In Silico Prediction and Functional Validation of σ²⁸-Regulated Genes in Chlamydia and Escherichia coli

Abstract

σ²⁸ RNA polymerase is an alternative RNA polymerase that has been proposed to have a role in late developmental gene regulation in Chlamydia, but only a single target gene has been identified. To discover additional σ²⁸-dependent genes in the Chlamydia trachomatis genome, we applied bioinformatic methods using a probability weight matrix based on known σ²⁸ promoters in other bacteria and a second matrix based on a functional analysis of the σ²⁸ promoter. We tested 16 candidate σ²⁸ promoters predicted with these algorithms and found that 5 were active in a chlamydial σ²⁸ in vitro transcription assay. hctB, the known σ²⁸-regulated gene, is only expressed late in the chlamydial developmental cycle only, and two of the newly identified σ²⁸ target genes (tsp and tlyC_1) also have late expression profiles, providing support for σ²⁸ as a regulator of late gene expression. One of the other novel σ²⁸-regulated genes is dnaK, a known heat shock-responsive gene, suggesting that σ²⁸ RNA polymerase may be involved in the response to cellular stress. Our σ²⁸ prediction algorithm can be applied to other bacteria, and by performing a similar analysis on the Escherichia coli genome, we have predicted and functionally identified five previously unknown σ²⁸-regulated genes in E. coli.

Genome sequencing has indicated that all Chlamydia species encode two alternative sigma factors, suggesting a role for alternative forms of RNA polymerase in chlamydial gene regulation. We have demonstrated that one of these alternative RNA polymerases, σ²⁸ RNA polymerase, transcribes hctB (24), a gene whose transcript is detectable only at late time points in the chlamydial developmental cycle (6, 16). hctB encodes Hc2, one of two histone-like proteins in Chlamydia that have been shown to be responsible for the condensation of DNA during conversion of the metabolically active form of chlamydiae, known as a reticulate body, to the infectious extracellular form, the elementary body (8). To date, hctB is the only σ²⁸-regulated gene that has been identified in Chlamydia, and it is not known whether the role of σ²⁸ RNA polymerase is confined to the regulation of late gene expression in the developmental cycle.

To identify additional σ²⁸-regulated genes in Chlamydia, we have combined the use of bioinformatics, to predict σ²⁸-regulated promoters in the chlamydial genome, with testing of promoter activity in a chlamydial σ²⁸ in vitro transcription assay. We used two in silico approaches, identifying candidate promoters on the basis of sequences that either resemble the consensus bacterial σ²⁸ promoter (9, 10) or are predicted to be highly transcribed by σ²⁸ RNA polymerase based on functional studies (25). Using information from both approaches, we have a developed a computer algorithm to identify candidate σ²⁸ promoters in the chlamydial genome and have shown that five promoters are transcribed by chlamydial σ²⁸ RNA polymerase. This method can be applied to other bacterial genomes, and we have also identified five new σ²⁸-regulated genes in Escherichia coli.

MATERIALS AND METHODS

Development of a program for extracting sequences.

We developed a program called SequenceExtractor to extract user-defined DNA sequences from a genome. The program requires two input files, consisting of a genome sequence file and a file containing the start and stop coordinates for each gene within the DNA sequence being examined. We applied this program to extract two files in fasta format from each of the genomes of C. trachomatis serovar D, E. coli K-12, and Salmonella enterica serovar Typhimurium using sequences obtained from TIGR (http://www.tigr.org). For each organism, the first output file contained 200 bp of sequence upstream for each gene (“200 bp upstream”). The second output file was more restrictive and contained up to 200 bp of upstream sequence for each gene, provided that these sequences were in the intergenic region and not within the coding region of the nearest upstream gene (“200-bp nonoverlap”).

Development of a program for predicting promoters.

We also developed a program, called PromoterMatcher, that uses a probability weight matrix to predict promoters in a genome. We generated two probability weight matrices, each based on complementary information about σ²⁸ promoter structure, and applied them to an input file consisting of extracted sequences in fasta format. The frequency matrix was based on a set of 21 known bacterial σ²⁸ promoters and takes into account the frequency of occurrence of each nucleotide at each position within this promoter set. The activity matrix used functional data in the form of the relative promoter strength attributable to each nucleotide at each promoter position and was derived from a comprehensive mutational analysis of a σ²⁸ promoter (25). For each promoter position, the algorithm assigned a probability value to the four possible nucleotides, with a total probability of 1. Both matrices also contained probability-weighted models for the length of the spacer between the two promoter elements based on either nucleotide frequency or relative promoter activity. The final score for each candidate promoter was determined by summing the log of the probability at each position (which is the mathematical equivalent of multiplying the probabilities). Only the highest-scoring promoter was recorded per upstream region. The predicted promoters were sorted by score, from best to worst.

