Published online before print
January 14, 2003, 10.1101/gr.401003
Vol 13, Issue 2, 206-215, February 2003
LETTER
A Microarray-Based Antibiotic Screen Identifies a Regulatory Role for Supercoiling in the Osmotic Stress Response of Escherichia coli
Kevin J. Cheung,
Vasudeo Badarinarayana,
Douglas W. Selinger,
Daniel Janse and
George M. Church1
Department of Genetics, Harvard Medical School,
Boston, Massachusetts 02115, USA
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ABSTRACT
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Changes in DNA supercoiling are induced by a wide range of
environmental stresses in Escherichia coli, but the
physiological significance of these responses remains unclear. We now
demonstrate that an increase in negative supercoiling is necessary for
transcriptional activation of a large subset of osmotic stress-response
genes. Using a microarray-based approach, we have characterized
supercoiling-dependent gene transcription by expression profiling under
conditions of high salt, in conjunction with the microbial antibiotics
novobiocin, pefloxacin, and chloramphenicol. Algorithmic clustering and
statistical measures for gauging cellular function show that this
subset is enriched for genes critical in osmoprotectant
transport/synthesis and rpoS-driven stationary phase
adaptation. Transcription factor binding site analysis also supports
regulation by the global stress  factor rpoS. In addition,
these studies implicate 60 uncharacterized genes in the osmotic stress
regulon, and offer evidence for a broader role for supercoiling in the
control of stress-induced transcription.
Changes in the level of DNA supercoiling coincide with a diverse
spectrum of environmental events including
nutritional upshift, entry into stationary phase, temperature stress,
peroxide stress, and osmotic shock (Hengge-Aronis 1999 ; Lopez-Garcia
and Forterre 2000; Weinstein-Fischer et al. 2000; Travers et al. 2001).
These phenomena have been best characterized in the stress responses of
Escherichia coli but have been described in a number of other
bacterial species as well (Rohde et al. 1994; Jordi et al.
1995; Alice and Sanchez-Rivas 1997; Ali et al. 2002). Concurrently,
expression and activity of global transcriptional regulators such as
Fis (fis), cyclic AMP receptor protein (crp), and
stress-induced factor S (rpoS) have been
shown to be dependent on the supercoiling state of the cell (Finkel and
Johnson 1992; Schneider et al. 1999). Supercoiling modulates the
transcriptional effects of these regulators directly by affecting the
efficiency of protein binding to their DNA targets, or indirectly by
altering the transcriptional expression of the regulators themselves.
These observations have raised the possibility that DNA supercoiling
may play a functional role in coupling stress signals to
transcriptional activity (Dorman 1996).
In this paper, we report the application of whole-genome microarrays
toward understanding the significance of supercoiling in the osmotic
stress response. Osmotic shock is among the most common environmental
challenges faced by bacterial organisms (Kempf and Bremer 1998; Wood
1999), and serves as an ideal model system because of the rapidity of
its effects. High osmolarity causes a rapid increase in negative
supercoiling, with subsequent relaxation to preinduction values as the
cell recovers. These supercoiling effects are accompanied by a distinct
sequence of cellular events: plasmolysis, followed by active potassium
influx and glutamate synthesis to restore intracellular water, and
finally, replacement of potassium glutamate with osmoprotectants more
compatible with cell growth. Artificially induced positive supercoiling
delays recovery and retards growth. Meanwhile, increased negative
supercoiling is reported to stimulate transcription of several
osmoregulated genes in vitro and in vivo, including the primary active
transporter for the osmoprotectants proline and glycine–betaine
(proU) (Higgins et al. 1988; Jordi and Higgins 2000) and the
lipoprotein osmE (Conter et al. 1997), as well as to repress
transcription of the outer-membrane  -barrel porin ompF(Graeme-Cook et al. 1989).
Here we demonstrate that increased negative supercoiling is necessary
for proper elicitation of the E. coli osmotic shock response.
