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J. Biol. Chem., Vol. 277, Issue 15, 13175-13183, April 12, 2002
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From the Department of Chemical Engineering, UCLA,
Los Angeles, California 90095
Received for publication, November 12, 2001, and in revised form, January 7, 2002
This study characterized the transcript profile
of Escherichia coli in acetate cultures using DNA
microarray on glass slides. Glucose-grown cultures were used as a
reference. At the 95% confidence level, 354 genes were up-regulated in
acetate, while 370 genes were down-regulated compared with the
glucose-grown culture. Generally, more metabolic genes were
up-regulated in acetate than other gene groups, while genes involved in
cell replication, transcription, and translation machinery tended to be
down-regulated. It appears that E. coli commits more
resources to metabolism at the expense of growth when cultured in the
poor carbon source. The expression profile confirms many known features
in acetate metabolism such as the induction of the glyoxylate pathway,
tricarboxylic acid cycle, and gluconeogenic genes. It also
provided many previously unknown features, including induction of malic
enzymes, ppsA, and the glycolate pathway and repression of
glycolytic and glucose phosphotransferase genes in acetate. The carbon
flux delivered from the malic enzymes and PpsA in acetate was further
confirmed by deletion mutations. In general, the gene expression
profiles qualitatively agree with the metabolic flux changes and may
serve as a predictor for gene function and metabolic flux distribution.
Physiological characteristics of Escherichia coli using
acetate or glucose as a sole carbon and energy source have been studied for more than three decades (1, 2). Briefly, E. coli uptakes glucose using the phosphotransferase system that converts
extracellular glucose into intracellular glucose 6-phosphate, which can
be further metabolized by the glycolytic pathway to produce energy and
biosynthetic precursors. In the presence of glucose, the adenylate
cyclase is inactive, and the cAMP level is low. In the absence of
glucose, the adenylate cyclase is activated to produce cAMP, which when binding to the cAMP receptor protein activates the expression of a
large set of catabolite derepression genes (2, 3). On the other hand,
acetate is transported into the cell and converted to acetyl-CoA, which
is further metabolized through the glyoxylate shunt and the
tricarboxylic acid cycle. The acetate-metabolizing genes are typically
repressed in the presence of glucose. The induction and regulation of
acetate-metabolizing genes have been studied extensively (4). Since the
two carbon sources, glucose and acetate, are utilized by distinct
metabolic pathways, the metabolic flux distribution differs
significantly in these two carbon sources (5). Understanding global
gene expression profiles in different carbon sources is important to
the investigation of E. coli growth in natural environment,
where the availability of carbon sources changes dynamically.
Acetate-metabolizing culture is particularly relevant to the
biotechnology industry, since the accumulation of acetate in bioreactor
is commonly observed and often poses as an obstacle to high cell
density cultivation (6).
The recent advent of microarray technology allows a thorough analysis
of gene expression patterns in different environmental conditions (7,
8). In this approach, individual DNA probes are arrayed on a small
glass surface, and labeled first strand cDNA from specific tissue
or cell sources is hybridized onto the array. The amount of
fluorescence at each DNA probe spot correlates with the abundance of
specific mRNA transcript in the cell. This approach enables the
characterization of transcriptionally regulated pathways at a genomic
scale. In particular, the genome of E. coli has been
arrayed and used for the comprehensive analysis of the expression level
in various physiological states (9-21).
In this paper, we investigated the transcript profile of E. coli grown in acetate as the sole carbon source and compared it with the culture grown in glucose. We employed the fluorescence-based microarray system by spotting 96% E. coli open reading
frames on glass slides. The expression levels of each gene were
monitored by fluorescence-labeled cDNA using the printed DNA
microarray. A rigorous statistical method was used to access the
confidence interval of expression ratio of each gene by taking into
account slide-to-slide and culture-to-culture variations. The gene
expression profile in acetate was used to assess the metabolic flux
distribution in key pathways.
Preparation of DNA Array--
To make the E. coli
cDNA microarrays, PCRs were performed in 96-well plates using
Genosys E. coli ORFmers (Sigma) as primers and E. coli MG1655 (E. coli Genetic Stock Center, Yale
University) chromosome as a template. Eppendorf MasterTaq Kit
(Westbury, NY) was used for a 100-µl volume PCR. After PCR, 3 µl of
PCR solutions were run in 1% (w/v) agarose gel to examine the quality
of PCR products. Among 4290 primer pairs, 161 pairs failed to yield the desired PCR products. The remaining 4129 PCR products representing 96%
of the E. coli open reading frames were precipitated by
mixing with 10 µl of 3 M sodium acetate (pH 5.3) and 66 µl of isopropyl alcohol and centrifuging at 4000 rpm for 45 min. The
precipitants were dissolved in 100 µl of TE buffer (pH 8.0) and then
precipitated once again with isopropyl alcohol. After they were dried
overnight, the precipitants were dissolved in 10 µl of 350 mM sodium bicarbonate/carbonate buffer (pH 9.0) and printed
on a poly-L-lysine-coated glass slide using a robotic
spotter. The diameter of each spot was about 150 µm, and the distance
between the centers of the spots was about 250 µm.
The slide was hydrated over 95 °C water for 5 s and snap dried
on a 100 °C heating block. The probes were cross-linked to the
surface of the slide by UV light using the Stratalinker (Stratagene, La
Jolla, CA) at 400 mJ. The free lysine groups on the slide surface were
blocked by soaking slides in the mixture of 315 ml of
m-methylpyrrilidinone with 5 g of succinic anhydride
and 35 ml of sodium borate solution (0.2 M, pH 8.0) for 15 min. The slides were then washed with 95 °C water for 2 min and
transferred to 95% ethanol at room temperature for 1 min. The slides
were dried by centrifugation (22).