Generation of the sequence logo.

All sequence logos were derived using SEQLOGO, which is available online at http://ep.ebi.ac.uk/EP/SEQLOGO/. The format for data input into this site is a series of numbers representing either nucleotide frequency or relative promoter activity. The resulting sequence logo consists of stacks of letters at each position. The height of the stack indicates the importance of a particular position for promoter activity. The height of an individual letter within a stack indicates the relative preference for that nucleotide based on transcriptional activity or frequency (with a maximum height defined as 2 bits).

Cloning of transcription plasmids.

Each candidate σ²⁸-regulated promoter to be tested was cloned into a plasmid so that promoter activity could be measured with a σ²⁸ in vitro transcription assay. The promoter insert, consisting of sequence from approximately 300 bp upstream of the transcription start site to the +5 position, was amplified by PCR using either C. trachomatis serovar D or E. coli K-12 genomic DNA and respective primers (see Table S1 in supplemental material). This PCR insert was cloned upstream of a synthetic G-less cassette transcription template in plasmid pMT1125 (23). Transcription from the predicted promoter by σ²⁸ RNA polymerase was expected to produce a 130-nt transcript.

Overexpression and purification of σ²⁸.

C. trachomatis serovar L2 His₆-σ²⁸ and E. coli His₆-σ²⁸ were individually overexpressed in E. coli BL21(DE3) and purified, as previously described (24, 25), to a concentration of 35.7 μg/ml and 115.8 μg/ml, respectively.

In vitro transcription reactions.

Transcription reactions were performed as previously described (24, 25). C. trachomatis σ²⁸ RNA polymerase was reconstituted by mixing 1 μl C. trachomatis recombinant His₆-σ²⁸ with 1 μl heparin-agarose-purified C. trachomatis RNA polymerase at 4°C for 15 min, immediately prior to the transcription reaction. E. coli σ²⁸ RNA polymerase was reconstituted from 1 μl E. coli recombinant His₆-σ²⁸ and 0.03 units E. coli core enzyme (Epicenter, Madison, Wis.). For antibody inhibition reactions, 8 μg of rabbit polyclonal antichlamydial σ²⁸ antibodies (24) was preincubated with the RNA polymerase for 20 min at room temperature prior to transcription.

Purification of C. trachomatis RNA polymerase from chlamydiae grown in tissue culture.

C. trachomatis serovar L2 was grown in mouse L929 cells and harvested at 18 h postinfection (hpi). RNA polymerase was partially purified by heparin-agarose chromatography as previously described (21).

Purification of reticulate body RNA.

L929 cells grown in suspension and infected with C. trachomatis serovar D were recovered by centrifugation and lysed by Dounce homogenization as previously described, with slight modifications (21). A second centrifugation step separated chlamydiae from host cellular debris. Chlamydial RNA was extracted using RNA STAT-60 (Teltest, Inc., Friendswood, TX).

Mapping transcription start sites by primer extension.

The primer was prepared from 100 ng of a DNA oligonucleotide that was labeled with 50 μCi of [γ-³²P]ATP in the presence of T4 polynucleotide kinase at 37°C for 1 h. Unincorporated free nucleotides were removed with a DNA mini-Quick Spin DNA column (Roche Diagnostics, Indianapolis, Ind.). Radioactive samples were counted with a scintillation counter. Fifty micrograms of reticulate body RNA and 5 × 10⁶ cpm labeled primer were preheated at 65°C for 10 min and chilled on ice. cDNA was synthesized by adding Superscript II reverse transcriptase (Invitrogen, Carlsbad, Calif.) and 10 mM deoxynucleoside triphosphates, followed by incubation at 42°C for 50 min. The reaction was stopped by the addition of distilled water and a 1/10 volume of 3 M sodium acetate to a total volume of 100 μl, followed by phenol-chloroform extraction and chloroform extraction. cDNA was recovered by ethanol precipitation, dried, and resuspended in 9 μl formamide stop solution (95% formamide, 20 mM EDTA, 0.05% bromophenol blue, 0.05% xylene cyanol). The primer extension products were electrophoresed on a 6% acrylamide-urea sequencing gel together with a single-stranded M13mp18 DNA sequence ladder and exposed to X-ray film.

