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Journal of Bacteriology, June 2004, p. 3516-3524, Vol. 186, No. 11
0021-9193/04/$08.00+0 DOI: 10.1128/JB.186.11.3516-3524.2004
Copyright © 2004, American Society for Microbiology. All Rights Reserved.
Transcriptome Analysis of Crp-Dependent Catabolite Control of Gene Expression in Escherichia coli
Guillermo Gosset,1,2 Zhongge Zhang,1 Samir Nayyar,1 William A. Cuevas,3 and Milton H. Saier Jr.1*
Division of Biological Sciences, University of California at San Diego, La Jolla, California 92093-0116,1
Departamento de Ingenieria Celular y Biocatálisis, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Morelos 62550, México,2
Genencor International, Inc., Palo Alto, California 94304-10133
Received 7 November 2003/
Accepted 22 February 2004

ABSTRACT
We report here the transcriptome analyses of highly expressed
genes that are subject to catabolite repression or activation
mediated by the cyclic AMP receptor protein (Crp). The results
reveal that many operons encoding enzymes of central carbon
metabolic pathways (e.g., Krebs cycle enzymes), as well as transporters
and enzymes that initiate carbon metabolism, are subject to
direct Crp-mediated catabolite repression. By contrast, few
enzyme-encoding genes (direct regulation) but many ribosomal
protein- and tRNA-encoding genes (indirect regulation) are subject
to Crp-dependent glucose activation. Additionally, Crp mediates
strong indirect catabolite repression of many cytoplasmic stress
response proteins, including the major chaperone proteins, five
ATP-dependent protease complexes, and several cold and heat
shock proteins. These results were confirmed by (i) phenotypic
analyses, (ii) real-time PCR studies, (iii) reporter gene fusion
assays, and (iv) previously published reports about representative
genes. The results serve to define and extend our appreciation
of the Crp regulon.

INTRODUCTION
A dominant mechanism by which
Escherichia coli and other related
bacteria sense carbon sufficiency involves cyclic AMP and its
receptor protein, Crp (
15,
35). The mechanisms by which Crp
regulates gene expression in response to variable cytoplasmic
levels of cyclic AMP have been extensively investigated with
primary emphasis on
E. coli and
Salmonella strains (
5,
34,
35,
41). Dozens of operons have been shown to be subject to Crp-mediated
control by using classical approaches (
17). Transcriptome and
proteome approaches have been used to study the control of various
regulons, as well as to facilitate glucose flux analyses (
10,
39). Indeed, the transcriptome approach has allowed investigators
to challenge established paradigms, and the technology has enjoyed
rapid adoption among researchers (
7). However, no report has
focused on genomewide analyses of Crp-mediated catabolite regulation
in
E. coli. In this study, we corrected this deficiency by conducting
combined transcriptome, phenotypic, and bioinformatic analyses
of the Crp-mediated responses of
E. coli to exogenous glucose
availability. We also tabulated comparative data derived from
the classical literature and confirmed representative regulatory
responses by using alternative approaches. We show here that
a variety of stress-related genes, encoding chaperones (
1,
4,
38), ATP-dependent proteases (
9,
11,
20), and certain temperature
shock proteins (
12,
31,
40), are regulated in response to the
presence of Crp, apparently by an indirect mechanism.