Strain and Culture Conditions--
E. coli MC4100
(F RNA Purification and Labeling--
Total RNA was purified from
roughly 1 × 109 cells using the RNeasy Midi Kit
(Qiagen, Valencia, CA) by following the manufacturer's instructions.
The RNA solution was incubated at 37 °C with 100 units of DNase
(Invitrogen) and 40 units of RNAsin ribonuclease inhibitor
(Promega, Madison, WI) for 30 min, extracted with phenol/chloroform, and then precipitated with 2.5 volumes of ethanol. After dissolved in
10-20 µl of RNase-free water, 30 µg of total RNA was labeled with
either Cy3 or Cy5 dCTPs during reverse transcription. The reverse transcription mixture included 200 units of Superscript RNase
H Hybridization and Scanning--
The concentrated Cy3 and Cy5
cDNA was resuspended in 10 µl of hybridization solution,
consisting of 50% formamide, 3× SSC, 1% SDS, 5× Denhardt's
solution, 0.1 mg/ml salmon sperm DNA, 0.05 mg/ml yeast total RNA. The
labeled cDNA in hybridization solution was denatured in 95 °C
for 2 min and cooled for 5 min at room temperature. The hybridization
solution was then placed on the slide and covered by cover glass. The
slide was assembled with a hybridization chamber (Corning) and
hybridized for 14-20 h at 42 °C inside a water bath. The slide was
washed in 2× SSC, 0.1% SDS for 5 min at room temperature and then
0.2× SSC for 5 min prior to scanning.
After it was dried by centrifugation, the hybridized slide was scanned
with an Affymetrix GMS-418 scanner (Santa Clara, CA). The two images
with the wavelengths of Cy3 and Cy5 dyes were individually analyzed by
use of image processing software, Imagene (Biodiscovery, Santa Monica,
CA). The median intensities of each spot calculated by the program were
obtained for further analysis.
Internal Normalization by Rank-invariant Method--
It has been
shown that the intensity of Cy3 and Cy5 labeling is different and that
the correlation between Cy3 and Cy5 intensity is
slide-dependent (24). Thus, each slide needed an internal normalization to account for the labeling effect. We used a
rank-invariant criterion to select genes that were nondifferentially
expressed in each slide (24). These genes were then used to determine the normalization curve. This method was based on the assumption that
if a gene is up-regulated, its intensity rank among one channel, say
Cy5, should be significantly higher than the rank among the other. This
method may fail in some extreme cases where a majority of genes are
up-regulated (or down-regulated) to the same extent. However, if there
are a large number of nondifferentially expressed genes, as in the case
of most cDNA microarray experiments, this method works well.
The ranks of Cy3 and Cy5 intensities of each gene on the slide were
separately computed. For a given gene, if the ranks of Cy3 and Cy5
intensities differ by less than a threshold value and the rank of the
averaged intensity was not among the extremely high or low, this gene
was classified as a rank-invariant gene. We used an iterative selection
scheme to achieve this task. In each iteration, the threshold for rank
difference was determined by the number of remaining genes multiplied
by a predetermined percentage, and the threshold for rank averaged
intensity was only applied in the first iteration. The iteration
stopped when the remaining set of genes did not decrease in the
selection criterion. After the selection of the rank-invariant genes, a
quadratic equation was used to fit the data. After normalization, the
logarithmic residuals were calculated as
log(Cy5i/f(Cy3i)), where Cy3i and Cy5i represent Cy3
and Cy5 intensities on spot i, and
f(x) is the normalization function between the two channels.
Confidence Interval of the Gene Expression Ratios--
We
previously developed a statistical method for computing the confidence
intervals of each gene (24). This method considers the slide-to-slide
and culture-to-culture variations. Calibration experiments, where Cy3-
and Cy5-labeled cDNAs from the same culture were pooled together
and co-hybridized to multiple slides, were used to assess the
between-slide variations. The culture-to-culture variation was assessed
by repeating the same experiment multiple times under the same
condition. With such data, we then used a hierarchical Bayesian model
and a Markov chain Monte Carlo
(MCMC)1 simulation to
determine the confidence intervals of the gene expression ratios. The
details of this method have been described previously (24). A computer
program was developed for such a simulation, which is available upon request.
Monitoring ppsA Promoter Activity Using Promoter Fusion to Green
Fluorescent Protein--
The gene coding green fluorescent protein was
amplified from pGFPuv (CLONTECH, Palo Alto, CA) and
cloned in EcoRI and HindIII restriction sites of
pJF118EH (25) to form pMK50. The ppsA promoter region was
amplified by the primers 5'-CGGAGCTCGCACAGAAGCGTAGAACG-3' and
5'-CGAATTCCTTTTGTGATAAATGAACGG-3' from the E. coli
chromosome and cloned into the EcoRI and EcoRV
sites of the pMK50. The resulting plasmid is named pKK1. The plasmid
was transformed into MC4100. Fluorescence intensity was measured using
a fluorimeter (ISA, Inc., Edison, NJ) during exponential growth phase
(A600 = 0.3-0.8) in M9 minimal medium
with glucose or acetate. Fluorescence was excited at a wavelength of
395 nm, and emission was measured at a wavelength of 509 nm. The
fluorescence intensity for each culture was normalized by dividing the
fluorescence by cell density at A600.
Deletion Mutation of pckA, ppsA, sfcA, and maeB--
Each gene
was disrupted by the method developed by Datsenko and Wanner (26).
Briefly, primers (listed in Table I) for deletion were used to amplify
the chloramphenicol-resistant gene from pKD3 (26). Strains harboring
pKD46 (26) were grown in SOB medium containing 200 mg/liter ampicillin
and 1 mM L-arabinose and transformed with the
PCR products using an electroporator (Eppendorf).
