Transcription Profiling-Based Identification of Staphylococcus aureus Genes Regulated by the agr and/or sarA Loci

Abstract

The advent of transcription profiling technologies has provided researchers with an unprecedented ability to study biological processes. Accordingly, a custom-made Affymetrix GeneChip, constituting >86% of the Staphylococcus aureus genome, was used to identify open reading frames that are regulated by agr and/or SarA, the two best-studied regulators of the organism's virulence response. RNA extracted from wild-type cells and agr, sarA, and agr sarA mutant cells in the early-, mid-, and late-log and stationary phases of growth was analyzed. Open reading frames with transcription patterns expected of genes either up- or downregulated in an agr- and/or SarA-dependent manner were identified. Oligonucleotide microarray and Northern blot analyses confirmed that the transcription of several known virulence genes, including hla (alpha-toxin) and spa (protein A), is regulated by each effector and provided insights about the regulatory cascades involved in both alpha-hemolysin and protein A expression. Several putative virulence factors were also identified as regulated by agr and/or SarA. In addition, genes that are involved in several biological processes but which are difficult to reconcile as playing a direct role in the organism's pathogenesis also appeared to be regulated by each effector, suggesting that products of both the agr and the sarA locus are more-global transcription regulators than previously realized.

Staphylococcus aureus is a major cause of human disease. The organism causes a variety of clinical manifestations, ranging from localized skin infections to severe sepsis, and is a leading cause of hospital-acquired infection (3). Despite advances in antibacterial chemotherapy, S. aureus strains have demonstrated resistance to all currently available antibiotics. Due in part to the immense clinical importance of this organism, an enormous amount of effort has been directed toward identifying the genes and regulatory mechanisms associated with S. aureus pathogenesis. Collectively, this work has demonstrated that the organism's pathogenesis can be attributed to its capacity to produce a variety of virulence factors (29).

The identification of virulence factors and the regulatory networks that influence their expression has been facilitated by the observation(s) that many, if not most, virulence genes are expressed in laboratory cultures. While there is currently a substantial list of staphylococcal virulence factors, it is likely that this list is incomplete and is skewed by the limitations of the experiments used to identify them. Virulence factors that have already been identified generally include (i) bacterial surface proteins that are involved in processes such as adhesion and evasion of the host immune response and (ii) secreted exoproteins that degrade host tissue(s) and inactivate host defensive mechanisms (29).

The genes encoding most virulence factors belong to an extensive regulon that is coordinately regulated in response to a variety of intra- and extracellular signals (1, 5, 21). Octapeptide signaling molecules that are produced as laboratory cultures increase in cell density are the best-studied “stimuli” of the virulence response, mediating a transition in expression of the genes encoding virulence determinants from a predominance of cell surface-expressed genes to a predominance of genes encoding exoproteins (26).

The density-dependent regulation of most virulence factors is mediated by regulatory loci such as the accessory gene regulator (agr) and staphylococcal accessory regulator (sarA) loci (9, 19). In the laboratory setting, post-exponential growth activates the agr locus, which contains two divergent promoters, P2 and P3, that direct expression of RNAII and RNAIII transcripts, respectively (19). RNAII encodes four proteins, AgrB, AgrD, AgrC, and AgrA, all of which are required for agr-mediated virulence factor regulation (19, 27). AgrD and AgrB act to generate an octapeptide quorum-sensing molecule (autoinducing peptide [AIP]), which, after reaching an extracellular threshold concentration, stimulates activation of AgrC and AgrA, the sensor and regulator, respectively, of a two-component regulatory pathway (17–19, 27, 28). Activated AgrA results in the upregulation of RNAII and RNAIII production; the latter is the effector molecule of the agr response (25, 27, 28). RNAIII expression is, in part, responsible for the upregulation of exoprotein production and the downregulation of cell surface protein transcription during the late-log and stationary phases of growth. The manner in which RNAIII regulates target genes is not yet understood, but it is clear that the molecule acts mainly at the level of transcription. RNAIII also encodes the structural gene for delta-hemolysin, although the transcript, rather than the protein product, acts as a global virulence factor regulator (28).

The S. aureus accessory regulatory (SarA) protein also influences both exoprotein and cell surface protein expression (9). The sarA locus contains three overlapping transcripts designated sarA, sarC, and sarB, each of which has a common 3′ end encoding SarA (2). SarA binds to conserved regions termed Sar boxes within promoter regions of genes encoding cell surface proteins (spa, encoding protein A), genes encoding exoproteins (hla, encoding alpha-hemolysin), and agr (12). SarA binding to agr promoter elements augments both RNAII and RNAIII transcription and therefore contributes to virulence factor regulation indirectly (7, 13). SarA has also been shown to regulate expression of spa and hla in an agr mutant background, indicating that SarA controls regulation of certain virulence factors directly, in an agr-independent manner.

