinformatics Core Facility web site (www.ou.edu/microarray). We consider the transcriptome analysis of these regulatory mutants, as follows.

Transcriptome of rpoS and crp Mutants.

Under diauxie conditions, the strain lacking RpoS grew normally, as shown in ref. 6. The strain lacking Crp was unable to resume growth on lactose, as expected (14). Fig. 1 shows a transcriptome comparison for the mutant strains during the diauxic lag period with that of the wild-type strain. In the wild type, exhaustion of glucose was accompanied by diauxie and a whole-genome expression profile characteristic of release from catabolite repression and induction of the general stress response; the Crp regulon was induced in the 10-min interval immediately preceding diauxie, whereas induction of the RpoS regulon occurred within the first 10 min after growth arrest. These results are consistent with the known diauxie-dependent kinetics of RpoS protein accumulation, which is slow (6), and cAMP accumulation, which is rapid (14) (Fig. 1).

Fig. 1.
Transcriptome analysis of the diauxic lag in E. coli MG1655 wild-type, rpoS, and crp strains. (a) Log2 ratio plot of time series microarray data. K-means cluster analysis (K = 22) of the data set revealed four clusters containing 97 significantly regulated ...

To identify genes regulated by RpoS and/or Crp during diauxie, we used K-means cluster analysis (K = 22) of the entire data set shown in Fig. 1a. This analysis revealed four clusters containing 97 highly regulated genes, which are shown in Fig. 1b. The constituent genes of the Crp or RpoS regulons were not induced in the respective mutants (Fig. 1b). Fourteen genes were not induced in the rpoS strain during the diauxic lag, including genes that are typically associated with the general stress response and known to be induced in an RpoS-dependent manner, such as bolA, dps, wrbA, and mscL (15). Thirty-one genes were not induced in the crp strain when glucose was exhausted during diauxie, including genes such as lacZA, mglBA, lamB, glpFK, and rbsD that are known to depend on Crp for their expression (16). Finally, another 14 genes were not induced in either the rpoS or crp strains during diauxie, including glgS, which is known to be regulated by both RpoS and Crp (17). The analysis also revealed repression of 37 genes, shown in Fig. 1b, that are known to be associated with the stringent response (3). Repression of these genes was not affected to a large extent by mutation of rpoS or crp. Taken together, these results are consistent with diauxie in the wild type being accompanied by the stringent response and induction, simultaneously, of the RpoS-dependent general stress response and Crp-dependent scavenging for alternative carbon sources. The remaining genes that were not regulated to a large extent during diauxie are shown in gray in Fig. 1a.

Transcriptome of relA Mutant.

Because accumulation of RpoS and transcription of RpoS-dependent genes requires ppGpp (10), we wanted to determine whether a defect in ppGpp synthesis would affect expression of RpoS-dependent genes under diauxic conditions. There are two ppGpp synthetase enzymes to consider. The dogma is that ribosome-associated RelA synthesizes ppGpp in response to amino acid starvation, and that SpoT, which has both weak synthetase and strong ppGpp hydrolase activities, is responsible for ppGpp accumulation in response to carbon starvation (5, 18). Upon growth arrest, regardless of whether it is synthesized by RelA or SpoT, ppGpp accumulates rapidly in the cell, binds to RNAP, and stimulates the down-regulation of the translation apparatus that characterizes the stringent response (19). The rapid accumulation of high levels of ppGpp during glucose-lactose diauxie was reported previously (20).