Using a whole-genome approach (Lockhart et al. 1996; DeRisi et al.
1997; Richmond et al. 1999; Selinger et al. 2000), we have
characterized the genetic subprograms activated in response to osmotic
shock, clustered by expression profiles under salt stress and perturbed
with three antibiotics: novobiocin, pefloxacin, and chloramphenicol.
Statistical assignment of cellular function using the GenProtEC E.
coli database (Riley and Serres 2000) identifies enrichment for
several major functional categories and one subset of genes with
transcriptional kinetics consistent with supercoiling-dependent
regulation. This subset is composed of genes implicated in the
adaptation of E. coli to osmotic stress, including genes in
the major osmoprotectant synthesis/transport families, betaine
(betABIT), trehalose (otsAB), and proline
(proVWX). Statistical analyses of known transcription factor
binding sites support the involvement of rpoS, and
computational predictions implicate other global regulators including
crp and fis that are sensitive to supercoiling. We
propose a functional role for supercoiling in the osmotic stress
response, and suggest that supercoiling may have broader significance
in stress-induced transcription.
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RESULTS
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Microarray Studies of Genome-Wide E. coli Transcription in Response to Salt and a Panel of Microbial Antibiotics
We examined whole-genome mRNA expression in E. coli MG1655
(Blattner et al. 1997) following exposure to five different conditions:
novobiocin, high salt, novobiocin plus salt, pefloxacin, and
chloramphenicol (Table 1). Samples obtained
at 0 and 10 min after treatments with novobiocin or salt, at 0, 7, and
10 min with pefloxacin, and at 0, 10, and 40 min with chloramphenicol
were fluorescently labeled and hybridized to microarrays.
The microbial antibiotics novobiocin and pefloxacin were chosen for
their effects on supercoiling in the cell (Maxwell 1997). E.
coli topoisomerase II, also known as gyrase, is unusual among
topoisomerases in that it can introduce negative supercoils into DNA by
ATP hydrolysis (Luttinger 1995; Wang 1996; Champoux 2001). Novobiocin
binding decreases the affinity of gyrB (the subunit of
topoisomerase II) for the ATP nucleotide, which is required for DNA
breakage and strand passage (Gellert et al. 1976). Novobiocin therefore
causes increased positive supercoiling. The quinolone antibiotic
pefloxacin also targets gyrase, but possesses a different mechanism of
inhibition. Pefloxacin, in contrast to novobiocin, stabilizes the
transition state of gyrase after DNA breakage, leading to the formation
of a cleavable ternary complex (Drlica and Zhao 1997). This complex can
form a barrier, which on collision with replication forks, leads to
double-stranded breaks. Joint treatment with novobiocin and salt
produces an intermediate response (Conter et al. 1997). Chloramphenicol
blocks the 23S subunit of the ribosome and thus protein synthesis. This
antibiotic is not reported to have direct effects on supercoiling, but
we include it to facilitate in the discovery of coregulated genes.
Total RNA samples were enriched for mRNA, biotinylated, hybridized to
Affymetrix E. coli arrays, and scanned according to the
Affymetrix protocol. Genes below detection threshold as reported by
Affymetrix software were eliminated from consideration. For further
stringency, a z-test was used to eliminate probes near threshold as
defined by negative controls found on the microarrays. Using these
criteria, 2146 of 4249 mRNAs and untranslated RNAs were deemed above
detection with P values  0.01.
gyrB and topA Transcription Match Predicted Effects of Supercoiling
E. coli topoisomerases are regulated by supercoiling in a
negative feedback loop (Menzel and Gellert 1987). Positive supercoiling
stimulates gyrase expression, whereas negative supercoiling stimulates
topoisomerase I expression. Results shown in Figure 1A and B support
this model. gyrB mRNA expression
increases with novobiocin and pefloxacin treatments, but decreases with
salt. In contrast, expression of the topA gene (encoding
topoisomerase I) increases with salt, and is unchanged with novobiocin.