Chloramphenicol-resistant strains were selected on agar plate, and the
chloramphenicol-resistant gene was eliminated from the strain by
transforming pCP20 (26) and colony-purified at 42 °C. The
chloramphenicol-resistant gene and pCP20 were popped out during this
process. The mutation was confirmed by PCR with primers for
confirmation (Table I). Multiple deletion
mutations were performed sequentially.
Transcript Profiling Using DNA Microarray--
E. coli
transcription profiles in acetate and glucose minimal media were
compared using six sets of DNA microarray data generated from three
independent experiments. To achieve a balanced physiological state, the
E. coli strain was subcultured at least twice after initial
inoculation from an LB agar plate and harvested in the midlog phase.
Total RNA was purified and labeled with Cy3 or Cy5 dCTP during reverse
transcription. In the first two experiments, we labeled the RNA from
the glucose culture with Cy5 dyes and the RNA from the acetate culture
with Cy3. The labels were reversed in the third experiment. In each
experiment, the labeled cDNAs were mixed and then hybridized on two
microarray slides. For calibration experiments, RNA from glucose
culture was divided into two tubes and labeled with Cy3 and Cy5 dCTP,
respectively, and hybridized to two slides. The same process was
repeated with RNA purified from an acetate culture. Four data sets of
calibration experiments were used to provide the statistical parameter
for slide-dependent variation of each gene. This
information was incorporated to evaluate the confidence intervals in
the MCMC method.
The arraying solutions without DNA were arrayed 24 times on each slide in four different positions and used as negative
controls. The average plus two S.D. values of intensities of the
negative controls was computed in every slide and used as a threshold
for data filtering. The spots showing intensity lower than the
threshold were filtered out. These spots were attributed to either
improper probe arraying or low expression. The spots whose intensities exceeded the detection range of the scanner were also excluded. After
filtering the outliers, the rank-invariant genes were selected as a
basis for normalization, and then the logarithmic ratio of expression
levels were calculated as described above. The four sets of calibration
data and six sets of acetate/glucose comparison data were used to
access the expression ratios using the MCMC method. The expression
levels of 3649 genes, which passed the threshold filtering, were
computed successfully. Among them, 354 genes were up-regulated, and 370 genes were down-regulated with 95% confidence in acetate medium
compared with those in glucose medium. Because of the high number of
simultaneous statistical tests used, the error rate at 5% for 4000 genes will generate 200 false positives when discussing the genome as a
whole. Therefore, a more stringent confidence level was also used. With
99% confidence, 185 and 177 genes were up- and down-regulated,
respectively. At this confidence level, the error rate of 1% will
produce 40 false positives. However, when discussing individual genes,
a 95% confidence level was appropriate for most purposes discussed
here unless specified otherwise.
In Fig. 1, the logarithmic ratio of each
gene was plotted against the average log intensity of that gene. The
red and green dots represent the genes
that were up- or down-regulated, respectively, with 95 or 99%
confidence, whereas the black dots represent the genes that were not differentially regulated based on the statistical judgment. Fig. 1 shows that the -fold difference does not necessarily correlate with statistical significance, as previously pointed out
(10). For example, rpmF was down-regulated more than 4-fold in acetate, but it was statistically insignificant because of its high
variances of the data. Meanwhile, the dnaN level was repressed only 1.3-fold with very small variance, and thus this gene
was determined to be down-regulated with a 99% confidence level. The
source of the variance can be severalfold, including poor quality or
quantity of PCR products, cross-contamination by nonspecific binding,
and high background on the slides. Therefore, a vigorous statistical
analysis and repeats of the experiment are essential to obtain reliable
data. The expression ratios and confidence intervals of all the genes
are available on the World Wide Web at
www.seas.ucla.edu/~liaoj/glcace.html.
Functional Groups of Differentially Regulated Genes--
The
differentially expressed genes with 95 and 99% confidence intervals
were further classified into 23 groups by their functions (Table
II). As expected, the most significant
difference of growing in different carbon sources occurred among the
central intermediary metabolic genes. 27 and 14% of the 161 genes in
this group were significantly induced or repressed, respectively, in
acetate medium compared with those in glucose medium. In the functional
groups, such as carbon compound catabolism and fatty acid
metabolism, the numbers of up-regulated genes surpassed those of
down-regulated genes significantly, suggesting that these groups of
genes were generally repressed by glucose or induced by acetate.
Apparently, E. coli up-regulates the genes that allow the
utilization of any possible carbon sources such as fatty acids when
growing in a relatively poor carbon source such as acetate.
On the other hand, the number of down-regulated genes was much higher
than up-regulated genes in many functional groups. For example, amino
acid biosynthesis and nucleotide biosynthesis genes were generally
down-regulated. The number of repressed genes exceeds the induced genes
by 28 to 9 and 10 to 3 in these two groups, respectively, suggesting
that E. coli turned down the expression of biosynthetic
genes to match the low growth rate and save energy in the poor carbon
source. In addition, in the functional groups such as cell structure,
DNA replication, transcription, and translation, the number of
down-regulated genes surpasses the up-regulated genes by 21 to 7, 22 to
4, 9 to 3, and 47 to 2, respectively. This result can be explained by
the fact that the growth rate was much lower in acetate compared with
glucose as a carbon source. Indeed, many of the genes belonging to
these categories were known to be correlated with the growth rate of
the cell (27).
Central Metabolic Genes Involved in Acetate Metabolism--
When
acetate is metabolized as a sole carbon and energy source in E. coli, it is first activated to acetyl-CoA and then metabolized in
the tricarboxylic acid cycle and glyoxylate shunt (4). Two pathways
were responsible for the acetate activation,
pta-ackA and acs. Interestingly,
pta and ackA were both down-regulated by
~2-fold in acetate. On the other hand, acs was induced
more than 8-fold in acetate, one of the most significantly up-regulated genes. This result suggests that Acs is the major enzyme for acetate uptake and activation. Acetyl-CoA is converted to malate through the
glyoxylate shunt. The induction mechanism of the glyoxylate shunt
genes, aceB and aceA, in acetate has been well
characterized (4). These two genes are located in the same operon with
aceK (encoding isocitrate dehydrogenase kinase/phosphatase).