MATERIALS AND METHODS
Bacterial strains and growth conditions.
The strains used in this study were BW25113 and an isogenic
crp mutant derivative, LJ3017, constructed as described by Zhang
et al. (
43). Strains were grown at 37°C with agitation at
250 rpm in Luria-Bertani (LB) broth containing 50 mM potassium
phosphate, pH 7.4, and 0.2 mM
L-cysteine with or without 0.4%
glucose. Cells were grown in 25 ml of medium in 250-ml shake
flasks starting at an optical density at 600 nm (OD
600) of 0.05.
Each determination was performed three times, and the results
were averaged (
6).
Cell harvesting and preparation of RNA.
Cells from each triplicate experiment were harvested in the exponential growth phase when cultures reached an OD600 of 0.5. The total contents of each shake flask (25 ml) were poured into a Millipore vacuum filtration apparatus (catalog no. 1004700) with a Millipore 0.8-µm-pore-size filter (catalog no. AAWPO4700). The collected cells with filter were immediately transferred to a 200-ml glass beaker containing liquid nitrogen. RNA was extracted from each sample as described by Caldwell et al. (6).
Target preparation.
RNA harvested from a given E. coli strain and at a given time point was reverse transcribed into biotin-labeled cDNA by the method of de Saizieu et al. (8). Total RNA (18 µg) was incubated at 37°C overnight in an 80-µl reaction mixture consisting of 1x GIBCO first-strand buffer (50 mM Tris-HCl [pH 8.3], 75 mM KCl, 3 mM MgCl2); 10 mM dithiothreitol; 40 µM random hexamer; 0.3 mM concentrations (each) of dCTP, dGTP, and dTTP; 0.12 mM dATP; 0.3 mM biotin-dATP (NEN catalog no. NEL999); and 1,800 U of SuperScript II reverse transcriptase. To remove RNA, the reaction mixture was brought to 0.25 M NaOH and incubated at 65°C for 30 min. The reaction mixture was neutralized with HCl, and the nucleic acid was precipitated at 20°C in ethanol with 2.5 M ammonium acetate and 20 µg of glycogen. The pellet was washed, air dried, resuspended in water, and quantitated by UV spectroscopy. The yield was approximately 10 to 12 µg of biotin-labeled cDNA. This cDNA (10 µg) was fragmented in 33 µl of 1x One-Phor-All buffer (Amersham-Pharmacia no. 27-0901-02) with 3.75 mU of DNase I at 37°C for 10 min. After the DNase had been heat killed, fragmentation was validated by running 1.5 µg of the fragmented cDNA on a 1.2% agarose gel. Biotin-containing cDNA routinely ranged in size from 25 to 400 nucleotides. The remaining 8 µg of cDNA was hybridized to an Affymetrix (Santa Clara, Calif.) E. coli GeneChip array.
Array description.
The Affymetrix E. coli array has been described in detail by Selinger et al. (37). Briefly, each microarray contains 295,936 25-mer oligonucleotide probes. Half of the probes are a perfect match to the corresponding E. coli chromosomal sequences, while the other half have a single mismatch at the 13th base position. The array includes 4,327 genes and intergenic regions (2). The complete set of averaged data is provided at our website (www-biology.ucsd.edu/
msaier/supmat; see Table S1).
Hybridization, scanning, and data collection.
The hybridization, scanning, and data collection procedures described by Caldwell et al. (6) were followed.
RT-PCR studies.
Bacterial cells were cultured in LB broth with or without 0.4% glucose exactly as for the microarray analyses. When the OD600 reached 0.5, cells were collected by centrifugation at 4°C. Total RNA was subsequently isolated with the RNeasy Mini Kit (Qiagen) in accordance with the manufacturer's protocol. Residual DNA present in the RNA preparations was removed by RNase-free DNase (Stratagene). cDNAs were synthesized with the superscript first-strand synthesis kit (Invitrogen) in accordance with the manufacturer's instructions and stored at 20°C prior to use. Real-time PCR (RT-PCR) was carried out on the LightCycler instrument (Roche Diagnosis Corporation) in accordance with the manufacturer's recommended protocol. Primers used for the RT-PCR were as follows: for aceE, GAA GAA GGT GTT GAG CGT GC and TTG CGG AAGA CTG GAA GGAC; for clpB, CGAC ATC CTG AAA GCA GCAG and CAG ACC TTC AAC GAT GGCAG; for ibpA (hslT), CG CTT TAC CGT TCT GCT ATT GG and TGC GTT CAA AGT TGC GTT CAG.
Data analysis.
Array images were analyzed with Affymetrix Microarray Suite 5.0 software to determine raw expression intensity values, which then were scaled and normalized to a target value of 1,000. These data were imported into a Microsoft Excel spreadsheet for further analysis. The intensity values in the data sets ranged from about 3 x 101 to 8 x 104. Three data sets were obtained for each of the following experimental conditions: wild type in LB, wild type in LB plus glucose, crp mutant in LB, and crp mutant in LB plus glucose. The data sets for each strain and condition were compared pairwise to determine the Pearson correlation coefficient with the formula r = [n(
XY) (
X)(
Y)/{[n
X2 (
X)2][n
Y2 (
Y)2]}1/2. For each triplicate data set, the two sets with the highest Pearson correlation coefficient were retained for further analysis. The statistical significance of fold changes between data sets of different strains and conditions was determined as described by Caldwell et al. (6) and also in Results. Briefly, for each pair of replicate data sets, a table was generated from the average of the logs of the two replicate signals (ALS) and their log ratio (LR). From this table, with a series of sliding windows with a size of 201 ALS values, the mean ALS values and the standard deviation values of the corresponding LR were calculated (SDLR).