These results are consistent with the known effects of these treatment
conditions on DNA supercoiling (Fig. 1C).
Functional Characterization of the Osmotic Stress Regulon
Genes significantly induced or repressed by salt were identified by
changes in expression greater than twofold, and in the same direction
consistently between replicates. One hundred seventy-five genes
satisfied these criteria. We list the top 5 up-regulated and top 5
down-regulated genes in Table 2. In orderto
functionally characterize the osmotic stress response, we used a
two-stage approach. Genes were first clustered using self-organizing
maps. Six-cluster partitioning was chosen for optimal balance between
separation and number of clusters (Fig. 2).
Second, the resulting clusters were assigned cellular functions using
the GenProtEC: E. coli genome and protein database (Riley and
Labedan 1997; Riley and Serres 2000) (Table
3). Following the method of Tavazoie et
al. (1999), we estimated the probability that an observed set of genes
with a common functional role could have segregated into an
individual cluster at random (Sokal and Rohlf 1995). P values
below the Bonferroni-corrected significance threshold (P
value <0.01) are suggestive of functional enrichment (see Methods).
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Figure 2. The osmotic stress response partitions into distinct clusters of drug
sensitivity. Mean-variance normalized expression profiles for the 175
genes changed greater than twofold with salt treatment were partitioned
into six clusters using self-organizing maps. The black line denotes
the mean expression profile of the cluster, and the gray lines indicate
one standard deviation above and below this mean (N) Novobiocin; (S)
salt; (NS) novobiocin + salt; (P) pefloxacin; (C) chloramphenicol.
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As shown in Table 3, several major functional classes of genes govern
the osmotic stress response. These include adaptation to stress and
osmoregulation (cluster 0), peptidoglycan biosynthesis (cluster 1),
macromolecular biosynthesis (cluster 2), amino acid biosynthesis
(cluster 4), and the PTS Mannose-Fructose-Sorbose family (cluster 5). A
majority of the known osmoregulated genes as listed by GenProtEC are
found in cluster 0 (P value 5.4 x 10–4). These
results provide evidence that the transcriptional response to osmotic
shock greatly extends beyond what is currently known about
osmoregulated genes, and provides a promising set of candidates for
future experimental validation.
Potassium and Osmoprotectant Genes Segregate into Different Clusters
The immediate effect of hyperosmotic stress is plasmolysis, leading
to reduced respiration and growth arrest (Ingraham and Marr 1996).
Restoration of intracellular water by potassium influx and glutamate
synthesis occurs within minutes, but is only an interim solution
because high intracellular ion concentrations are also unfavorable
for cell growth (Record Jr. et al. 1998). Osmoprotectants, in contrast,
allow resumption of growth, but must be synthesized or brought into the
cell by active transport. Thus, two emergency systems, one fast and one
slow, are triggered by osmotic stress (Wood 1999). In support of this
model, our data demonstrate that Trk family and Kdp family potassium
transport genes (clusters 1 and 3) cocluster separately from
osmoprotectant genes (cluster 0). Major components for
synthesis/transport of osmoprotectants betaine (betABIT),
trehalose (otsAB and treF), and proline transport
(proVWX) are all located in cluster 0. In contrast,
trkH is located in cluster 1, whereas kdpABCD,
although outside the twofold threshold, exhibits expression profiles
similar to cluster 3 (r = 0.89–0.99). Likewise, the low
affinity proline transporter (proP, cluster 3) and components
of the murein oligopeptide transporter (oppA, cluster 3;
oppDEF, cluster 1) segregate outside of cluster 0.
proP is activated in medium hyperosmolarity and its synthesis
coincides with potassium influx. Glutamate is a necessary building
block in the synthesis of the peptidoglycan wall (Van Heijenoort 1996).
Up-regulation of murein oligopeptide transporters (Goodell and Higgins
1987) may therefore reflect coupling of peptidoglycan
degradation with potassium counter-ion synthesis.
