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Labeled cDNA was generated from RNA samples by direct incorporation of
Cy3- or Cy5-labeled dUTP into cDNA. Differentially labeled samples from
two different conditions (strains) were mixed and hybridized to the DNA
microarrays, and each experiment was done at least three times (see
below). For labeling, RNA (10 to 50 μg) was incubated with 1 μg of
random hexamers and E. coli control RNA at 70°C for 10 min and then
placed on ice for 2 min. A labeling mix containing 2× reverse
transcription buffer (Life Technologies), 5 mM MgCl, 20 mM
dithiothreitol, deoxynucleoside triphosphates (1 mM dATP, 1 mM dGTP, 1
mM dCTP, and 0.4 mM dTTP), and either Cy3-dUTP or Cy5-dUTP
(Perkin-Elmer Life Sciences) was added to the RNA-primer mixture and
incubated at 25°C for 5 min. Superscript II reverse transcriptase (300
U) (Life Technologies) was added, and the mixture was incubated at 25°C
for 10 min and then at 42°C for 70 min. The reaction was stopped by
heating the reaction mixture to 70°C for 15 min. RNA was digested by
adding RNase A and RNase H and incubating the mixture at 37°C for 30
min. Unincorporated nucleotides were removed by using QiaQuick
purification spin columns (Qiagen) or DyeEx spin columns (Qiagen).
Labeled cDNA was dried and resuspended in hybridization buffer (25 mM
HEPES [pH 8.0], 1 mM EDTA, 0.8 μg of yeast tRNA/μl, 3× SSC, 0.2% sodium
dodecyl sulfate). Hybridizations were performed as described previously
(13). Slides were scanned on a GenePix 4000B scanner (Axon
Instruments, Inc.). E. coli control RNA corresponded to the genes ybaS,
yfiF, yciC, and ygjU. These genes were amplified by PCR from E. coli
with an upstream primer that contained a promoter recognized by T7 RNA
polymerase. RNA was made by in vitro transcription with T7 RNA
polymerase with the PCR products as template.
Data analysis. (i) Image analysis and normalization.
Images were processed and analyzed with GenePix 3.0 software (Axon
Instruments, Inc.). To be considered a valid signal, 40% of the pixels
in a spot had to be at least 1 standard deviation above the local
background in at least one of the channels. Spots not making this
cutoff were excluded from further analysis. Because many of the genes
in the analysis are expressed under only one condition, we had to
assign a value to the spots that did not contain a significant signal
in one channel. This value was the lowest signal in a channel that met
our criterion of being at least 1 standard deviation above background.
Background signal was not subtracted from the signal intensity of the
spots. Once spots with significant signals were identified, the two
channels were normalized by making the total signal in each of the
channels equal.
(ii) Determination of outliers.
Genes whose expression differed significantly between the two
conditions being compared were determined by two independent methods.
We analyzed our data by a method similar to the previously described
iterative outlier analysis (35). Each time point was the average of
at least three independent experiments (independently grown and
prepared samples). In at least one hybridization, the fluorophores were
swapped to help decrease bias introduced by the dyes. In cases where
multiple hybridizations were done from the same RNA sample, the data
were averaged and treated as a single value for the experiment. The
ratios from the independent samples were log transformed, and then
the data for each individual spot were averaged between the replicate
experiments. We then calculated the geometric mean and standard
deviation of the entire population. Any spot that had a ratio that was
more than 2.5 standard deviations away from the mean was considered an
outlier. Outliers were then removed from the population, and the means
and standard deviations were recalculated. Once again, any spot more
than 2.5 standard deviations away from the mean was considered an
outlier. This process was repeated until few or no outliers were
detected. In these experiments generally three iterations were needed
to identify all outliers in the population.
Array data were also analyzed with the Rosetta Resolver application
Axon error model (Rosetta Biosoftware). The lists of outliers from the
two analysis methods were compared, and only those genes that were
considered significantly changed in both were considered further. The
range of ratios of the outliers was from a high of 12 to a low of 1.6.
HMM analysis.
The sigma-H promoter sequence was modeled by using the HMMER 2.1.1
suite of software packages to create a series of closely related HMMs
(http://hmmer.wustl.edu). Known sigma-H promoter sequences from
citG, ftsAZ, kinA, sigA-P3, spo0A, spo0F, spoIIA, spoVG, spoVS,
ureaABC, dnaG/sigA, minC, and ytxG were manually aligned and used to
hand specify models with the HMMBUILD module. The various models
differed only in their accommodation of alternate direction of
transcription and variation in the size of the spacer region. In all
cases, a background nucleotide distribution consistent with that of B.
subtilis was assigned to portions of the promoter sequences
corresponding to the spacer region, while regions corresponding to the
-10 and -35 boxes were assigned to an HMM “match” state. To enable
detection of multiple occurrences of promoters within the genome, all
HMMs were required to match globally with respect to the model but
locally with respect to the B. subtilis genome. By using the HMMSEARCH
module, B. subtilis genome release 14.2 was searched with each HMM, and
the positions of hits were compiled and correlated with nearby reading
frames.

RESULTS AND DISCUSSION


Comparison of transcriptional profiles from sigH+ and sigH mutant
cells.
We found significant differences between the transcriptional profiles
(RNA levels) of sigH+ and sigH-null mutant cells. All strains in these
studies contained a mutation in sigF, which encodes a sigma factor that
is required for early-stage sporulation gene expression. By including a
sigF mutation in all of the strain backgrounds, we eliminated most of
the gene expression differences between sigH+ and sigH mutant cells
that are due to downstream sporulation differences between the two
strains. This allowed us to focus on changes in gene expression that
are associated with the time that sigma-H is most active, the
transition from exponential growth to stationary phase. sigH+ (RL1265)
and sigH mutant (PE170) strains were grown in sporulation medium, and
samples were taken during late exponential growth (approximately 1 h
before the onset of stationary phase), at the onset of stationary phase
(the end of exponential growth and the beginning of sporulation), and 1
h after entry into stationary phase. RNA was isolated from the samples,
labeled, and hybridized to DNA microarrays containing 4,074 of the
4,106 protein coding genes in the B. subtilis genome.
We found a total of 433 genes that had significantly different levels
of RNA from at least one time point in the sigH mutant compared to the
sigH+ strain. Of the genes altered, 245 were dependent on sigma-H for
expression (that is, they were more highly expressed in wild-type
cells) and 188 had higher expression levels in the sigH mutant.
Together, over 10% of the genes in the B. subtilis genome were altered
in the sigH mutant (discussed further below), demonstrating the
important role of sigma-H in cellular physiology. Graphs comparing the
relative abundance of RNA for each gene between the two strains are
shown in Fig. Fig.1A.1A. Points that fall on or near the line with
a slope of 1 are the majority of genes whose expression is not
significantly affected in the sigH-null mutant. Points that are