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In Silico Prediction and Functional Validation of ²⁸-Regulated Genes in Chlamydia and Escherichia coli ^,

Institute for Genomics and Bioinformatics,¹ Department of Microbiology and Molecular Genetics,² Department of Medicine, School of Medicine,³ Department of Computer Science, Donald Bren School of Information and Computer Science, University of California, Irvine, California 92697-4025⁴

Received 21 July 2006/ Accepted 6 September 2006

	ABSTRACT

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²⁸ 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 ²⁸-dependent genes in the Chlamydia trachomatis genome, we applied bioinformatic methods using a probability weight matrix based on known ²⁸ promoters in other bacteria and a second matrix based on a functional analysis of the ²⁸ promoter. We tested 16 candidate ²⁸ promoters predicted with these algorithms and found that 5 were active in a chlamydial ²⁸ in vitro transcription assay. hctB, the known ²⁸-regulated gene, is only expressed late in the chlamydial developmental cycle only, and two of the newly identified ²⁸ target genes (tsp and tlyC_1) also have late expression profiles, providing support for ²⁸ as a regulator of late gene expression. One of the other novel ²⁸-regulated genes is dnaK, a known heat shock-responsive gene, suggesting that ²⁸ RNA polymerase may be involved in the response to cellular stress. Our ²⁸ 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 ²⁸-regulated genes in E. coli.

	INTRODUCTION

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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, ²⁸ 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 ²⁸-regulated gene that has been identified in Chlamydia, and it is not known whether the role of ²⁸ RNA polymerase is confined to the regulation of late gene expression in the developmental cycle.

To identify additional ²⁸-regulated genes in Chlamydia, we have combined the use of bioinformatics, to predict ²⁸-regulated promoters in the chlamydial genome, with testing of promoter activity in a chlamydial ²⁸ in vitro transcription assay. We used two in silico approaches, identifying candidate promoters on the basis of sequences that either resemble the consensus bacterial ²⁸ promoter (9, 10) or are predicted to be highly transcribed by ²⁸ RNA polymerase based on functional studies (25). Using information from both approaches, we have a developed a computer algorithm to identify candidate ²⁸ promoters in the chlamydial genome and have shown that five promoters are transcribed by chlamydial ²⁸ RNA polymerase. This method can be applied to other bacterial genomes, and we have also identified five new ²⁸-regulated genes in Escherichia coli.

	MATERIALS AND METHODS

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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 ²⁸ 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 ²⁸ 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 ²⁸ 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 ²⁸-regulated promoter to be tested was cloned into a plasmid so that promoter activity could be measured with a ²⁸ 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 ²⁸ RNA polymerase was expected to produce a 130-nt transcript.

Overexpression and purification of ²⁸. C. trachomatis serovar L2 His₆-²⁸ and E. coli His₆-²⁸ 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 ²⁸ RNA polymerase was reconstituted by mixing 1 µl C. trachomatis recombinant His₆-²⁸ with 1 µl heparin-agarose-purified C. trachomatis RNA polymerase at 4°C for 15 min, immediately prior to the transcription reaction. E. coli ²⁸ RNA polymerase was reconstituted from 1 µl E. coli recombinant His₆-²⁸ and 0.03 units E. coli core enzyme (Epicenter, Madison, Wis.). For antibody inhibition reactions, 8 µg of rabbit polyclonal antichlamydial ²⁸ 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 [-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 x 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

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Development of computer algorithms to identify ²⁸ promoters. We developed two computer algorithms, which we used in parallel to identify candidate ²⁸ 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 ²⁸ promoter structure and sequence. We used the structure of the extended bacterial ²⁸ promoter (12) with eight positions in the –35 element and another eight positions in the –10 element separated by a spacer of variable length.

View larger version (16K): [in this window] [in a new window]   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.

To identify candidate ²⁸ 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 ²⁸ 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 ²⁸ 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 ²⁸ promoters, with the exception of the chlamydial promoter, have a spacer of 11 nt, this spacer length was also heavily weighted.

View larger version (30K): [in this window] [in a new window]   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 28 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/.

By applying these two weighted matrices to the two sets of upstream sequences, we generated four lists of candidate ²⁸ promoters using PromoterMatcher. hctB, the known C. trachomatis ²⁸-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 ²⁸ RNA polymerase. We chose 16 of the top candidate promoters (Table 1) for functional testing with our chlamydial ²⁸ 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 ⁶⁶ RNA polymerase activity (24), we tested for transcription in the absence and presence of recombinant chlamydial ²⁸ as a measure of ⁶⁶-specific and ²⁸-specific activity, respectively. We also assayed for ²⁸-dependent activity by testing for inhibition of transcription by anti-²⁸antibodies.