The expression of aceB gene was not monitored in this
experiment because of failed PCR amplification. However, the other
genes (aceA and aceK) in the same operon showed
more than 10-fold up-regulation. Not only the aceBAK operon
but also the glcDFGB operon was induced significantly. The
last gene in the glc operon is the secondary malate synthase (glcB), which can replace the malate synthase A
(aceB) in acetate (28) when aceB is mutated. This
operon could be induced by acetate (29). Meanwhile, most of the
tricarboxylic acid cycle genes (gltA, acnA,
acnB, icdA, sucABCD operon,
sdhCDAB operon, fumA, fumC,
fumB, and mdh) were up-regulated. In particular,
the genes involved in the glyoxylate pathway, mdh,
gltA, and acnB, were highly up-regulated more
than 4-fold. Together with induction of aceBAK operon, these
results confirmed that metabolic flux in the glyoxylate cycle (4) is
very high in acetate (Fig. 2 and Table
III).
The gluconeogenic enzyme, phosphoenolpyruvate carboxykinase (coded by
pckA), is known to be responsible for delivering the carbon
flux from the tricarboxylic acid cycle to the gluconeogenic pathways in
acetate (4, 5). Indeed, pckA was 5-14-fold induced in
acetate. Surprisingly, ppsA (coded for phosphoenolpyruvate synthase) was also induced 9-21-fold in acetate, although the gene
product was nonessential for gluconeogenesis during growth on acetate
(5). This result suggested that PpsA may play an important role for
gluconeogenic flux when E. coli is grown in acetate.
Together with the malic enzymes, PpsA could serve the same function as
PckA. Indeed, both NAD-dependent (sfcA) and
NADP-dependent (maeB) malic enzymes were also
induced. The functions of these gluconeogenic genes in acetate were
further verified with deletion mutations, which will be discussed shortly.
Other Carbon and Energy Metabolism Genes--
Many glycolytic
genes (pfkA, fba, gapA,
epd, pgk, eno, pykF, and
ppc) were down-regulated in acetate. In addition, the first two pentose pathway genes (zwf and gnd) and
pyruvate dehydrogenase (aceEF operon) were also
significantly down-regulated. These data correlate with the reduced
metabolic flux in these pathway genes. The expression levels of many
carbon transport genes were also affected seriously by different carbon
sources. The genes involved in glucose transport, the
ptsHI-crr operon and ptsG, were
repressed significantly in acetate, 1.5-2-fold and 2-4-fold,
respectively. These operons were known to be regulated by
Mlc (30). In the absence of glucose, Mlc
represses the operons. In the presence of extracellular glucose,
the conformation of EIIBCglc protein is changed and
bound strongly with Mlc, which no longer represses the operons.
On the other hand, transport genes for other carbon sources were
induced significantly by catabolite derepression. Examples are the
galactose ABC transporter operon (mglBAC, 4-8-fold), the ribose uptake gene operon (rbsD and rbsACB
operon, 2-8-fold and 3-5-fold, respectively), the
N-acetyl-D-glucosamide transport subunit
(nagE, 2-4-fold), the arginine ABC transport gene
(argT, 2-6-fold), the C4 dicarboxylate transporter gene
(dctA, 3-6-fold) (31), tagatose metaoblic genes
(gatYZ operon, 3-10-fold), and the maltose translocating
gene (lamB, 2-4-fold).
Interestingly, not only the genes involved in glyoxylate shunt pathway
(ace and glc operons), but also those
involved in other glyoxylate-related metabolic pathways, such as
glycolate and allatoine metabolism, were all up-regulated (Fig.
3 and Table
IV). These genes are located close
together on the chromosome and expressed by four different
operons (32). However, the role of these genes in acetate metabolism is
unknown.
Genes Involved in the Cell Machinery--
Among the
genes involved in the cell structure, DNA replication, transcription,
and translation, a total of 16 genes (3.3% of the total) were
up-regulated, whereas 99 genes (20%) were down-regulated. Generally,
the expression data of these groups of genes do not vary much from
experiment to experiment compared with those of metabolic genes.
Therefore, the accuracy of measurements was higher. Among 60 ribosomal
proteins, including S1-S21, L1-L25, L27-L36, two EF-Tu subunits,
EF-Ts, and EF-G, the expression levels of 40 genes were successfully
monitored. Among them, more than 70% (29 of 40) were down-regulated in
acetate at the 95% confidence level. The down-regulation of these
genes was attributed to the growth rate-dependent
regulation (27).
Roles of Gluconeogenic Genes in Acetate Growth--
Although
ppsA was known to be nonessential for growth in acetate, it
was up-regulated 9-21-fold in acetate (Fig. 2 and Table III). To
verify the induction of ppsA transcript in acetate, we constructed a transcriptional fusion between the ppsA
promoter and the green fluorescence protein. The E. coli
MC4100 harboring the reporter plasmid (pKK1) was cultured in either
glucose or acetate minimal media, and the fluorescence level was
monitored in the midlog phase. In two independent experiments, the
fluorescence level of the cells grown in acetate was detected at least
4-fold higher than the level in glucose (Fig.
4). This result supports the data
obtained by DNA microarray analysis.