RESULTS

Development of computer algorithms to identify σ²⁸ promoters.

We developed two computer algorithms, which we used in parallel to identify candidate σ²⁸ promoters within a genome (Fig. 1). The first program, called SequenceExtractor, selects sequences from a genome for analysis by the second program, PromoterMatcher, which makes predictions on the basis of the σ²⁸ promoter structure and sequence. We used the structure of the extended bacterial σ²⁸ promoter (12) with eight positions in the −35 element and another eight positions in the −10 element separated by a spacer of variable length.

FIG. 1.

Flow chart showing the use of computer algorithms for promoter prediction. The SequenceExtractor algorithm was used to extract two sequence files for each open reading frame (ORF) that were then analyzed with a promoter prediction algorithm (PromoterMatcher) using either of two probability weight matrices (see the text). In total, this scheme produced four lists of predicted promoters ranked by scores. Details are provided in Materials and Methods and Results.

We focused our search on the intergenic region upstream of each predicted gene where promoters were most likely to be present. SequenceExtractor was used to select sequences up to 200 bp upstream of each gene, provided that they were not in a coding region (200-bp nonoverlap region). In Chlamydia, however, many intergenic regions are short, and promoters have been located within the upstream gene (13). Thus, we also separately examined all sequences in the region 200 bp upstream of each gene even if they were beyond the intergenic region (200-bp upstream region).

To identify candidate σ²⁸ promoters within these upstream sequences, we used the PromoterMatcher algorithm to apply a weighted matrix and assign probability scores for the 16 promoter positions and the spacer length. To increase the likelihood of identifying σ²⁸ promoters, we used two weighted matrices, each based on a different measure of the contribution of sequence to promoter activity. The first matrix, called the frequency matrix, was based on the occurrence of a given nucleotide at each promoter position within a compilation of 21 known σ²⁸ promoters, including 20 promoters from E. coli and Salmonella, and the C. trachomatis hctB promoter. For example, at the seventh position in the −10 element (Fig. 2A), an A was present in 19 of the 21 promoters, and the remaining two promoters had a G at this position. Accordingly, an A was given a strong weighting of 19/21, while the weighting for a G was 2/21. As all known σ²⁸ promoters, with the exception of the chlamydial promoter, have a spacer of 11 nt, this spacer length was also heavily weighted.

FIG. 2.

Probability weight matrices and sequence logos for predicting promoters in the chlamydial genome. (A) Frequency matrix for the 16 positions in the −35 and −10 promoter elements and the four possible nucleotides at each position (see Materials and Methods). Each value in the matrix indicates how many of the 21 known σ²⁸ promoters in the training set contained the given nucleotide at that promoter position. A sequence logo depicting the relative nucleotide preference at each position in the promoter is shown below the matrix (25). (B) Activity matrix for the −35 and −10 promoter elements with values derived from a mutational analysis of the C. trachomatis hctB promoter as described in the text (25). At each promoter position, the values are proportional to the relative promoter activity attributable to that nucleotide for a total of 100%. The sequence logo for the promoter is shown below the matrix. Details of the sequence logo format are presented in Materials and Methods and Results. All sequence logos were derived using SEQLOGO, which is available online at http://ep.ebi.ac.uk/EP/SEQLOGO/.

A second weighted matrix, called the activity matrix, assigned a weighting to each of the four possible nucleotides for every position based on the promoter activity attributed to that nucleotide in a mutational analysis of the hctB promoter (25). For example, at the seventh position of the −10 element, hctB promoter activity with C. trachomatis σ²⁸ RNA polymerase was greatest when an A was present and was reduced by 2.6-, 3.4-, and 19.3-fold with a G, T, or C, respectively (25). We thus assigned probability scores for A (58/100), G (22/100), T (17/100), or C (3/100) that were proportional to these promoter activities (Fig. 2B). The probability weighting for the spacer length was based on the measured effect of a spacer length from 9 to 13 nt on hctB promoter activity (25).

By applying these two weighted matrices to the two sets of upstream sequences, we generated four lists of candidate σ²⁸ promoters using PromoterMatcher. hctB, the known C. trachomatis σ²⁸-regulated promoter, was the highest-scoring promoter sequence in all four lists. The top 30 predictions for each list are shown in Table S2 (frequency matrix), and Table S3 (activity matrix) in the supplemental material.