Because RNAIII and/or SarA influences the transcription of most known virulence factors, it was hypothesized that additional, previously uncharacterized potential virulence factors may be identified by analyzing the S. aureus genome for open reading frames (ORFs) that are expressed in an agr- and/or SarA-dependent manner. Furthermore, such an analysis may begin to unravel the overlapping RNAIII and SarA regulatory contributions to known virulence factors. Using an S. aureus GeneChip (Affymetrix), we have identified genes that produce transcript patterns expected of genes regulated by the S. aureus agr and/or sarA locus. In addition to confirming the predicted expression patterns of a number of known virulence factors, and thus validating the methodology, we identified a set of putative virulence factors as being regulated by each effector. Moreover, expression patterns of genes involved in a number of biological processes that are difficult to reconcile as contributing directly to S. aureus pathogenesis produced expression profiles expected of genes regulated in an agr- and/or SarA-dependent, density-dependent manner. These results suggest that, in addition to regulating virulence factors, each effector is a more-general transcriptional regulator than previously recognized.

MATERIALS AND METHODS

Bacterial strains and culture conditions.

The S. aureus strains used in this study are listed in Table 1. Strains were grown overnight in 25 ml of brain heart infusion (BHI) medium at 37°C with aeration. Overnight cultures were used to inoculate (1:100 dilution) 1.5 liters of fresh BHI medium. Cultures were incubated with vigorous aeration at 37°C and aliquots were removed at the indicated growth phases as determined by growth phase analysis (data not shown). Cells from each aliquot were pelleted by centrifugation at 5,000 × g for 10 min at 4°C in a Beckman JA-10 rotor and were stored at −80°C.

TABLE 1.

S. aureus strains used in this study

Strain	Relevant genotype	Source or reference
RN27	agr+sarA+
RN6911	agr sarA+; RN27 derivative with an agr::tetM mutation	28
ALC488	agr+sarA; RN27 derivative with a sarA::ermC mutation	8
ALC842	agr sarA; agr mutation in RN6911 transduced into ALC488	8

RNA extraction.

Total bacterial RNA was extracted from samples by resuspending each cell pellet at a concentration of 10⁹ CFU per ml in TE buffer (10 mM Tris-HCl, 1 mM EDTA [pH 8.0]) containing 50 μg of lysostaphin (Amicon)/ml. Resuspensions were incubated at 37°C for 60 min, and 1 × 10¹⁰ to 5 × 10¹⁰ CFU was then applied to a Qiagen RNeasy Maxi column, according to the manufacturer's recommendations for prokaryotic RNA isolation. RNA concentrations were determined by spectrophotometry (an optical density at 260 nm of 1.0 equals 40 μg of RNA/ml). To assess RNA integrity and for Northern blot analysis, 2 μg of each RNA sample was electrophoresed in a 1.2% agarose–0.66 M formaldehyde gel, according to Qiagen RNA electrophoresis recommendations.

Northern blot analysis.

RNA samples were transferred from formaldehyde-containing agarose gels to Biodyne B membranes (Gibco-BRL) by standard molecular procedures. High-stringency hybridization was performed in Kapak/Scotchpak heat-sealable pouches containing 12 ml of PerfectHyb Plus hybridization buffer (Sigma) and 10 μl of radiolabeled randomly primed DNA synthesis products from PCR-generated templates at 42°C. Membranes were subjected to high-stringency washes and were analyzed by phosphorimaging (Bio-Rad Phosphorimager) or densitometry (Bio-Rad). The following PCR primers were used to generate PCR templates: for 16S rRNA (478 nucleotides [nt] of GenBank accession no. Y15856), 5′-AAATCTTGACATCCTTTGACAACTC-3′ and 5′-CTAGCTCCTAAAAGGTTACTCCACC-3′; for spa (1,999 nt of GenBank accession no. M18264), 5′-AAGATTTAATTGAAACAATCCACCA-3′ and 5′-GACCAGGTTTGATCATGTTTTTATC-3′; for hla (769 nt of GenBank accession no. X01645), 5′-TAGGTTCCATATTGATGAATCCTGT-3′ and 5′-ATATTTGTTTGTTGTTTGGATGCTT-3′; and for RNAIII (480 nt of GenBank accession no. AF288215), 5′-GGGGCTCACGACCATACTTA-3′ and 5′-GGAGTGATTTCAATGGCACA-3′. All PCR products were subjected to restriction enzyme analysis and were gel purified prior to labeling reactions.

mRNA enrichment, fragmentation, and biotinylation.