RESULTS
Glucose-activated Crp-dependent genes. Table
1 summarizes the
genes that gave (i) a statistically significant change (the
fold change was at least 4.37 times higher than the SD
LR for
the ALS of the gene on the wild-type and
crp strains), (ii)
consistently large signals (signal intensity,

1,000),
(iii) a
crp/wild-type ratio of

2.0 following growth
in LB broth, and (iv) activation by glucose in a Crp-dependent
fashion. Very few enzyme-encoding genes met these criteria,
although several genes encoding ribosomal proteins and tRNAs
did. Enzyme-encoding genes showing Crp-mediated glucose activation
were the
aceE gene, encoding the pyruvate dehydrogenase E1 component;
the
guaB gene, encoding inosine 5' monophosphate dehydrogenase;
and the
ptsG gene, encoding the glucose-specific IIBC permease
of the phosphotransferase system (PTS). These genes may be subject
to repression by the cyclic AMP-Crp complex, although
ptsG is
subject to glucose activation by a distinct Mlc-dependent mechanism
(
16,
28,
29). A few other enzyme-encoding genes exhibited Crp-mediated
glucose activation but did not satisfy all of the criteria cited
above (see Table S1 at our website). These genes included, for
example,
aceF, encoding the pyruvate dehydrogenase E2 component;
accC, encoding acetyl coenzyme A (CoA) carboxylase;
ack, encoding
acetate kinase; and
adk, encoding adenylate kinase. It is surprising
that so few highly expressed enzyme-encoding genes are directly
activated by the cyclic AMP-Crp complex compared to enzyme-encoding
genes that are subject to repression (see below).
Two other genes,
fis, encoding the Fis (factor for inversion
stimulation) protein, a basic, histone-like, chromatin-binding
protein, and the
spf gene, encoding an antisense regulator of
galK translation, were subject to strong Crp-dependent glucose
activation. Crp-binding sites could be found in or near most
of the enzyme-encoding and
fis operons. On this basis, we assume
that these genes are directly regulated by Crp.
Many genes concerned with transcription and translation proved to be subject to Crp-dependent glucose activation. These genes encode tRNAs, ribosomal proteins, elongation factors, and RNA polymerase subunits (see Table S1 at our E. coli transcriptome website). Those satisfying the criteria cited above are listed in Table 1. We could not find Crp-binding sites in or near these genes or operons, suggesting that they are indirectly regulated, possibly in response to changes in growth rate due to the presence or absence of Crp and/or glucose availability (14). This suggestion is in agreement with previous reports on select ribosomal protein and tRNA-encoding genes (14).
Glucose-repressed Crp-dependent genes.
Table 2 tabulates genes subject to Crp-dependent glucose repression identified by the same criteria cited above for Table 1, except that the crp/wild-type strain signal ratio had to be
0.5. The majority of these genes encode enzymes and transporters involved in carbon catabolism. These proteins include (i) enzymes of central carbon metabolism (e.g., the Krebs cycle), (ii) transporters and enzymes that initiate the metabolism of an exogenous carbon compound (e.g., galactitol), and (iii) transporters and enzymes that initiate the metabolism of an exogenous amino acid or peptide that can be used as a primary source of both carbon and nitrogen (e.g., aspartate). The second largest class encodes stress response proteins primarily concerned with protein folding and degradation. Thus, 51 of the 72 genes listed in Table 2 are carbon catabolic genes while 18 of these genes are stress response genes. Only three genes fell into the "miscellaneous" category. For all of these genes, the crp mutation eliminated or decreased the magnitude of catabolite repression. Residual glucose repression in the crp mutant strain is presumably due to effects of other transcription factors.
The prominent carbon catabolic genes include several genes encoding
enzymes in central pathways of carbon catabolism. These enzymes
include (i) the glyoxalate shunt enzymes (AceA and AceB); (ii)
lactaldehyde dehydrogenase A (AldA); (iii) most of the Krebs
cycle enzymes, including fumarase (FumA), citrate synthase (GltA),
malate dehydrogenase (Mdh), succinate dehydrogenase (Sdh), 2-oxoglutarate
dehydrogenase (SucAB), and succinyl-CoA synthetase (SucCD);
and (iv) the central gluconeogenic enzyme phosphoenolpyruvate
carboxykinase (PckA). However, nearly half of the genes exhibiting
Crp-dependent catabolite repression are concerned with the initiation
of carbon metabolism. These genes include the enzymes catalyzing
catabolism of formate (Fdo), galactitol (Gat), glycerol and