Five candidate chlamydial promoters were transcribed by σ²⁸ RNA polymerase.

We chose 16 of the top candidate promoters (Table 1) for functional testing with our chlamydial σ²⁸ in vitro transcription assay. In general, these promoters were among the top-50-scoring promoters in at least two of the four prediction lists. Since the source of our core enzyme contains chlamydial σ⁶⁶ RNA polymerase activity (24), we tested for transcription in the absence and presence of recombinant chlamydial σ²⁸ as a measure of σ⁶⁶-specific and σ²⁸-specific activity, respectively. We also assayed for σ²⁸-dependent activity by testing for inhibition of transcription by anti-σ²⁸antibodies.

TABLE 1.

Predicted chlamydial σ²⁸ promoters tested for in vitro activity

Gene ID	Gene name	Function	Location from ORF (nt)	−35 sequence	Spacer length (nt)	−10 sequence	Rankingsa	σ28 activity
CT047		Possible outer membrane protein I	−96	TTTTGTAT	11	GTCGAAAT	36, *, 51, 24	No
CT099	trxB	NADPH thioredoxin reductase	−104	TTAGTTTT	11	GTCGAAAC	*, 32, 6, 3	No
CT181		Hypothetical protein	−193	TAATGTAT	12	CACGAATG	*, *, 17, 8	No
CT249		Hypothetical protein	−188	TCAACATT	12	ATCGAAAT	*, *, 9, 42	No
CT256	tlyC_1	Hypothetical protein, possible hemolysin	−104	TACAGTTG	11	GCCGAAGA	4, 3, 45, 20	Yes
CT359	bioY	Probable biotin synthase	−48	TAAAGGCC	12	GTCGATTC	*, *, 8, 4	Yes
CT396	dnaK	Dnak protein (Hsp70)	−63	TAAAGGAA	11	AACGAAGA	28, *, 27, *	Yes
CT415	yebL	ABC transporter	−89	TAAAGAAT	11	GAAGACAA	14, 9, 14, 6	Weak
CT418	yhbZ	GTP-binding protein	−196	TAAACGTT	12	GACGATAC	10, *, 4, *	Weak
CT425		Hypothetical protein	−42	TAAAGGAC	10	CTCGAACT	*, *, 54, 28	Weak
CT441	tsp	Probable tail-specific protease	−61	TCAAGTTT	11	GTCGAAGA	3, 2, 2, 2	Yes
CT553	fmu	FMU/SUN-related methyltransferase	−37	TAAAGAGC	13	CTCGAAGG	*, *, 23, 12	No
CT631		Hypothetical protein	−41	TCTGCTTT	12	ATCGAAAA	*, *, 41, 18	No
CT693	pgk	Phosphoglycerate kinase	−56	TGAGTTTT	11	GCCTATAA	13, 8, *, *	Yes
CT789		Hypothetical protein	−45	TTAATATT	11	AACGAAAG	21, 12, 21, 10	No
CT798	glgA	Glycogen synthase	−41	TTAATGAG	12	AACGAAAC	*, *, 28, 13	No

^aThe rankings for each of four promoter prediction lists are shown in the following order: 200-bp upstream frequency matrix, 200-bp nonoverlap frequency matrix, 200-bp upstream activity matrix, and 200-bp nonoverlap activity matrix. *, ranking is not in the top 50.

Five of the 16 candidate promoters tested showed σ²⁸-specific activity (Fig. 3), and an additional three promoters (yebL, yhbZ, and CT425) were weakly transcribed (data not shown). Four of the strongly transcribed promoters (tsp, dnaK, tlyC_1, and bioY) produced a transcript only when recombinant chlamydial σ²⁸ was added, as was the case with the hctB positive control promoter. Transcription of these four promoters was also abrogated by rabbit polyclonal anti-σ²⁸ antibodies (Fig. 3, lane 3). The results were less clear-cut with the pgk promoter, because although there was a large increase in transcription when σ²⁸ was added, there was baseline transcription in the absence of σ²⁸, raising the possibility of some σ⁶⁶-dependent activity. Anti-σ²⁸ antibodies decreased transcription of the pgk promoter, but there was still residual transcript present. These results provide evidence that the promoters for tsp, dnaK, tlyC_1, and bioY are transcribed by σ²⁸ RNA polymerase and suggest that the pgk promoter is recognized by both σ²⁸ and σ⁶⁶ RNA polymerases.