Salt Stress Causes a Reduction in Metabolic Synthesis
Clusters 2, 4, and 5 also demonstrate the presence of a significant
down-regulated component in the osmotic stress response. These include
biosynthetic genes for amino acids (cluster 4), flagellar biosynthetic
genes (cluster 2), and genes encoding components for galactitol and
maltose transport (clusters 2 and 5, respectively). Maltose transport
has been previously linked to trehalose synthesis, and is repressed by
increased osmolarity in the absence of inducer (Boos and Shuman 1998).
Galactitol, however, has not been described in the context of osmotic
stress (Nobelmann and Lengeler 1996). Peptidoglycan-associated
lipoprotein, pal, and peptidoglycan synthetase, mrdA,
are also repressed by salt (cluster 2), as is trehalase 6-phosphate
hydrolase (treC), which degrades
intracellular trehalose. These findings are consistent with the
mirror-image up-regulation seen for murein oligopeptide uptake and
trehalose synthesis genes, respectively.
Cluster 0 is Enriched for Supercoiling-Dependent Transcription
If supercoiling regulates the expression of osmotic stress genes,
then we would predict that nonphysiological supercoiling would cripple
the osmotic stress response. Genes regulated by supercoiling should be
down-regulated in novobiocin, and pefloxacin, and up-regulated in salt,
or vice versa. Conversely, genes that fit this pattern are
candidates for regulation by a supercoiling-dependent mechanism.
Topoisomerase genes gyrB and topA (Fig. 1), for
example, have expression profiles satisfying this criterion. Using this
working model, we scored the 175 genes for their consistency with this
expression pattern (Fig. 3A). Scores were
based on 10 criteria or equivalently two replicates of five
constraints. Higher scores correspond to greater consistency with our
predicted model. For a given score, we calculated the number of genes
with at least this score or better for each cluster, and tested the
null hypothesis that this distribution could have occurred by chance.
These calculations demonstrate that at all score cutoffs, only cluster
0 is statistically enriched for supercoiling-dependent transcription
(Fig. 3B and Table 4). Given the functional
significance of cluster 0 in osmoprotectant synthesis and stress
adaptation (Table 3), we propose that negative supercoiling is a
physiologically necessary activation signal for osmotically induced
transcription.
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Figure 3. Cluster 0 is enriched for supercoiling-dependent gene regulation.
(A) Scoring function for supercoiling-dependent transcription.
A total of five tests with two replicates were incorporated into our
scoring function. Because genes could be up-regulated or down-regulated
in response to supercoiling, we took the better score assuming either
of these two cases. Thus, scores could range from 5–10. (B)
The 175 genes in the putative osmotic stress response were assayed for
their consistency with a supercoiling-dependent profile using the
scoring function described in A. The minimum score denotes the
lowest score or better that is included in the set of
supercoiling-regulated genes. (C) For each minimum score, a
hypergeometric test was used to gauge statistical enrichment over all
clusters. Only cluster 0 shows statistically significant enrichment.
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The Global Transcription Factor rpoS Is Implicated in the Regulation of Cluster 0
Cluster 0 expression (Fig. 2) is significantly lower in the absence
of salt stress, in support of a model in which supercoiling does not
act alone, but rather is a coactivator of gene expression. Several
lines of evidence indicate that rpoS, encoding the stationary
phase/stress-activated factor S, may be a partner in
this interaction (Hengge-Aronis 1996). First, rpoS is a member
of cluster 0, and by our assay, shows supercoiling-dependent regulation
(see Table 4). In addition, the rpoS transcript has a short
half-life that is dramatically stabilized by high osmolarity (Muffler
et al. 1996). Second, cluster 0 is enriched for stationary phase genes.
rpoS was initially characterized as a critical regulator of
the stationary phase response; rpoS mutants demonstrate both
reduced transcription of a number of stationary phase-activated genes
as well as decreased viability (Lange and Hengge-Aronis 1991).