For mRNA enrichment reactions (240 μl), 200 μg of total bacterial RNA was mixed with 0.7 μM (final concentration) rRNA-specific oligonucleotide mix (5′-GATACGGCTACCTTGTT-3′, 5′-TCAACCTTGCGGTCGTACTC-3′, 5′-TCCGGATAACGCTTGCCACC-3′, 5′-AGCACTTATCCCGTCCACAC-3′, 5′-CTACAGTAAAGCTCCACGGG-3′, and 5′-TCCCCATCACAGCTCAGCCT-3′). Aliquots (30 μl) were transferred to 0.5-ml thin-walled Eppendorf tubes (Perkin-Elmer), and solutions were incubated at 70°C for 5 min in a thermocycler (Perkin-Elmer). Reverse transcriptase reaction mixture (50 mM Tris-HCl [pH 8.3], 10 mM MgCl₂, 75 mM KCl, 10 mM dithiothreitol, 0.5 mM each deoxynucleoside triphosphate, 735 U of RNAguard [Amersham Pharmacia Biotech], and 3,000 U of Moloney murine leukemia virus reverse transcriptase [Epicentre]) was then added. Solutions were incubated at 42°C for 25 min, followed by 45°C for 20 min. For rRNA removal, 0.5 U of RNase H (Epicentre) was added and mixtures were incubated first at 37°C for 45 min and then at 65°C for 5 min. DNase I (0.12 U/μl; Amersham Pharmacia Biotech) and 0.225 U of RNAguard were added, and mixtures were incubated at 37°C for 20 min. Reactions were terminated by addition of 10 mM EDTA. Samples were applied to a Qiagen RNeasy minicolumn, according to manufacturer recommendations for RNA clean-up. For RNA fragmentation, 20 μg of an mRNA-enriched sample was mixed with fragmentation buffer (1× T4 polynucleotide kinase buffer [New England Biolabs], 70 mM Tris-HCl [pH 7.6], 10 mM MgCl₂, 5 mM dithiothreitol) and incubated at 95°C for 30 min and then cooled to 4°C. Samples were then 5′-thiolated by addition of 0.1 mM γ-S-ATP and 1 U of T4 polynucleotide kinase (New England Biolabs) and were incubated at 37°C for 50 min, followed by 65°C for 10 min. Samples were then ethanol precipitated and subsequently biotinylated by addition of 2 mM polyethylene oxide (PEO)-iodoacetyl-biotin and 30 mM morpholinepropanesulfonic acid (MOPS), pH 7.5. Unincorporated biotin molecules were removed by passing RNA mixtures through a Qiagen RNA/DNA minicolumn according to manufacturer recommendations.

S. aureus GeneChip design.

Preliminary genomic sequence data of the S. aureus COL strain were obtained from The Institute for Genomic Research (TIGR) website (http://www.tigr.org). The sequence consisted of approximately 2,000 contigs, which were concatenated into a single sequence alternating with an 18-nt sequence containing stop codons in all 6 frames. ORF predictions were made using a combination of GLIMMER 1.0 (32) and GeneMark.hmm (23), with a minimum ORF size of 75 nt. In all, 4,528 ORFs, 12 tRNAs, and 3 rRNAs were tiled, with an average of 25 probe sets per ORF. Due to the preliminary state of the genome sequence, many genes are represented on the chip as partial, duplicate, or overlapping fragments. Recent analyses based on the updated sequences of the genomes of COL, NCTC 8325 (OU-ACGT), and MRSA (Sanger Centre) suggest that these 4,528 tilings represent approximately 2,700 to 2,900 individual genes and that >86% of the S. aureus COL genome is represented on the chip.

GeneChip hybridization and washing.