FIG. 3.

In vitro transcription of predicted C. trachomatis σ²⁸-dependent promoters using chlamydial σ²⁸ RNA polymerase. Lane 1, transcription by a C. trachomatis core enzyme preparation containing σ⁶⁶ transcriptional activity but no σ²⁸ activity; lane 2, addition of recombinant chlamydial σ²⁸ to the core enzyme; lane 3, transcription by core enzyme plus recombinant chlamydial σ²⁸ in the presence of anti-chlamydial σ²⁸ antibodies (Ab).

For in vivo validation of these results, we used primer extension to map the transcription start sites for the three strongest promoters, hctB, tsp, and pgk, to within 6 nt of the predicted σ²⁸ −10 promoter element, at a location consistent with the predicted promoter (Fig. 4). A previously mapped transcription start site for dnaK (5) was located within 8 nt of the σ²⁸ promoter that we have predicted for this gene.

FIG. 4.

Mapped transcription start sites. The sequence immediately upstream of the ATG start codon (underlined) is shown for each C. trachomatis gene. Transcription start sites mapped by primer extension are shown in capital letters. Predicted σ²⁸ promoters are underlined and in boldface type. The sequences for hctB, tsp, and pgk are from C. trachomatis serovar D. The sequence for dnaK is from the C. trachomatis strain MoPn (C. muridarum), and this transcription start site was mapped previously by Engel et al. (5).

pgk is regulated by two overlapping promoters.

Analysis of the sequence in the pgk promoter revealed a possible σ⁶⁶ promoter overlapping the predicted σ²⁸ promoter. To confirm the presence of an active σ²⁸ promoter, without the confounding effect of a second promoter, we introduced substitutions predicted to disrupt the putative σ⁶⁶ promoter but not the σ²⁸ promoter (Fig. 5A). With this mutant promoter, there was no baseline σ⁶⁶-dependent activity, and all transcription was dependent on the addition of chlamydial σ²⁸ (Fig. 5B, lanes 1 and 2). Transcription was specifically inhibited by anti-σ²⁸ antibodies (Fig. 5B, lane 3). These results provide good experimental support for the predicted σ²⁸-dependent pgk promoter and an overlapping σ⁶⁶ promoter.

FIG. 5.

Transcription of a mutated pgk promoter. (A) The sequence upstream of the C. trachomatis pgk promoter is shown with the predicted σ²⁸ promoter in boldface type, and the mapped transcription start sites are in boldface capital letters. For reference, the consensus bacterial σ²⁸ promoter (10, 12) is shown above the sequence, and the chlamydial σ⁶⁶ promoter (17, 22) is shown below the sequence. The bottom sequence shows a mutant pgk promoter with two nucleotide changes in the −35 element and one nucleotide substitution in the −10 element (changes are underlined). wt, wild type. (B) In vitro transcription of the mutant pgk promoter by chlamydial σ²⁸ RNA polymerase. Lane 1, C. trachomatis core enzyme preparation alone; lane 2, core enzyme with recombinant chlamydial σ²⁸ protein; lane 3, transcription of the mutant pgk promoter by core enzyme plus recombinant chlamydial σ²⁸ in the presence of anti-chlamydial σ²⁸ antibodies (Ab).

Five predicted E. coli σ²⁸ promoters were transcriptionally active.

As we also had functional data for promoter recognition by E. coli σ²⁸ RNA polymerase (25), we were able to apply our promoter-finding algorithm to the genomes of E. coli and the closely related bacterium Salmonella. Lists of the top 30 candidate promoters are shown in Tables S4 and S5 for E. coli and Tables S6 and S7 for Salmonella (see the supplemental material). Many of these promoters are known σ²⁸ promoters in E. coli and Salmonella. We tested seven candidate σ²⁸ E. coli promoters that had not been previously studied (Table 2) and found that five (modA, ynjH, yecF, yhiL, and yjcS) were functionally active in an E. coli in vitro σ²⁸ transcription assay (Fig. 6).

TABLE 2.