Although previously undetermined, the role of PpsA in acetate-grown
E. coli may be the delivery of metabolites from the
tricarboxylic acid cycle to the Embden-Meyerhoff pathway. This
gluconeogenic function requires the malic enzymes (coded by
sfcA and maeB), which were also up-regulated in
acetate. The malic enzyme-PpsA pathway can theoretically serve as an
alternative to PckA, which is known to be the main gluconeogenic
pathway in acetate-grown E. coli and is also up-regulated
significantly. To determine the roles of these genes in E. coli growth in acetate, a set of deletion mutants were constructed
as described. The deletions of the genes on the chromosome were
confirmed by PCR using the primers outside of the genes. The growth
rates of those mutants were measured in acetate (Table
V). Almost no growth inhibition was
detected in either pckA or ppsA single mutants,
while the growth of the pckA ppsA double mutant was
abolished in acetate (Table V). These results showed that PckA and
PpsA-malic enzyme can serve as an alternative pathway to each other and
that these are the only two gluconeogenic enzymes to provide the
phosphoenolpyruvate pool. We further examined the functions of the
malic enzymes, which are required to supply pyruvate, the substrate of
PpsA. The deletion mutation of one malic enzyme, either sfcA
or maeB, in the pckA background did not reduce
the growth rate in acetate. However, the deletion of both malic enzymes
with pckA mutation abolished the growth in acetate (Table
V). The series of mutation studies proved that PckA and PpsA-malic
enzymes are the two substitutable pathways that can deliver carbon flux
from the tricarboxylic acid cycle to the Embden-Meyerhoff
pathway.
Statistics--
Although DNA microarray has gained momentum in
genomic scale expression profiling, methods for assessing statistical
significance have just begun to be developed. The initial approach of
assigning genes that shows a more than 2-fold expression ratio as
"significant" has proven unsatisfactory (10). Because the
variations among the signals on the array are
gene-dependent, a common threshold for all of the genes is
inadequate. Our data here again show the same conclusion. Long et
al. (33) proposed a t test using the Bayesian inference
of variance. This approach was developed for the one-channel membrane
arrays and used the intensity data of the two treatments as variables
for comparison. This method is not optimal for the two-channel glass
arrays, which allow the direct comparison of two treatments (acetate
and glucose) simultaneously on the same slide. Such two-channel systems
reduce the slide-to-slide variation, which can be significant in both
glass and membrane arrays. The method used here (24) directly compares
the two treatments by taking the log ratio of the normalized intensity. The gene-dependent, slide-to-slide and
experiment-to-experiment variations were accounted for in a
hierarchical Bayesian model. The gene-specific confidence intervals as
a result of multiple experiments and multiple slides were calculated.
Because of the large number of statistical tests, we calculated both 95 and 99% confidence levels. At these two confidence levels, about 200 and 40 false positives, respectively, will occur among 4000 genes, when
the whole genome is considered simultaneously.
Metabolic Genes--
In general, the expression profiles obtained
from E. coli grown in glucose and acetate agree with the
direction of intracellular carbon fluxes. In acetate cultures, the
phosphotransferase systems for glucose uptake and glycolytic genes were
highly down-regulated compared with those in glucose cultures. On the
other hand, genes involved in acetate uptake (acs), the
glyoxylate cycle (aceBAK and glcB), the
tricarboxylic acid cycle (gltA, icdA,
acnA, acnB, sucABCD,
sdhCDAB, fumA, fumB, fumC,
and mdh), and gluconeogenesis (pckA,
ppsA, sfcA, and maeB) were all
up-regulated in acetate. The only exception is the Pta-AckA pathway,
which was down-regulated significantly in acetate. This result suggests
that Acs is the main path for acetate uptake, whereas the Pta-AckA
pathway is used for acetate excretion during growth on glucose. Because
the mutation of both pta and ackA inhibited cell
growth in acetate (34), it has been suspected that this pathway also
delivers a significant amount of carbon flux into the cell. However,
acs induction in acetate has been shown to be impaired in
the pta ackA double mutant (35), which may explain why the
pta ackA mutant did not grow well in acetate.
Among the genes that are highly up-regulated in acetate,
ppsA was unexpected, since this gene was dispensable during
growth on acetate. The ppsA induction was confirmed
independently by a promoter fusion experiment. The role of
ppsA during growth on acetate was demonstrated using
mutation analysis. In acetate, PpsA and the two malic enzymes form an
alternative pathway to PckA. Deletion mutation of either one of the
pathways has no effect on growth rate in acetate, suggesting that
either one has sufficient capacity to deliver carbon flux to the
Embden-Meyerhoff pathway. The malic enzyme pathway was not
clearly defined in E. coli previously. It has been known for
more than 30 years that two malic enzymes existed in E. coli
(36). However, the genes coding those enzymes were not mapped on the
genetic level until recently. NAD-dependent malic enzyme,
sfcA, was cloned and characterized recently (37), whereas
NADP-dependent malic enzyme, maeB, was only
predicted with sequence similarity. Interestingly, both sfcA
and maeB were highly up-regulated in acetate. In addition,
mutation analysis showed that those genes can compensate each other in
acetate cultures.
The most striking difference between acetate-grown and glucose-grown
E. coli cultures is the up-regulation of metabolic genes such as acs, pckA, ppsA, and
aceBAK. These genes are either essential or carry
significant metabolic flux. In general, results from the microarray
analysis summarized in Fig. 2 show a dramatic agreement with the
deduced metabolic pathway for acetate growth. All of the essential or
flux-carrying pathways (glyoxylate shunt, tricarboxylic acid cycle)
were up-regulated, and most of the unused pathways (glycolysis) were
down-regulated. It is tempting to suggest that the significantly
up-regulated genes delineate the functional pathways. The PpsA-malic
enzymes pathway, the PckA pathway, and the Acs pathway are good
supports for this argument. If this theory holds, one can predict
functional pathways through gene expression profiling.