Ideally, this experiment should compare diauxie in the wild type with that of a strain that is completely devoid of ppGpp. However, spoT mutants are only viable in a relA background, and relA spoT strains are multiply auxotrophic for nine amino acids (21). Thus, we tested the wild-type and relA spoT strains with amino acids added to the growth medium and found they did not exhibit glucose-lactose diauxie (data not shown). Presumably, this is because the amino acids served as carbon sources to support growth during the period when the lac operon was being induced. Regardless of the cause, it was not possible to culture the relA spoT strain under glucose-lactose diauxie conditions because of the amino acid requirement of this strain. However, the relA strain is able to grow on minimal medium without added amino acids. Therefore, we investigated the impact on the transcriptome of a relA mutation, which has been shown to extend diauxie in E. coli strains (5, 20, 22, 23). We observed large-scale differences in both the timing and extent of differential gene expression in the relA mutant during diauxie (Fig. 2). The down-regulation of the transcription and translation apparatus genes (listed in Fig. 1 and Table 1, which is published as supporting information on the PNAS web site) was delayed, reaching a minimum in the 26- to 36-min interval of diauxie, as opposed to the 0- to 10-min interval for the wild-type strain. Thus, the results are consistent with the known role of ppGpp in stringent control of ribosome synthesis during growth arrest.

Fig. 2.
Transcriptome analysis of the diauxic lag in E. coli MG1655 wild-type and relA strains. Expression of RpoS and Crp regulons (identified as being significantly regulated in Fig. 1) for the wild-type (a and b) and relA (c and d) strains, shown as log2 ratio ...

Altered Induction of RpoS and Crp Regulons in relA Mutant.

All members of the RpoS regulon exhibited delayed induction in the relA mutant, with the exception of one gene (bolA). The amplitude of the “burst” of gene induction normally seen at the onset of diauxie in the wild type was lessened 3-fold in the rpoS strain (compare b and d of Fig. 2). Because relA mutants delay ppGpp accumulation during nutrient downshifts (23, 24) and the RpoS-dependent general stress response requires ppGpp (1012), the data are consistent with a model that places ppGpp in control of the general stress response.

The relA mutant also exhibited diminished induction of the Crp regulon (Fig. 2b), with an average peak expression of Crp-activated genes that was 3-fold lower than that observed in the wild type (Fig. 2d). In the wild-type strain, induction of some Crp-dependent genes was immediate, occurring during the 10-min interval before diauxie, and constituted a first wave of gene induction in response to glucose starvation. Other Crp-dependent genes, including the lac operon, were not induced in the wild type until the onset of diauxie. By contrast, in the relA mutant induction of the lac operon was delayed by 25 min (Table 1). This delay likely is the ultimate cause of the lengthened diauxie of the relA strain. Thus, the relA mutation generally dampened expression of all CRP-dependent genes that normally are induced in the wild type during diauxie (Table 1), as well as rmf (Fig. 6, which is published as supporting information on the PNAS web site), which is known to be outside control of both Crp and RpoS (25). Because relA strains are known to have higher than normal cAMP levels after nutrient downshifts (24) and normal cAMP levels after amino acid starvation (26), these results argue that the RelA-dependent effect on Crp-dependent gene activation is not mediated by the intracellular cAMP concentration. Rather, in vitro transcription assays demonstrated that ppGpp is required for maximal induction of the lac operon, and it has been suggested that this might also be the case for other catabolic genes and operons (27). The results shown in Fig. 2. are consistent with the idea that maximal induction of Crp-activated genes depends on ppGpp.

In further support of the assertion that ppGpp is required for induction of carbon catabolism genes, phenotype arrays (GN2 MicroPlate, Biolog) showed a 15% decrease in the number of carbon sources used by the relA and relA spoT mutants (Fig. 7, which is published as supporting information on the PNAS web site). Specifically, we observed that α-hydroxybutyric acid, α-ketobutyric acid, propionate, d-saccharic acid, lactulose, Tween 40, and Tween 80 were not consumed by these mutants (Table 2, which is published as supporting information on the PNAS web site). These results are consistent with a model wherein RelA-dependent adjustment of intracellular ppGpp levels in response to starvation is required for normal induction of survival genes, including the Crp regulon.

Discussion

E. coli is a comparatively simple model system, yet a full understanding of the regulatory connections that shape prokaryotic physiology remains elusive. The strategy of iteratively examining the roles of several transcription factors in a single, complex physiological transition (i.e., growth arrest) provided a conceptual framework for integrating diverse cellular processes. This general strategy and the data sets generated here should be of value for systems biology.