Predicted E. coli σ²⁸ promoters tested for in vitro activity

Gene ID	Gene name	Function	Location from ORF (nt)	−35 sequence	Spacer length (nt)	−10 sequence	Rankingsa	σ28 activity
b0132	yadD	Hypothetical protein	−82	TAAACAAT	12	GACGATAG	*, 25, 8, 6	No
b0763	modA	Molybdate ABC transporter	−129	TCAACTTC	11	GCCGATAT	10, 9, 29, 18	Yes
b1760	ynjH	NADPH thioredoxin reductase, predicted protein	−62	TAAAGTTT	12	ACCGTTAT	*, *, 13, 9	Yes
b1915	yecF	Hypothetical protein	−134	TAACGTAT	11	GCCGATAT	20, 14, 11, 7	Yes
b3204	ptsN	Enzyme IIANtr	−63	TAAACAAC	12	ACCGATAA	29, 19, 24, 15	No
b3490	yhiL	Hypothetical protein	−118	TCAAGAAT	11	GCCGACAA	11, 10, 12, 8	Yes
b4083	yjcS	Hypothetical protein	−190	TAAAGTTT	11	GACGATTA	16, 12, 5, 5	Yes

^aThe rankings for each of four promoter prediction lists are shown in the following order: 200-bp upstream frequency matrix, 200-bp nonoverlap frequency matrix, 200-bp upstream activity matrix, and 200-bp nonoverlap activity matrix. *, ranking is not in the top 30.

FIG. 6.

In vitro transcription of predicted E. coli σ²⁸-dependent promoters. Promoters were transcribed with E. coli σ²⁸ RNA polymerase reconstituted from E. coli core enzyme and recombinant E. coli σ²⁸ as described in the text.

DISCUSSION

This study demonstrates how a combination of a bioinformatic analysis and functional validation can be used to identify previously unrecognized target genes of an alternative RNA polymerase. From a genome-wide search for sequences resembling known σ²⁸ promoters and sequences that have been shown to be highly transcribed by σ²⁸ RNA polymerase, we identified five novel σ²⁸-regulated genes in Chlamydia and another five new σ²⁸-regulated genes in E. coli. Although we did not test any of the predicted Salmonella σ²⁸ promoters, our list of top-scoring promoters includes three (STM3152, STM3216, and STM2314) of four newly identified σ²⁸ target genes in S. enterica serovar Typhimurium (7). These results demonstrate that our promoter prediction algorithm can successfully identify σ²⁸ promoters, and it is likely that additional σ²⁸-regulated genes remain to be discovered in many bacterial genomes.

Our results show that the combination of a frequency matrix, derived from known σ²⁸ promoter sequences, and an activity matrix, based on a mutational analysis of a chlamydial σ²⁸-dependent promoter, increased our chances of identifying additional σ²⁸ promoters. hctB and tsp had the two strongest promoters in terms of transcriptional activity and sequence conservation with the bacterial σ²⁸ consensus promoter, and both ranked equally high with the two matrices (see Tables S2 and S3 in the supplemental material). For promoters with weaker sequence conservation, such as bioY, the activity matrix may be a better predictor. For instance, bioY ranked in the top 10 using the activity matrix (Table 1) but was not in the top 50 with the frequency matrix.

In general, we found that a strict pattern-matching algorithm based only on the bacterial σ²⁸ consensus sequence would not be very sensitive as a means of identifying σ²⁸-dependent promoters in Chlamydia. With the exception of the hctB promoter, the other chlamydial σ²⁸ promoters identified in this study are not well conserved with the bacterial consensus promoter. For example, while the dnaK promoter (TAAAGGAA-N11-AACGAAGA) contains the signature TAAA of the −35 promoter element, the −10 promoter element has only a 4/8 match with the consensus sequence. The CGA motif was the only recognizable sequence in the dnaK −10 promoter element, highlighting the importance of this motif for σ²⁸ promoter activity (25). In all, the CGA motif of the −10 element was present in five of the six transcriptionally active chlamydial σ²⁸ promoters.

In Chlamydia, two of the five newly identified σ²⁸-regulated genes have been shown to be expressed late in the developmental cycle in a fashion similar to that of the original σ²⁸ target gene, hctB. Transcripts for hctB, tsp, and tlyC_1 were each first observed at 16 hpi by microarray expression analysis (3). These late expression profiles support a role for σ²⁸ RNA polymerase in late gene expression in Chlamydia. In contrast, mRNA from pgk and bioY were detectable earlier, at 8 hpi, while the dnaK transcript was present by 3 hpi (3). It is worth noting, however, that this microarray analysis measures only steady-state transcript levels and would not be able to distinguish between the temporal activity of multiple promoters, such as transcription of pgk by both σ²⁸ and σ⁶⁶ RNA polymerases. Thus, it is entirely possible that σ²⁸-regulated expression of these target genes may also be restricted to late time points, and as yet, there is no definitive evidence that σ²⁸ RNA polymerase is transcriptionally active at earlier times in the chlamydial developmental cycle. In summary, there is accumulating evidence for σ²⁸-dependent regulation of a subset of late genes in Chlamydia, distinct from the late genes transcribed by σ⁶⁶ RNA polymerase (6).