View this table: [in this window] [in a new window]   TABLE 1. Predicted chlamydial 28 promoters tested for in vitro activity

View larger version (24K): [in this window] [in a new window]   FIG. 3. In vitro transcription of predicted C. trachomatis 28-dependent promoters using chlamydial 28 RNA polymerase. Lane 1, transcription by a C. trachomatis core enzyme preparation containing 66 transcriptional activity but no 28 activity; lane 2, addition of recombinant chlamydial 28 to the core enzyme; lane 3, transcription by core enzyme plus recombinant chlamydial 28 in the presence of anti-chlamydial 28 antibodies (Ab).

View larger version (10K): [in this window] [in a new window]   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 28 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).

View larger version (6K): [in this window] [in a new window]   FIG. 5. Transcription of a mutated pgk promoter. (A) The sequence upstream of the C. trachomatis pgk promoter is shown with the predicted 28 promoter in boldface type, and the mapped transcription start sites are in boldface capital letters. For reference, the consensus bacterial 28 promoter (10, 12) is shown above the sequence, and the chlamydial 66 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 28 RNA polymerase. Lane 1, C. trachomatis core enzyme preparation alone; lane 2, core enzyme with recombinant chlamydial 28 protein; lane 3, transcription of the mutant pgk promoter by core enzyme plus recombinant chlamydial 28 in the presence of anti-chlamydial 28 antibodies (Ab).

Five predicted E. coli ²⁸ promoters were transcriptionally active.

View this table: [in this window] [in a new window]   TABLE 2. Predicted E. coli 28 promoters tested for in vitro activity

View larger version (14K): [in this window] [in a new window]   FIG. 6. In vitro transcription of predicted E. coli 28-dependent promoters. Promoters were transcribed with E. coli 28 RNA polymerase reconstituted from E. coli core enzyme and recombinant E. coli 28 as described in the text.

	DISCUSSION

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Our results show that the combination of a frequency matrix, derived from known ²⁸ promoter sequences, and an activity matrix, based on a mutational analysis of a chlamydial ²⁸-dependent promoter, increased our chances of identifying additional ²⁸ promoters. hctB and tsp had the two strongest promoters in terms of transcriptional activity and sequence conservation with the bacterial ²⁸ 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 ²⁸ consensus sequence would not be very sensitive as a means of identifying ²⁸-dependent promoters in Chlamydia. With the exception of the hctB promoter, the other chlamydial ²⁸ 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 ²⁸ promoter activity (25). In all, the CGA motif of the –10 element was present in five of the six transcriptionally active chlamydial ²⁸ promoters.

In Chlamydia, two of the five newly identified ²⁸-regulated genes have been shown to be expressed late in the developmental cycle in a fashion similar to that of the original ²⁸ 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 ²⁸ 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 ²⁸ and ⁶⁶ RNA polymerases. Thus, it is entirely possible that ²⁸-regulated expression of these target genes may also be restricted to late time points, and as yet, there is no definitive evidence that ²⁸ RNA polymerase is transcriptionally active at earlier times in the chlamydial developmental cycle. In summary, there is accumulating evidence for ²⁸-dependent regulation of a subset of late genes in Chlamydia, distinct from the late genes transcribed by ⁶⁶ 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 ²⁸ RNA polymerase and ⁶⁶ 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 ⁶⁶ RNA polymerase (21) under the control of the HrcA repressor (23). We now show that dnaK has an independent promoter that is transcribed by ²⁸ 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).

View larger version (8K): [in this window] [in a new window]   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 66 and 28 RNA polymerases, respectively. (B) The 66 promoter for the hrcA-grpE-dnaK operon is located upstream of hrcA, while a 28 promoter is located upstream of dnaK within the coding region of grpE. The figure is not drawn to scale.

Our studies support a role for ²⁸ as a developmental regulator of late gene expression in Chlamydia, but little is known about how ²⁸ 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 ²⁸ (11). The discovery that one of the target genes of ²⁸, dnaK, is a known heat shock gene is intriguing and may provide clues about the signal for ²⁸-dependent transcription. Perhaps ²⁸ RNA polymerase is involved in the general stress response in Chlamydia, as supported by the finding that ²⁸ transcript levels were increased under conditions of heat shock (19). By extension, ²⁸-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 ²⁸ promoters in other bacteria or promoters for other forms of RNA polymerase. ²⁸ 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.

	ACKNOWLEDGMENTS

 
We thank Eike Niehus, Johnny Akers, Elizabeth Di Russo, Allan Chen, and Narae Park for their support and G. Wesley Hatfield and Marian Waterman for critical review of the manuscript.

This work was supported by a grant from the NIH (AI 44198). M.T. is supported by an NIH Independent Scientist Award (AI 057563), and H.H.Y.Y. was supported by a predoctoral training grant from the NIH (National Research Service Award 1 T15 LM007443 from the National Library of Medicine).

	FOOTNOTES

 
* Corresponding author. Mailing address: B240, Med. Sci. I, Department of Microbiology and Molecular Genetics, University of California, Irvine, CA 92697-4025. Phone: (949) 824-3397. Fax: (949) 824-8598. E-mail: mingt{at}uci.edu.

Published ahead of print on 22 September 2006.

Supplemental material for this article may be found at http://jb.asm.org/.

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