Regulatory Systems--
Although many global regulators have been
characterized, it is not straightforward to identify the regulators
involved in the change of expression profiles form glucose to acetate
cultures. For example, the tricarboxylic acid cycle genes were
up-regulated in acetate partly by ArcA and Fnr (38). However, the
effects of those regulators were not observed in other ArcA or
Fnr-regulated genes, such as cydAB, cyoABCDE,
dmsABC, and sodA (38). It is possible that
multiple regulators co-control these genes to manifest a complex
response to the environment. The only regulatory system showing
ubiquitous effects on many different genes is the catabolite derepression by cAMP-cAMP receptor protein. This regulator activates many genes in acetate. For example, the tricarboxylic acid cycle and
gluconeogenic pathway and many carbon transport genes were fully or
partially induced by catabolite derepression. Among the biosynthetic
genes, which were generally down-regulated, a few genes, such as
tnaA, ilvBN, dadX, and
aspA, were strongly up-regulated, possibly attributable to
this mechanism (39-41). Catabolite derepression also induces fatty
acid degradation genes, fadD and fadBA (42), and
flagella regulatory genes (flhCD) (43) in acetate growth. Finally, the 11-20-fold and 3-5-fold inductions of cstA
and astA, of which the functions are not clear, were also
attributed to this mechanism (44, 45).
Functional Genomics--
One of the outstanding features of the
microarray experiment is its utility in the prediction of gene
functions. In this experiment, the function of maeB, the
predicted but unconfirmed NADP-dependent malic enzyme, was
successfully examined with both microarray and mutation experiments.
This gene was up-regulated about 4-6-fold in acetate compared with
glucose cultures. This result suggested that the maeB gene
product is involved in acetate metabolism, which is in agreement with
the prediction of the gene function (malic enzyme) and successfully
confirmed by mutation studies. Another example is the
b2341-b2342 operon, whose function is
presumed to be similar to that of the fadAB fatty acid
degradation genes by the KEGG data base (46). Indeed, both operons were
up-regulated 2.5-4- and 2.8-7-fold, respectively, in acetate and have
a FadR binding site on their promoters (47).
Meanwhile, the expression level of the newly characterized class I type
of fructose-bisphosphate aldolase, coded by dhnA (48), showed 3-fold induction in acetate, in contrast to the known
fructose-bisphosphate aldolase, fba, which was
down-regulated 1.5-2.5-fold as other glycolytic genes. This result
suggests that the dhnA product may serve other functions
in vivo. Further, the membrane-associated malate
dehydrogenase gene, mqo (yojH) (49), was not
regulated in acetate, in contrast to a more than 2-6-fold increase of
the other malate dehydrogenase, mdh, and up-regulation of
other tricarboxylic acid cycle genes. Again, the expression pattern
suggests that the mqo gene may serve other functions. The
inconsistency between expression regulation of the genes and the
presumed function calls for further investigations. Similarly, the two
pyruvate kinase genes, pykA and pykF, were
nonregulated and down-regulated, respectively, in acetate. This result
suggests that pykA serves a different role than
pykF, which plays the glycolytic role. In addition, many
unknown or poorly characterized open reading frames were differentially
regulated in acetate compared with glucose cultures. Those data are
expected to provide important information to predict their functions in
the future.
Acetate Shock--
The effect of acetate shock induced by high
concentrations of sodium acetate (100 mM) was previously
studied with membrane arrays (13). Those experiments aimed to study the
physiological response of E. coli to a sudden addition of a
high concentration of acetate during growth in a glucose minimal
medium. In contrast, we monitored the transcript profile of E. coli during balanced growth in an acetate medium with a nontoxic
concentration (42 mM). As expected, the results from these
experiments were not the same. Although there were some common
phenomena, such as down-regulation of ribosomal proteins and
up-regulation of katE, dhnA, talA, and pflB, most transcript profiles in this experiment were
different from the acetate shock experiment. In particular, the effect
of *
This work was supported by National Science Foundation Grant
EEC-0087589 and National Institute of Standards and Technology Grant
70NANBOH0064.The costs of publication of this
article were defrayed in part by the
payment of page charges. The article
must therefore be hereby marked
"advertisement" in
accordance with 18 U.S.C. Section
1734 solely to indicate this fact.
Published, JBC Papers in Press, January 28, 2002, DOI 10.1074/jbc.M110809200
The abbreviation used is:
MCMC, Markov chain
Monte Carlo.
Global Expression Profiling of Acetate-grown Escherichia
coli*
ABSTRACT
TOP
ABSTRACT
INTRODUCTION
EXPERIMENTAL PROCEDURES
RESULTS
DISCUSSION
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INTRODUCTION
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ABSTRACT
INTRODUCTION
EXPERIMENTAL PROCEDURES
RESULTS
DISCUSSION
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EXPERIMENTAL PROCEDURES
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ABSTRACT
INTRODUCTION
EXPERIMENTAL PROCEDURES
RESULTS
DISCUSSION
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araD139 (argF-lac) U169 rpsL150 relA1 flb5301
deoC1 ptsF25 rbsR) was cultured in M9 minimal medium (23) containing either 0.5% (w/v) glucose or 0.25% (w/v) acetate as carbon
sources for the steady state experiment. 125 mg/liter (w/v) of arginine
was added to the medium. When an OD of the cell reached 0.4-0.6 at 550 nm, the cultures were quickly chilled in an ethanol/dry ice bath and
harvested by centrifugation for RNA purification.
reverse transcriptase (Invitrogen), random hexamers
(Invitrogen), 0.5 mM dATP, dTTP, and dGTP, 0.2 mM dCTP, and 0.1 mM Cy3 or Cy5 dCTP (Amersham
Biosciences). After reverse transcription, the RNA was degraded by
incubating at 65 °C for 40 min after adding 2 µl of 0.5 M EDTA (pH 8.0) and 5 µl of 1 N NaOH. The
cDNAs, labeled with either Cy3 or Cy5, were diluted with 60 µl of TE buffer (pH 8.0) and then mixed together. The labeled
cDNA mixture was then concentrated to 1-2 µl by using Micron-50
from Millipore Corp. (Bedford, MA).