pgk and dnaK are the first examples of genes in Chlamydia that can be transcribed by more than one form of RNA polymerase. With pgk, the promoters for σ²⁸ RNA polymerase and σ⁶⁶ RNA polymerase overlap and appear to have the same transcription start site (Fig. 5 and 7A), which raises the question of how promoter occupancy by the two forms of RNA polymerase is regulated. dnaK is known to be transcribed as part of the hrcA-grpE-dnaK operon by σ⁶⁶ RNA polymerase (21) under the control of the HrcA repressor (23). We now show that dnaK has an independent promoter that is transcribed by σ²⁸ RNA polymerase (Fig. 4 and 7B), and we predict that this promoter is responsive to heat shock. We base this prediction on the observation that elevated temperatures have been shown to upregulate levels of the dnaK transcript by greater than 10-fold, while hrcA and grpE mRNA levels were not similarly increased (5).

FIG. 7.

Diagram of the C. trachomatis pgk gene and hrcA-grpE-dnaK operon. Arrows indicate the approximate locations of transcription start sites. (A) Upstream of the pgk gene are two overlapping promoters recognized by σ⁶⁶ and σ²⁸ RNA polymerases, respectively. (B) The σ⁶⁶ promoter for the hrcA-grpE-dnaK operon is located upstream of hrcA, while a σ²⁸ promoter is located upstream of dnaK within the coding region of grpE. The figure is not drawn to scale.

While we have identified a total of six σ²⁸-regulated genes in Chlamydia, it is not clear whether these genes belong to a specific functional group. hctB encodes Hc2, a histone-like protein that causes DNA condensation (1, 2, 4, 14, 15). Tsp is a predicted protease with similarity to CPAF, a secreted chlamydial protease that cleaves host transcription factors involved in major histocompatibility complex class I and class II antigen expression (18, 26). tlyC_1 encodes a hypothetical protein, which may be involved in hemolysis (20). Of the remaining three target genes, dnaK encodes a heat shock chaperone, pgk encodes a phosphoglycerate kinase, and bioY encodes a hypothetical protein with homology to a predicted biotin synthase in Bacillus subtilis and Treponema pallidum. Thus, unlike other bacteria, where σ²⁸ RNA polymerase regulates particular classes of genes involved in chemotaxis, motility, and flagellum synthesis, it is not clear how these σ²⁸ target genes in Chlamydia are related.

Our studies support a role for σ²⁸ as a developmental regulator of late gene expression in Chlamydia, but little is known about how σ²⁸ activity is itself regulated. Although Chlamydia encodes a predicted anti-sigma factor, RsbW, as part of a partner-switching mechanism, doubts have been raised about its ability to regulate σ²⁸ (11). The discovery that one of the target genes of σ²⁸, dnaK, is a known heat shock gene is intriguing and may provide clues about the signal for σ²⁸-dependent transcription. Perhaps σ²⁸ RNA polymerase is involved in the general stress response in Chlamydia, as supported by the finding that σ²⁸ transcript levels were increased under conditions of heat shock (19). By extension, σ²⁸-regulated transcription late in the developmental cycle may be triggered in response to cellular stress, such as nutrient deprivation or other conditions within the chlamydial inclusion, although the details remain to be elucidated.

Our promoter search algorithm is versatile and can be applied to predict σ²⁸ promoters in other bacteria or promoters for other forms of RNA polymerase. σ²⁸ promoter recognition appears to be conserved among bacteria (25), and thus, our existing frequency- and activity-weighted matrices can be readily used for other prokaryotic genomes. With the appropriate probability weight matrix, the algorithm can also be used to identify promoters recognized by different forms of RNA polymerase. More generally, this same algorithm could be applied to any DNA sequence, such as a protein-binding site, as long as examples are available to build the weighted matrix. As our results have shown, however, an essential component of this bioinformatic approach is the validation of the in silico predictions with functional testing.