To derive a conceptual model of diauxie that accounts for the global redistribution of gene expression in response to growth arrest, such as that caused by diauxie, we propose a simple RNAP switch model that is consistent with known biochemical parameters (regulatory mechanisms) of the stringent response (Fig. 3). To our knowledge, all growth perturbations result in rapid accumulation of ppGpp (5, 28), which binds to RNAP and causes the down-regulation of the translation apparatus that characterizes the stringent response (5). Given that stable RNA synthesis constitutes up to ≈80% of transcription in rapidly growing cells (29, 30), reduced transcription from these stringent promoters, which has been proposed to result from various mechanisms, including destabilization of the RNAP-promoter open complex or inactive dead-end promoter complexes (13, 19, 31), should greatly increase the availability of free RNAP (5, 30). The indirect or so-called passive model postulates that the increased availability of RNAP caused by inhibition of rRNA transcription frees RNAP to bind to other promoters, i.e., amino acid biosynthetic genes and those shown in this study to be activated. Alternatively, the direct model of RelA-dependent activation proposes that ppGpp and DksA act directly to stimulate RNAP at promoters of amino acid biosynthesis genes (5, 13). These models are not mutually exclusive and may in fact both contribute to the observed changes in gene expression caused by ppGpp. Because gene expression profiles reflect the distribution of RNAP at promoters across the genome (32), our data suggest that reprogramming of RNAP by binding ppGpp increases the transcription initiation frequency at many more promoters than had been recognized previously, including the CRP and RpoS regulons. Also, it was recently shown that growth arrest is accompanied by RelA-dependent, physical redistribution of RNAP (29). Thus, regardless of whether the ppGpp effect is direct or indirect, gene expression profiling of E. coli during diauxie supports the proposed switch model (Fig. 3), wherein ppGpp not only controls the down-regulation of stringent promoters, but also the activation of stress survival and carbon catabolism genes. We propose that ppGpp-dependent reprogramming of RNAP is the driving force behind differential gene expression during diauxie.

Fig. 3.
RNAP switch model in E. coli (wild type). Cells programmed for growth (Left) have low intracellular levels of ppGpp and 80% of active RNAP is engaged in stable RNA synthesis, resulting in balanced growth; RNAP availability, cAMP, and RpoS levels are low, ...

Our conceptual model of diauxie, based on the microarray data presented above and incorporating the RNAP switch model (Fig. 3), is given in Fig. 4. Diauxie involves much more than induction of the lac operon. Before the lac operon is induced, the general stress and stringent responses are induced and catabolite repression is released. Through the use of regulatory mutants, we show that the large-scale changes in the transcriptome during diauxie, including induction of the Crp and RpoS regulons and adaptation to metabolism of a less-preferred carbon source, requires RelA for efficient and timely control. By accounting for the ppGpp-dependent system that controls ribosome number in bacteria (5, 12, 3336), our model incorporates one of the fundamental principles of bacterial physiology, i.e., that bacterial cell growth rate is determined by the number of ribosomes active in protein synthesis (33). Also, the model is consistent with the recently discovered roles of DksA in mediating physical interactions of ppGpp with RNAP (13) and ppGpp-dependent RpoS accumulation (37).

Fig. 4.
Conceptual model of glucose-lactose diauxie in wild-type E. coli MG1655. During the −10- to 0-min interval, a cascade of responses stimulated by glucose exhaustion results in rapid accumulation of ppGpp, which binds to and reprograms RNAP, culminating ...