-glycerophosphate (Glp) lactate (Lct and Lld), maltose (Mal),
mannose (Man), galactose (Mgl), glucitol (or sorbitol) (Srl),
and trehalose (Tre). Four of these carbohydrates, galactitol,
mannose, glucitol, and trehalose, are substrates of the
E. coli PTS, while six of these compounds, formate, glycerol,

-glycerol
phosphate, lactate, maltose, and galactose, are non-PTS carbon
and energy sources. Many of these genes (i.e.,
gat,
glp,
mgl,
lld,
mal, and
tre) showed residual glucose repression in the
crp mutant. In several of these cases, the
fruR mutation by
itself diminished the repressive effect of glucose while the
crp fruR double mutant exhibited full resistance to glucose
repression. The genes that most clearly showed dual control
of catabolite repression by Crp and Cra were
gat,
mgl,
tre,
and
dsd (data not shown). These observations suggest that in
several cases, catabolite repression depends on both Crp and
Cra (
32,
33). The fact that many genes show residual glucose
repression in the
crp mutant is not surprising since multiple
mechanisms of catabolite repression are recognized in
E. coli (
35).
Crp also regulates genes encoding enzymes, permeases, and regulators that initiate the catabolism of amino acids as both carbon and nitrogen sources. Representative genes encode (i) aspartase (AspA), (ii) D-serine dehydratase (DsdA), (iii) the primary proline uptake permease (PutP), (iv) tryptophanase (TnaA), and (v) the tna operon leader peptide (TnaL). One gene encoding an oligopeptidase (PrlC) is also listed. Many additional carbon metabolic genes not meeting our rigorous criteria for inclusion in Table 2 were similarly found to be subject to Crp-dependent catabolite repression, as expected on the basis of published results, as well as our phenotypic data (see below; see also Table S1 at our website).
Several additional interesting enzyme- or transporter-encoding genes proved to be subject to strong glucose repression mediated at least partially by Crp (see our website). For example, the nmpC and tsx outer membrane porin-encoding genes fell into this category. Both exhibited only partial relief from catabolite repression in the crp mutant, but a crp-fruR double mutant (32, 33) showed no glucose repression (unpublished results). The same behavior was observed for the dadA and dadX genes encoding D-amino acid dehydrogenase and alanine racemase, respectively, as well as the genes encoding the dicarboxylate transporters DctA and DcuA. Crp-binding sites could be identified in the control regions of some of these genes but not others. It therefore appears that Crp and the fruR gene product, Cra, cooperate in the regulation of many operons in E. coli.
Phenotypic analyses.
We conducted phenotypic analyses, measuring the oxidation of various carbon and nitrogen sources with 96-well Biolog microtiter plates (3). For carbon sources of interest that were not included in the Biolog plates, or where the responses were ambiguous, we conducted studies of growth on minimal agar plates containing the carbon source at 0.2%. Table 3 summarizes some of the results. For a more complete compilation of the phenotypic data for carbon and nitrogen sources found to be under Crp control, see our website (Table S2).
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TABLE 3. Oxidation or utilization of various carbon sources by isogenic wild-type and crp mutant strains of E. coli
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Almost all of the carbon sources tested (e.g., acetate, fumarate,
D- and
L-malate, succinate,