Cross-referencing stationary and log phase data by Selinger et al.
(2000), we have found that, of 50 genes significantly increased in
stationary phase relative to log phase, 29 are found in cluster 0,
corresponding to a P value of enrichment of
1.2 x 10–4. This result indicates overlap between
stationary phase and osmotic stress responses, very likely through
rpoS. Third, a number of genes in cluster 0 have been shown to
be directly controlled by rpoS. Of 13 rpoS-regulated
genes described by Hengge-Aronis (1996) that are changed greater than
twofold in the salt condition, 11 are also located in this cluster
(P value = 1 x 10–4). Interestingly, the
lipoprotein nlpD, found in cluster 0, has been shown to be
part of an operon with rpoS (Lange and Hengge-Aronis 1994).
More recently, glutaredoxin 2 (grxB), also in this cluster,
has been shown to be rpoS-dependent (Potamitou et al. 2002).
We therefore consider it likely that the other genes in this cluster
are also regulated by a similar mechanism. Fourth, transcription by
S (rpoS) is enhanced by both high salt conditions
(Ohnuma et al. 2000) and by more positively supercoiled templates in in
vitro studies (Kusano et al. 1996). This indicates that rpoS
may be sensitive to supercoiling topology in vivo. These findings are
consistent with the hypothesis that the effects of supercoiling are
mediated through rpoS.
We have also calculated enrichment for predicted transcription factor
binding sites within upstream noncoding regions of genes in the osmotic
stress response (Table 5). Predicted
binding sites were generated computationally from a library of
DNA-binding site matrices built from known transcription factor binding
sites (Robinson et al. 1998; Roth et al. 1998). Eleven DNA-binding
motifs were enriched at P values below 0.05, with three motifs
corresponding to global regulators known to interact with supercoiling.
These include S (rpoS), Fis (fis), and
cyclic AMP receptor protein (crp). We were unable to resolve
motifs further to individual clusters by this method. Many of the
global transcription factors linked to supercoiling recognize
structural features in addition to and sometimes preferentially over
explicit sequence motifs. Structural recognition presents a challenge
to computational binding site prediction by sequence data alone. In
these cases, genome-wide in vivo protein–DNA cross-linking experiments
(genome-wide ChIP) will be expected to be particularly informative
(Lieb et al. 2001; Simon et al. 2001).
Expression Profiles of Temperature Stress Genes Indicate Supercoiling-Dependent Regulation
We observed that the heat shock gene, clpB, was among the
highest fold up-regulated with novobiocin treatment (2.7-fold), whereas
the cold shock gene, cspA, was among the highest fold
down-regulated (0.57-fold). It has been reported that transient
increases in positive supercoiling follow heat shock; conversely,
increased negative supercoiling is observed following cold shock (Ogata
et al. 1996; Tse-Dinh et al. 1997; Phadtare et al. 1999; Yura and
Nakahigashi 1999). To examine this further, we clustered known
temperature stress genes into two categories (see Fig.
4). Strikingly, cold shock genes
predominate in cluster 0, whereas heat shock genes predominate in
cluster 1. Among heat shock genes found in cluster 0, rpoE,
rseA, and htrA are all members of the E regulon
(Pallen and Wren 1997) involved in envelope protein stress, and
mseB is a suppressor of htrB, found in cluster 1.
Thus, at least four of six heat shock genes found in our putative cold
shock cluster (cluster 0) would not be expected a priori to cluster
with up-regulated heat shock genes. Similarly, the single cold shock
gene found in cluster 1 is gyrA. gyrA synthesis is
stimulated following cold stress. A short lag phase, however, preludes
this increased transcription, and, in fact, gyrA synthesis
actually decreases during this lag phase (Jones et al. 1992). These
results offer the possibility that supercoiling-driven regulation is a
general property of bacterial stress responses.