Our data bring to light a controversy regarding the roles of the two ppGpp synthetase enzymes in regulating the stringent response. The prevailing notion is that RelA responds to amino acid starvation, whereas SpoT governs ppGpp accumulation during carbon starvation (5). The data presented here indicate that starvation for glucose in the wild type induces the stringent response. In addition, our results indicate that mutation of relA alters this response. However, our experiments do not distinguish whether this effect is mediated directly by RelA or indirectly, i.e., glucose starvation leads to amino acid starvation. We also note that the relA strain exhibits prolonged diauxie (Fig. 2) and diauxie is abolished in both the wild-type and mutant strains when amino acids are present (data not shown). The possibility that RelA, along with SpoT, can sense carbon starvation independently of amino acid pool fluctuations seems unlikely given the strong evidence for physical association of RelA with the ribosome, which allows it to monitor translational pausing and hence the amino acyl-tRNA pool (38). Thus, the model shown in Fig. 4 attributes ppGpp accumulation to amino acid starvation brought about indirectly from exhaustion of glucose, rather than directly by carbon starvation. Direct measurement of the amino acid pool should provide insight into the physiological state caused by carbon starvation under diauxic conditions.

ppGpp is known to affect the overall physiological state of the cell through transcriptional regulation of a large number of promoters. Processes affected include stable RNA synthesis, amino acid biosynthesis, sigma factor competition, and induction of the σs-dependent stress response genes. The lac operon has been shown to require ppGpp for maximal expression (27). The results presented here extend the influence of ppGpp beyond control of lac to the larger Crp regulon and therefore to catabolite repression and carbon catabolism in general. As such, ppGpp signals the nutritional quality of the environment and coordinates adjustments to gene expression across a continuum that ranges from maximum growth and metabolism to complete growth arrest and damage control. Accordingly, our conceptual model (Fig. 4) places ppGpp at the apex of the stimulus-response pathways that allow E. coli to successfully negotiate growth arrest during diauxie. This regulatory network includes ppGpp-dependent control of the general stress response, carbon scavenging, and ribosome synthesis. The benefit of their coordinated regulation during growth transitions is critically important, because the energy that would otherwise have been spent on growth functions (34, 36) is now conserved while the cell diverts its attention to survival in stationary phase until conditions allow growth to resume. Thus, ppGpp controls the feast and famine existence and may therefore profoundly influence the activities of microbes in a host as well as survival between hosts.

Materials and Methods

Strains and Growth Conditions.

E. coli MG1655 and isogenic mutants were cultured in a 2-liter Biostat B fermentor (B. Braun Biotech) containing 1 liter of morpholinepropanesulfonic acid (Mops) minimal medium with 0.5 g/liter glucose and 1.5 g/liter lactose, as described in ref. 3. The temperature was maintained at 37°C, and pH was kept constant at 7.2 by the addition of 2 M NaOH. The dissolved oxygen level was maintained above 20% of saturation by adjusting the agitation speeds in the range of 270–500 rpm with fixed 1 liter/min air flow. Growth was monitored as absorbance at 600 nm. E. coli ΔrelA251::kanR was a gift from M. Cashel (Laboratory of Molecular Genetics, National Institute of Child Health and Human Development, Bethesda) and was constructed as described in ref. 39. The E. coli Δcrp::kanR and ΔrpoS::kanR strains were constructed by allelic replacement (40) of the entire genes. These mutant strains are isogenic with E. coli MG1655.

Microarray Analysis.

Microarray analysis was carried out essentially as described in ref. 32. Total RNA was extracted from cells, diluted (1:1) in ice-cold RNAlater (Ambion), and purified by using RNeasy columns (Qiagen), as described in ref. 3. RNA was labeled by first-strand cDNA synthesis using reverse transcriptase, random primers, and aminoallyl-dUTP incorporation; Cy-3 and Cy-5 dyes were chemically coupled in vitro to the aminoallyl-derivatized cDNA. The oligonucleotide microarrays used in this study were printed on GAPS II slides (Corning) with a probe set containing 70 base oligonucleotide probes for all E. coli MG1655 genes (Operon) by using a Molecular Dynamics Gen III array spotter (Amersham Pharmacia Biosciences). Slides were hydrated and flash-dried, UV-cross-linked, and blocked with succinic anhydride; equal amounts of the Cy-3- and Cy-5-labeled samples were then hybridized in triplicate to microarrays by using a Discovery system and ChipMap reagents (Ventana Medical Systems). For all microarrays, the experimental sample was labeled with Cy-5, and the control, from early logarithmic growth of E. coli MG1655 wild type on minimal glucose medium, was labeled with Cy-3. Hybridized slides were scanned on a GenePix 4000 scanner (Axon Instruments), and the data were collected by using genepix 5.0 software and uploaded to our database for analysis (www.ou.edu/microarray). The data were normalized by a local Lowess algorithm (41) implemented on our database, and the replicate arrays were averaged for analysis. Clustering algorithms were implemented in decisionsite functional genomics software (Spotfire).