-ketoglutarate, lactate, glycerol,
D,
L-

-glycerol phosphate, maltose, maltotriose, and glucitol)
gave positive wild-type responses but negative
crp mutant responses.
Of the potential nitrogen sources,
L-aspartate, several peptides,
and proline also gave positive wild-type responses but negative
crp mutant responses.
D-Serine showed a decreased but appreciable
response in the mutant relative to that in the wild type. Oxidation
of galactitol, galactose, mannose, trehalose, and tryptophan
did not show differences between the wild type and the
crp mutant
on the Biolog plates, but when these compounds were assayed
for growth on minimal plates, the wild type, but not the
crp mutant, proved to use galactitol, trehalose, and tryptophan
while both strains utilized galactose and mannose, with the
mutant growing less well than the wild type. Thus, the transcriptome
data are in agreement with the phenotypic data in all of the
cases tested.
Correlation between transcriptome and classical approaches.
Table 4 summarizes the results of our transcriptome analyses and compares them with corresponding published data obtained by classical approaches. In most cases, the fold effects of glucose on gene expression in a wild-type background were examined. In every such case, the direction of the effect (repression or activation) was the same. Similar agreement has been reported for transcriptome analyses versus traditional methods conducted with Bacillus subtilis (21). Quantitative differences may be within the range of experimental error considering that in all cases different strains were used and that in several cases different media were used. The different genetic backgrounds could have contributed to the observed differences. However, for all but one of the glucose-repressed genes, the fold repression by glucose was greater when assayed by microarray technology than when lacZ reporter gene fusions were used. The lacZ fusion technology may introduce artifacts affecting gene expression, as discussed previously (27).
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TABLE 4. Correlation between the transcriptome results of this study and gene expression analyses reported in the literature
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Crp-mediated control of stress response genes.
The stress response proteins whose synthesis proved to be under
Crp control include the major chaperone proteins DnaJK, GrpE,
and GroEL/ES (MopAB) (Table
2). Five cytoplasmic ATP-dependent
proteases or protease complexes (ClpPBX, HflBKX, HslUV, HtpX,
and Lon) also proved to be subject to strong Crp-mediated catabolite
repression. In the case of the
hfl genes, the
crp mutation reversed
glucose repression (Table
2) while the
fruR mutation abolished
it (data not shown). Finally, three biochemically ill-defined
heat shock genes (
hstS,
hstT, and
htpG) are listed in Table
2. We also identified putative stationary-phase- or carbon starvation-inducible
genes (e.g.,
cspD, and
yjiY) that are subject to strong Crp-dependent
glucose repression (
42). Crp-binding sites could be found upstream
of both the
cspD and
yjiY genes. It is therefore clear that
many stress response genes in
E. coli are subject to Crp-dependent
catabolite repression, in agreement with the fact that
crp mutants
are sensitive to certain types of stress and starvation conditions
(
18,
19).
From a mechanistic standpoint, it is important to note that we were not able to find Crp-binding sites in the control regions of most of these stress genes and operons. The regulatory consequences of inclusion of glucose in the medium and of the loss of the Crp protein are therefore presumed to be indirect in most cases. Sigma-32 is known to be under Crp control, but its contribution to the rates of gene transcription should be minimal under the conditions used in our studies (19). Of the three miscellaneous genes (Table 2), one is a purine nucleoside phosphorylase while two are involved in translational regulation.
Confirmation by RT-PCR.