The primer sequences used to delete the gluconeogenic genes
RESULTS
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ABSTRACT
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EXPERIMENTAL PROCEDURES
RESULTS
DISCUSSION
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View larger version (16K):
[in a new window]
Fig. 1.
Expected logarithmic ratios between two dyes
(y axis) in calibration experiments
(a) and glucose-acetate comparison experiments
(b and c) were plotted against
log-transformed mean fluorescence intensities of the genes. The
red dots represent up-regulated genes, and
green dots show down-regulated genes with 95%
confidence level (b) and 99% confidence level
(c).
Numbers and percentages of up- or down-regulated genes in each
functional group
View larger version (25K):
[in a new window]
Fig. 2.
The expression levels of central metabolic
pathway genes in acetate compared with those in glucose. The
numbers beside gene names represent the expected -fold
changes of expression levels calculated from six repetitions of
experiments. The red arrows represent the induced
genes, and green arrows represent the repressed
genes in acetate compared with in glucose with more than 95%
confidence. The thicker the arrows, the higher
the genes were regulated. G6P, glucose 6-phosphate;
F6P, fructose 6-phosphate; F1,6P, fructose
1,6-phosphate; G3P, glyceraldehyde 3-phosphate;
PYR, pyruvate; AcCoA, acetyl-CoA;
Ac-P, acetylphosphate; ICT, isocitrate;
SUC, succinate; OAA, oxaloacetate;
6PGnt, 6-phosphogluconate; Ru5P, ribulose
5-phosphate; R5P, ribose 5-phosphate; X5P,
xylulose 5-phosphate. The gene names followed the E. coli K-12 linkage map in Ref. 50.
The -fold changes of central metabolic gene expression in acetate
compared with glucose
View larger version (18K):
[in a new window]
Fig. 3.
The expression profiles of glyoxylate
metabolic genes in acetate compared with in glucose. The
numbers, arrows, and abbreviations are
as described in the legend to Fig. 2.
The -fold changes of glycolate metabolic gene expression in acetate
compared with glucose
View larger version (10K):
[in a new window]
Fig. 4.
The expression levels of green fluorescence
protein fused to the ppsA promoter in E. coli grown in glucose and acetate minimal medium. The
y axis is the relative fluorescence level normalized to cell
density and the average fluorescence level in glucose.
Growth rates of the mutants of gluconeogenic pathway genes in
acetate
DISCUSSION
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ABSTRACT
INTRODUCTION
EXPERIMENTAL PROCEDURES
RESULTS
DISCUSSION
REFERENCES
S that was significant in acetate shock was not
observed in acetate culture.
FOOTNOTES
To whom correspondence should be addressed: Dept. of Chemical
Engineering, 5531 Boelter Hall, UCLA, Los Angeles, CA 90095. Tel.:
310-825-1656; Fax: 310-206-1642; E-mail: liaoj@ucla.edu.
ABBREVIATIONS
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 J. C. Silva, R. Denny, C. Dorschel, M. V. Gorenstein, G.-Z. Li, K. Richardson, D. Wall, and S. J. Geromanos Simultaneous Qualitative and Quantitative Analysis of theEscherichia coli Proteome: A Sweet Tale Mol. Cell. Proteomics, April 1, 2006; 5(4): 589 - 607. [Abstract] [Full Text] [PDF]
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 C. Sabatti and G. M. James Bayesian sparse hidden components analysis for transcription regulation networks Bioinformatics, March 15, 2006; 22(6): 739 - 746. [Abstract] [Full Text] [PDF]
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S. J. Lee, D.-Y. Lee, T. Y. Kim, B. H. Kim, J. Lee, and S. Y. Lee Metabolic Engineering of Escherichia coli for Enhanced Production of Succinic Acid, Based on Genome Comparison and In Silico Gene Knockout Simulation Appl. Envir. Microbiol., December 1, 2005; 71(12): 7880 - 7887. [Abstract] [Full Text] [PDF]
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 T.-W. Nam, Y.-H. Park, H.-J. Jeong, S. Ryu, and Y.-J. Seok Glucose repression of the Escherichia coli sdhCDAB operon, revisited: regulation by the CRP{middle dot}cAMP complex Nucleic Acids Res., November 27, 2005; 33(21): 6712 - 6722. [Abstract] [Full Text] [PDF]
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Y. Oda, S. K. Samanta, F. E. Rey, L. Wu, X. Liu, T. Yan, J. Zhou, and C. S. Harwood Functional Genomic Analysis of Three Nitrogenase Isozymes in the Photosynthetic Bacterium Rhodopseudomonas palustris J. Bacteriol., November 15, 2005; 187(22): 7784 - 7794. [Abstract] [Full Text] [PDF]
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K. C. Kao, L. M. Tran, and J. C. Liao A Global Regulatory Role of Gluconeogenic Genes in Escherichia coli Revealed by Transcriptome Network Analysis J. Biol. Chem., October 28, 2005; 280(43): 36079 - 36087. [Abstract] [Full Text] [PDF]
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L. Rohlin, J. D. Trent, K. Salmon, U. Kim, R. P. Gunsalus, and J. C. Liao Heat Shock Response of Archaeoglobus fulgidus J. Bacteriol., September 1, 2005; 187(17): 6046 - 6057. [Abstract] [Full Text] [PDF]
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D.-J. Tang, Y.-Q. He, J.-X. Feng, B.-R. He, B.-L. Jiang, G.-T. Lu, B. Chen, and J.-L. Tang Xanthomonas campestris pv. campestris Possesses a Single Gluconeogenic Pathway That Is Required for Virulence J. Bacteriol., September 1, 2005; 187(17): 6231 - 6237. [Abstract] [Full Text] [PDF]