Supplementary Material

Supporting Information:

Acknowledgments

We thank Ming Yann (Frank) Wu and Marybeth Langer for technical assistance with microarrays and phenotype assays, and Mike Cashel and Moselio Schaechter for critical reading of the manuscript. This work was supported by National Institutes of Health Grant R01-AI48945-05.

Glossary

Abbreviations:

ppGpp
guanosine 3′,5′-bispyrophosphate
RNAP
RNA polymerase.

Footnotes

Conflict of interest statement: No conflicts declared.

References

1. Koch A. L. Adv. Microb. Physiol. 1971;6:147–217. [PubMed]
2. Jacob F., Monod J. J. Mol. Biol. 1961;3:318–356. [PubMed]
3. Chang D. E., Smalley D. J., Conway T. Mol. Microbiol. 2002;45:289–306. [PubMed]
4. Cashel M., Gallant J. Nature. 1969;221:838–841. [PubMed]
5. Cashel M., Gentry D. R., Hernandez V. J., Vinella D. In: Escherichia coli and Salmonella: Cellular and Molecular Biology. Neidhardt F. C., Curtiss R. III, Ingraham J. L., Lin E. C. C., Low K. B., Magasanik B., Reznikoff W. S., Riley M., Schaechter M., Umbarger H. E., editors. Washington, DC: Am. Soc. Microbiol.; 1996. pp. 1458–1496.
6. Fischer D., Teich A., Neubauer P., Hengge-Aronis R. J. Bacteriol. 1998;180:6203–6206. [PMC free article] [PubMed]
7. Hengge-Aronis R. In: Escherichia coli and Salmonella: Cellular and Molecular Biology. Neidhardt F. C., Curtiss R. III, Ingraham J. L., Lin E. C. C., Low K. B., Magasanik B., Reznikoff W. S., Riley M., Schaechter M., Umbarger H. E., editors. Washington, DC: Am. Soc. Microbiol.; 1996. pp. 1497–1512.
8. King T., Ishihama A., Kori A., Ferenci T. J. Bacteriol. 2004;186:5614–5620. [PMC free article] [PubMed]
9. Saier M. H., Ramseier T. M., Reizer J. In: Escherichia coli and Salmonella: Cellular and Molecular Biology. Neidhardt F. C., Curtiss R. III, Lin E. C. C., Low K. B., Magasanik B., Reznikoff W. S., Riley M., Schaechter M., Umbarger H. E., editors. Washington, DC: Am. Soc. Microbiol.; 1996. pp. 1325–1343.
10. Gentry D. R., Hernandez V. J., Nguyen L. H., Jensen D. B., Cashel M. J. Bacteriol. 1993;175:7982–7989. [PMC free article] [PubMed]
11. Kvint K., Farewell A., Nystrom T. J. Biol. Chem. 2000;275:14795–14798. [PubMed]
12. Nystrom T. Mol. Microbiol. 2004;54:855–862. [PubMed]
13. Paul B. J., Ross W., Gaal T., Gourse R. L. Annu. Rev. Genet. 2004;38:749–770. [PubMed]
14. Pastan I., Adhya S. Bacteriol. Rev. 1976;40:527–551. [PMC free article] [PubMed]
15. Weber H., Polen T., Heuveling J., Wendisch V. F., Hengge R. J. Bacteriol. 2005;187:1591–1603. [PMC free article] [PubMed]
16. Zheng D., Constantinidou C., Hobman J. L., Minchin S. D. Nucleic Acids Res. 2004;32:5874–5893. [PMC free article] [PubMed]
17. Hengge-Aronis R., Lange R., Henneberg N., Fischer D. J. Bacteriol. 1993;175:259–265. [PMC free article] [PubMed]