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DISCUSSION
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Here we demonstrate that increased negative supercoiling is
necessary for induction of a functionally significant set of genes
activated in the osmotic stress response. Through a series of
genome-wide expression profiling experiments under conditions of high
salt and perturbation with drug antibiotics, we have identified several
major categories of cellular function involved in the osmotic stress
response (Fig. 2, Table 3) including adaptation to stress and known
osmoregulation (cluster 0), peptidoglycan biosynthesis (cluster 1),
macromolecular biosynthesis (cluster 2), amino acid biosynthesis
(cluster 4), and the PTS Mannose-Fructose-Sorbose family (cluster 5).
Our choice of experimental conditions permitted identification of
supercoiling-dependent gene transcription. These supercoiling-dependent
transcription profiles are enriched in cluster 0 (Fig. 3B).
Significantly, cluster 0 constitutes 35% of the OSR (61/175), and is
enriched for genes involved in stress protection such as osmoprotectant
synthesis. We propose that increased negative supercoiling is critical
for recovery because it mediates the activation of an important
subcomponent of the osmotic stress response program.
Computational analysis of predicted DNA regulatory binding domains
found upstream of genes in the osmotic stress response also indicates
involvement of a number of global regulators known to interact with
supercoiling, including fis, crp, and rpoS
(Table 5). Comparison of known downstream targets with their
distribution among our cluster supports regulation of cluster 0 by
rpoS transcriptional activation. We consider it likely that
supercoiling exerts its effects through these transcription factors.
More complex modeling of the genetic network will be expedited by
algorithms that incorporate the combinatorial interactions of these
transcription factors, as well as binding site data derived from
genome-wide ChIP analysis, and in vitro binding specificity assays
(Bulyk et al. 2001; Pilpel et al. 2001; Simon et al. 2001).
It is an interesting question as to what causes negative supercoiling
to increase following osmotic shock. ATP/ADP ratios are
observed on treatment with salt (Hsieh et al. 1991). Because gyrase
activity is coupled to the phosphorylation potential of the cell (van
Workum et al. 1996), increased ATP concentration following salt stress
is hypothesized to stimulate gyrase activity. In support of this
mechanism, an increased ATP/ADP ratio is also observed following
nutritional upshift, with a similar increase in negative supercoiling
(Balke and Gralla 1987; Travers et al. 2001). Because long-term
survival is contingent on adaptation to a new environment,
transcriptional activity of the genes found in cluster 0 may be
hardwired for optimal expression at high levels of negative
supercoiling. We acknowledge that our osmotic shock experiments are
performed in a rough E. coli K12 strain. Future experiments
should include a comparison of our microarray data with that derived
from an E. coli strain with full-length lipopolysaccharide
(LPS) or alternatively, Salmonella typhimurium.
Interestingly, the his operon in S. typhimurium has
been shown to be induced by novobiocin and repressed by high
osmolarity. An analogous role for supercoiling may therefore exist in
this bacterium as well.
Our data provide a number of bioinformatics insights. These include a
genome-wide characterization of the osmotic shock response and
tentative assignments for 60 uncharacterized genes in this
transcriptional program. The 30 genes in cluster 0 with
supercoiling-dependent transcription (Table 4) should expand the number
of gene targets available for future study of this regulatory
mechanism. Gmuender et al. (2001) have previously examined the
genome-wide effects of novobiocin and ciprofloxacin (an antibiotic in
the same class as pefloxacin) in Haemophilus influenzae. The
microarray data described here allow comparative study of drug
inhibition in both H. influenzae and E. coli (Table
6), and should facilitate the discovery of
potential drug targets. For example, gmhA mutants show
increased outer membrane permeability from aberrant lipopolysaccharide
synthesis, leading to increased sensitivity to hydrophobic compounds
such as novobiocin (Brooke and Valvano 1996). As shown in Table 6,
however, both novobiocin and quinolone classes of antibiotics repress
gmhA synthesis. An interesting possibility is that the
efficacy of these drugs can be attributed in part to their ability to
increase membrane permeability. Finally, we present here a set of
computational tools to gauge statistical enrichment for cellular
function drawn from GenProtEC as well for transcription factor
binding sites. These technologies should help accelerate the
functional annotation of the genome. Both programs and datasets are
available on our Web site,
http://arep.med.harvard.edu/supercoiling/supplement.htm.