Three of the genes exhibiting Crp-dependent glucose regulation were selected for confirmation by RT-PCR. Table 5 summarizes the results and compares them with the transcriptome data reported in Tables 1 and 2. The three genes analyzed are the aceE gene, which is subject to catabolite activation (Table 1), and the stress response genes clpB and hslT/ibpA (Table 2). Excellent agreement between the two methods is apparent. The only minor discrepancies are observed for the clpB and hslT/ibpA genes after growth of the crp mutant in LB plus glucose. In these two instances, the differences between the two methods are only twofold. Since the repressive effect of glucose was about 10-fold in both cases, this difference may be within the range of experimental error.
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TABLE 5. Comparison of RT-PCR and microarray analyses of gene expression in response to glucose and a crp mutationa
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DISCUSSION
In this paper, we have provided a comprehensive analysis of
Crp-mediated catabolite control in
E. coli. The transcriptome
data (Tables
1 and
2, as well as Table S1 at our website) were
confirmed and extended by (i) citing published data for representative
genes (Table
4), (ii) conducting phenotypic analyses that reflect
gene expression control (Tables
3 and S2), and (iii) conducting
RT-PCR experiments (Table
5). The agreement between the different
methods was striking, allowing us to conclude that the transcriptome
data are reliable.
Far more highly expressed genes proved to be subject to direct catabolite repression (Table 2) than catabolite activation (Table 1) (see also Table S1 at our website). Most of the carbon metabolic genes subject to catabolite control showed responses to glucose and the loss of Crp as expected. Thus, many genes that function in the initiation of carbon utilization proved to be strongly repressed, as were several central carbon metabolic genes (Table 2). By contrast, genes that showed glucose activation included the glucose transporter gene ptsG, those that encode specific metabolic enzymes that are required for growth under glucose fermentative conditions such as aceE and guaB (Table 1), a few regulatory nucleic acid-binding protein-encoding genes such as the fis and spf genes, and many genes subject to a positive growth rate response. Only in the former genes were Crp-binding sites identified in the control regions (Table 1).
Perhaps of greatest interest was the surprising number of stress genes that are subject to catabolite repression (Tables 2 and 5). These genes encode many chaperone proteins (1, 4, 38), all of the important cytoplasm ATP-dependent protease complexes that can function both as general chaperones and for protein degradation (9, 11, 20), and both heat shock and cold shock genes (12, 31, 40). Sometimes the repressive effects were very large (Table 2), even though a Crp-binding site could not be identified in the control regions of the encoding operons. Indirect mechanisms mediated by transcription factors under Crp control are therefore likely. Further experiments are required to ascertain what these mechanisms are.

ACKNOWLEDGMENTS
We thank Ron Sapolsky for assistance in the data analysis and
Mary Beth Hiller for assistance in the preparation of the manuscript.
This work was supported by National Institutes of Health grant GM64368 from the National Institute of General Medical Sciences. G.G. acknowledges a sabbatical fellowship from the Dirección General de Asuntos del Personal Académico de la Universidad Nacional Autónoma de México.

FOOTNOTES
* Corresponding author. Mailing address: Division of Biological Sciences, University of California at San Diego, La Jolla, CA 92093-0116. Phone: (858) 534-4084. Fax: (858) 534-7108. E-mail:
msaier{at}ucsd.edu.


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Journal of Bacteriology, June 2004, p. 3516-3524, Vol. 186, No. 11
0021-9193/04/$08.00+0 DOI: 10.1128/JB.186.11.3516-3524.2004
Copyright © 2004, American Society for Microbiology. All Rights Reserved.
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