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A. Perrenoud and U. Sauer Impact of Global Transcriptional Regulation by ArcA, ArcB, Cra, Crp, Cya, Fnr, and Mlc on Glucose Catabolism in Escherichia coli J. Bacteriol., May 1, 2005; 187(9): 3171 - 3179. [Abstract] [Full Text] [PDF]
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M. Liu, T. Durfee, J. E. Cabrera, K. Zhao, D. J. Jin, and F. R. Blattner Global Transcriptional Programs Reveal a Carbon Source Foraging Strategy by Escherichia coli J. Biol. Chem., April 22, 2005; 280(16): 15921 - 15927. [Abstract] [Full Text] [PDF]
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 A. J. Wolfe The Acetate Switch Microbiol. Mol. Biol. Rev., March 1, 2005; 69(1): 12 - 50. [Abstract] [Full Text] [PDF]
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M. Meister, S. Saum, B. E. Alber, and G. Fuchs L-Malyl-Coenzyme A/{beta}-Methylmalyl-Coenzyme A Lyase Is Involved in Acetate Assimilation of the Isocitrate Lyase-Negative Bacterium Rhodobacter capsulatus J. Bacteriol., February 15, 2005; 187(4): 1415 - 1425. [Abstract] [Full Text] [PDF]
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L. J. Lloyd, S. E. Jones, G. Jovanovic, P. Gyaneshwar, M. D. Rolfe, A. Thompson, J. C. Hinton, and M. Buck Identification of a New Member of the Phage Shock Protein Response in Escherichia coli, the Phage Shock Protein G (PspG) J. Biol. Chem., December 31, 2004; 279(53): 55707 - 55714. [Abstract] [Full Text] [PDF]
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D. Zheng, C. Constantinidou, J. L. Hobman, and S. D. Minchin Identification of the CRP regulon using in vitro and in vivo transcriptional profiling Nucleic Acids Res., November 1, 2004; 32(19): 5874 - 5893. [Abstract] [Full Text] [PDF]
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D. A. Rodionov, M. S. Gelfand, and N. Hugouvieux-Cotte-Pattat Comparative genomics of the KdgR regulon in Erwinia chrysanthemi 3937 and other gamma-proteobacteria Microbiology, November 1, 2004; 150(11): 3571 - 3590. [Abstract] [Full Text] [PDF]
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T. Sato, H. Imanaka, N. Rashid, T. Fukui, H. Atomi, and T. Imanaka Genetic Evidence Identifying the True Gluconeogenic Fructose-1,6-Bisphosphatase in Thermococcus kodakaraensis and Other Hyperthermophiles J. Bacteriol., September 1, 2004; 186(17): 5799 - 5807. [Abstract] [Full Text] [PDF]
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K. Palyada, D. Threadgill, and A. Stintzi Iron Acquisition and Regulation in Campylobacter jejuni J. Bacteriol., July 15, 2004; 186(14): 4714 - 4729. [Abstract] [Full Text] [PDF]
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T. L. Nicholson, K. Chiu, and R. S. Stephens Chlamydia trachomatis Lacks an Adaptive Response to Changes in Carbon Source Availability Infect. Immun., July 1, 2004; 72(7): 4286 - 4289. [Abstract] [Full Text] [PDF]
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R. Gerstmeir, A. Cramer, P. Dangel, S. Schaffer, and B. J. Eikmanns RamB, a Novel Transcriptional Regulator of Genes Involved in Acetate Metabolism of Corynebacterium glutamicum J. Bacteriol., May 1, 2004; 186(9): 2798 - 2809. [Abstract] [Full Text] [PDF]
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B. Lakaye, B. Wirtzfeld, P. Wins, T. Grisar, and L. Bettendorff Thiamine Triphosphate, a New Signal Required for Optimal Growth of Escherichia coli during Amino Acid Starvation J. Biol. Chem., April 23, 2004; 279(17): 17142 - 17147. [Abstract] [Full Text] [PDF]
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Q. Hua, C. Yang, T. Oshima, H. Mori, and K. Shimizu Analysis of Gene Expression in Escherichia coli in Response to Changes of Growth-Limiting Nutrient in Chemostat Cultures Appl. Envir. Microbiol., April 1, 2004; 70(4): 2354 - 2366. [Abstract] [Full Text] [PDF]
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 K. C. Kao, Y.-L. Yang, R. Boscolo, C. Sabatti, V. Roychowdhury, and J. C. Liao Transcriptome-based determination of multiple transcription regulator activities in Escherichia coli by using network component analysis PNAS, January 13, 2004; 101(2): 641 - 646. [Abstract] [Full Text] [PDF]
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T. E. Allen, M. J. Herrgard, M. Liu, Y. Qiu, J. D. Glasner, F. R. Blattner, and B. O. Palsson Genome-Scale Analysis of the Uses of the Escherichia coli Genome: Model-Driven Analysis of Heterogeneous Data Sets J. Bacteriol., November 1, 2003; 185(21): 6392 - 6399. [Abstract] [Full Text] [PDF]
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D. Hagiwara, M. Sugiura, T. Oshima, H. Mori, H. Aiba, T. Yamashino, and T. Mizuno Genome-Wide Analyses Revealing a Signaling Network of the RcsC-YojN-RcsB Phosphorelay System in Escherichia coli J. Bacteriol., October 1, 2003; 185(19): 5735 - 5746. [Abstract] [Full Text] [PDF]
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 Y. LI, K. COLE, and S. ALTMAN The effect of a single, temperature-sensitive mutation on global gene expression in Escherichia coli RNA, May 1, 2003; 9(5): 518 - 532. [Abstract] [Full Text] [PDF]
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T. Polen, D. Rittmann, V. F. Wendisch, and H. Sahm DNA Microarray Analyses of the Long-Term Adaptive Response of Escherichia coli to Acetate and Propionate Appl. Envir. Microbiol., March 1, 2003; 69(3): 1759 - 1774. [Abstract] [Full Text] [PDF]
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