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Table 6. Common Effects of Novobiocin and Quinolone Antibiotic Treatment for
Both Escherichia coli and
Haemophilus influenzae
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We propose that transiently induced changes in supercoiling may be
relevant in environmental challenges beyond osmotic stress. Clustering
of temperature stress genes yields expression profiles consistent with
reported changes in supercoiling under heat shock and cold shock (Fig.
4). We have also found that oxyR transcription increases with
positive supercoiling. Hydrogen peroxide stress induces a transient
increase in positive supercoiling; oxyR mutants show delayed
resupercoiling and peroxide response (Weinstein-Fischer et al. 2000).
An intriguing question is whether bacteria will fare worse if
challenged with multiple stresses that require conflicting supercoiling
responses. Identification of pharmacological effects on supercoiling
may therefore aid in the design of drug combinations with synergistic
potency.
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METHODS
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Array Design
Using an array of oligos capable of specifically hybridizing to
their target sequence (RNA or DNA), an entire mRNA population can be
probed in parallel (Lockhart et al. 1996). The oligonucleotide array
used here is a 544 x 544 grid divided into 24 x 24-µm
regions (Affymetrix). Each region contains  107
copies of a 25-mer oligonucleotide probe. Photolithography and
combinatorial chemical methods are used to synthesize the
oligonucleotides directly on a derivatized glass plate. Probe
oligonucleotides are grouped into pairs consisting of a perfect match
(PM) probe and a mismatch (MM) probe. The PM probe is complementary to
the target sequence, whereas the MM probe contains a single base pair
mismatch. The MM oligo serves as a control used in identifying
cross-hybridization. Probe pairs are, in turn, grouped into probe sets
that correspond to mRNA transcripts.
Experimental Design
Ten conditions were assayed in our experiments (see Table 1) using
E. coli MG1655 (provided by Fred Blattner, University of
Wisconsin, Madison, WI). For novobiocin and salt treatments, E. coli
was grown to optical density (OD) 0.4 in M9 minimal media
supplemented with 0.4% glucose at 37°C, and aliquoted into two
separate flasks. In the first flask, prewarmed
novobiocin/dH2O was added for a final concentration of 300
µg/mL of novobiocin. In the second flask, prewarmed
NaCl/dH2O was added for a final osmolarity of 0.8. All
samples were spun down, flash-frozen in dry ice-ethanol, and stored at
–70°C in accordance to the procedures outlined by DeRisi et al.
(1997). For pefloxacin and chloramphenicol treatments,
E. coli MG1655 was grown in rich media to OD 0.6 at 37°C.
Pefloxacin was added to a final concentration of 1 µg/mL. The
chloramphenicol concentration was 0.32 µg/uL. Samples were taken
directly to phenol. All conditions are averages of two biological
replicates (i.e., replicates of separate independent experiments)
except for pefloxacin 1-min, pefloxacin 7-min, and chloramphenicol
10-min time points, which are generated from single experiments only.
Sample Preparation/Labeling/Measurement
RNA was isolated by phenol-chloroform extraction and prepared
following the Affymetrix protocol for E. coli arrays. This
protocol includes an mRNA enrichment step using RNase H enzyme
to reduce cross-hybridization from ribosomal RNA. Chips
were read using an HP-Affymetrix scanner, and quantified using
Affymetrix GeneChip 3.2 software. The expression of each probe set was
quantified based on the intensities of its PM and MM probes by a
composite statistic, the "Average Difference" intensity. The
Average Difference (AvgDiff) is calculated as the mean of all PM–MM
pairs after removal of outliers. The AvgDiff is a more representative
measure of target sequence concentration than PM intensities alone,
because it accounts for cross-hybridization. The resultant data were
then background subtracted and total intensity was normalized to 5000
using Affymetrix GeneChip version 3.2 software.