18. Metzger S., Schreiber G., Aizenman E., Cashel M., Glaser G. J. Biol. Chem. 1989;264:21146–21152. [PubMed]
19. Barker M. M., Gaal T., Josaitis C. A., Gourse R. L. J. Mol. Biol. 2001;305:673–688. [PubMed]
20. Harshman R. B., Yamazaki H. Biochemistry. 1971;10:3980–3982. [PubMed]
21. Sarubbi E., Rudd K. E., Cashel M. Mol. Gen. Genet. 1988;213:214–222. [PubMed]
22. Ishiguro E. E. Can. J. Microbiol. 1979;25:1206–1208. [PubMed]
23. Lazzarini R. A., Cashel M., Gallant J. J. Biol. Chem. 1971;246:4381–4385. [PubMed]
24. Braedt G., Gallant J. J. Bacteriol. 1977;129:564–566. [PMC free article] [PubMed]
25. Izutsu K., Wada A., Wada C. Genes Cells. 2001;6:665–676. [PubMed]
26. Primakoff P. J. Bacteriol. 1981;145:410–416. [PMC free article] [PubMed]
27. Primakoff P., Artz S. W. Proc. Natl. Acad. Sci. USA. 1979;76:1726–1730. [PMC free article] [PubMed]
28. VanBogelen R. A., Kelley P. M., Neidhardt F. C. J. Bacteriol. 1987;169:26–32. [PMC free article] [PubMed]
29. Cabrera J. E., Jin D. J. Mol. Microbiol. 2003;50:1493–1505. [PubMed]
30. Bremer H., Dennis P. P. In: Escherichia coli and Salmonella: Cellular and Molecular Biology. Neidhardt F. C., Curtiss R. III, Ingraham J. L., Lin E. C. C., Low K. B., Magasanik B., Reznikoff W. S., Riley M., Schaechter M., Umbarger H. E., editors. Washington, DC: Am. Soc. Microbiol.; 1996. pp. 1553–1569.
31. Maitra A., Shulgina I., Hernandez V. J. Mol. Cell. 2005;17:817–829. [PubMed]
32. Wei Y., Lee J. M., Richmond C., Blattner F. R., Rafalski J. A., LaRossa R. A. J. Bacteriol. 2001;183:545–556. [PMC free article] [PubMed]
33. Keener J., Nomura M. In: Escherichia coli and Salmonella: Cellular and Molecular Biology. Neidhardt F. C., Curtiss R. III, Lin E. C. C., Low K. B., Magasanik B., Reznikoff W. S., Riley M., Schaechter M., Umbarger H. E., editors. Washington, DC: Am. Soc. Microbiol.; 1996. p. 14171431.
34. Neidhardt F. C., Ingraham J. L., Schaechter M. Physiology of the Bacterial Cell: A Molecular Approach. Sunderland, MA: Sinauer; 1990.
35. Travers A. Basic Life Sci. 1974;3:67–80. [PubMed]
36. Gralla J. D. Mol. Microbiol. 2005;55:973–977. [PubMed]
37. Brown L., Gentry D., Elliott T., Cashel M. J. Bacteriol. 2002;184:4455–4465. [PMC free article] [PubMed]
38. Wendrich T. M., Blaha G., Wilson D. N., Marahiel M. A., Nierhaus K. H. Mol. Cell. 2002;10:779–788. [PubMed]
39. Xiao H., Kalman M., Ikehara K., Zemel S., Glaser G., Cashel M. J. Biol. Chem. 1991;266:5980–5990. [PubMed]
40. Datsenko K. A., Wanner B. L. Proc. Natl. Acad. Sci. USA. 2000;97:6640–6645. [PMC free article] [PubMed]
41. Quackenbush J. Nat. Genet. 2002;32(Suppl.):496–501. [PubMed]

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