Normalization Specifications
We eliminated noisy gene expression data by a significance test
developed previously (Selinger et al. 2000). We assumed that (1)
intensities observed among 80 negative control probe sets on the chip
are representative of cross-hybridization and other forms of noise
only, and (2) noise is approximately normally distributed. For each
gene, we calculated its mean expression across all conditions (MEC). We
then estimated a mean MEC and standard deviation based on 80 negative
control probes on the array. For each gene on the chip, we performed
the following procedure. If the MEC was 2.33 standard deviations above
the mean MEC of our estimated noise distribution, we rejected the null
hypothesis that the observed gene expression was noise with a
P value 0.01. Genes that were below the 2.33 standard
deviation cutoff were called "absent" by Affymetrix GeneChip
software in greater than eight conditions, or that contained negative
values in any condition were eliminated from further analysis.
Functional Enrichment Analysis
Functional categories from the genProtEC database (Riley et al.
1997; Riley and Serres 2000) were downloaded from
http://genprotec.mbl.edu. The actual scoring algorithm is described as
follows. Given a file of clustered genes, the perl script catscore.pl
(CATegory Score) counts over all functional categories and clusters the
number of genes in a given cluster that are members of a particular
functional category. Tabulated data are subsequently analyzed using the
statistical test developed by Tavazoie et al (1999):
where P(k) = the cumulative probability of observing at
least k genes in a functional category within a given cluster,
f = the number of genes within the functional category,
n = the total number of genes, a = the number of
genes that match the functional category within the cluster, and
c = the number of genes inside the cluster.
We note that this test is mathematically equivalent to a one-tailed
Fisher's exact test for 2 x 2 contingency tables (Sokal and Rohlf
1995). With multiple clusters, we use the Bonferroni correction for
reducing experimental error rate. The procedure is simply:
where  is the desired error rate, ‘ is the necessary cutoff to
achieve this error rate, and m is the number of clusters. For
example, if = 0.05, and there are six clusters,
‘ = 1 – (1 – 0.05)1/5 = 0.01.
Computational Analysis of Motif Enrichment
Motifs were generated with the AlignACE program (Robinson et al.
1998; Roth et al. 1998), using known footprinting sites from the
DPInteract database. The highest scoring motif for each protein was
then scanned against the E. coli genome using the program
ScanACE. High-scoring sequences were defined to be two standard
deviations below the mean of the input binding sites or greater, and
constituting greater than 50% noncoding sequence. The enrichment for
high-scoring sequences was then assayed using catscore.pl.
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WEB SITE REFERENCES
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http://arep.med.harvard.edu/supercoiling/supplement.htm; complete
datasets and software as described in manuscript.
http://genprotec.mbl.edu; functional category assignments for theE. coli MG1655 genome.
www.arep.med.harvard.edu; resources and supplementary material for
publications from the Church laboratory.
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Acknowledgements
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We thank F. Ausubel and S. Lory for helpful comments in a
preliminary draft of this work, as well as members of the Church lab
for their generous comments and support. This research was supported by
grants from the DOE, ONR, and NSF to G.M.C.
The publication costs of this article were defrayed in part by payment
of page charges. This article must therefore be hereby marked
"advertisement" in accordance with 18 USC section 1734 solely to
indicate this fact.
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Footnotes
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1 Corresponding author. 
E-MAIL church{at}arep.med.harvard.edu; FAX (617) 432-7266.
Article and publication are at
http://www.genome.org/cgi/doi/10.1101/gr.401003. Article published online before print in January 2003.
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ABSTRACT
RESULTS