Prior to RNA hybridization, S. aureus GeneChips (Affymetrix) were brought to room temperature, washed once with hybridization buffer (100 mM N-morpholinoethanesulfonic acid [MES], 1 M Na⁺, 20 mM EDTA, 0.01% Tween 20), loaded (250 μl) with hybridization buffer, and incubated at 45°C for at least 10 min. Hybridization mixture (1.5 μg of biotinylated mRNA-enriched RNA, 50 pM oligonucleotide B2 [Affymetrix], 0.1 mg of herring sperm DNA/ml, 0.5 mg of acetylated bovine serum albumin [BSA; Gibco-BRL], and 1× control spike-in cocktail [Genetics Institute] in 1× hybridization buffer) was denatured by incubating at 95°C for 5 min, followed by 45°C for 5 min, and was centrifuged at high speed on a tabletop microcentrifuge for 20 min at room temperature to pellet all nonsolubilized material. A 200-μl volume of hybridization mixture was then loaded onto a GeneChip and incubated for 15 h at 45°C. Following hybridization, the RNA-containing mixture was removed and stored at −80°C. GeneChips were then washed 20 times with nonstringent buffer (6× SSPE [1× SSPE is 0.18 M NaCl, 10 mM NaH₂PO₄, and 1 mM EDTA {pH 7.7}], 0.01% Tween 20, 0.005% antifoam) at 25°C. Chips were then subjected to 60 washes with stringent buffer (100 mM MES, 0.1 M Na⁺, 0.01% Tween 20) at 45°C. Following washing, the hybridized RNA was treated with primary stain (10 μg of streptavidin, 2 mg of acetylated BSA) in stain buffer (100 mM MES, 1 M Na⁺, 0.05% Tween 20, 0.005% antifoam) for 10 min at 25°C and was washed 40 times with nonstringent buffer. Next each GeneChip was subjected to a secondary stain containing 2 mg of acetylated BSA, 0.1 mg of goat immunoglobulin G, and 5 μg of biotinylated anti-streptavidin (Vector Laboratories) in stain buffer for 10 min at 25°C. The chip-bound RNA molecules were labeled with 10 μg of streptavidin-phycoerythrin conjugate (Molecular Probes)/ml in stain buffer for 10 min at 25°C. Excess label was removed by 100 nonstringent washes at 25°C.

GeneChip analysis.

To increase reproducibility, each experiment was performed in duplicate (RNA extraction from various phases of growth for each strain) and each RNA sample was hybridized to two separate GeneChips. The work here constitutes a total of 16 GeneChips for each strain analyzed. Each GeneChip was scanned at 570-nm, 3-μm resolution in an Affymetrix GeneChip scanner. Affymetrix algorithms calculated signal intensities (average difference) and made present or absent determinations for each gene, as previously described (22). Next, to normalize for global systematic variations that could be caused by inconsistencies in loading, each average difference value was divided by the median average difference for a given GeneChip. Normalized intensity values were then averaged for each gene at each growth phase. GeneSpring, version 3.2.11, software (Silicon Genetics, Redwood City, Calif.) was used to plot normalized intensity values across growth phases. GeneSpring algorithms also determined signal strength values for each gene within a strain as the product of the average normalized signal intensity value of the gene of interest and the median normalized signal intensity of the gene taken throughout growth phases. To identify genes that are below the detection limit of the system, the signal strengths indicative of genes with profiles at the level of noise were determined for each strain as the average signal strength of genes considered absent (via GeneChip algorithms) plus 2 standard deviations.

Identification of Sar boxes.

Intergenic regions were identified within the S. aureus N315 genome by comparing ORF nucleotide coordinates (20). Putative Sar boxes (12) were identified using the Genetics Computer Group (Madison, Wis.) program (Wisconsin Sequencing Package).

RESULTS

Identification of S. aureus virulence factors.

In the laboratory setting, expression of many S. aureus virulence factors follows a predictable pattern. Cell surface virulence factors, such as protein A (encoded by spa), are predominately expressed during early-log-phase growth, but as the density of a growing culture increases, transcription of these genes decreases. Genes encoding extracellular virulence factors, such as alpha-toxin (encoded by hla), demonstrate a reciprocal phenotype; they tend to be transcribed at basal levels during early-log-phase growth and are upregulated at higher cell densities. This density-dependent regulation of most virulence determinants can be controlled by a product of the agr locus, RNAIII, and/or SarA.

Although there is currently a sizeable list of RNAIII- and SarA-regulated putative virulence factors, it seems likely that this list is incomplete and that an expansion of known virulence factors is needed to better understand S. aureus pathogenic processes. In an effort to identify previously unrecognized agr- and/or SarA-regulated genes, we performed transcription profiling on RNA samples extracted from wild-type cells (RN27) and agr (RN6911), sarA (ALC488), and agr sarA (ALC842) mutant cells during various phases of growth (early-, mid-, and late-log and stationary phases).

agr regulation by SarA.

To validate the transcription profiling methodology used, we first investigated whether the agr locus transcripts, RNAII and RNAIII, produced expression patterns mimicking previously reported data. It is well established that agr is temporally regulated in a growth phase-dependent manner. Basal levels of RNAII and RNAIII are detected during early-log-phase growth; transcription subsequently increases as cells progress through growth phases (16, 34). Additionally, Cheung and colleagues have shown that SarA interacts with agr promoter regions and facilitates RNAII and RNAIII transcription (7, 15).

As expected, transcription profiling of RNA extracted from cells at various phases of growth demonstrated that both RNAII and RNAIII production increased (4.8- and 5.7-fold, respectively) as wild-type cells (RN27) transitioned from the early-log to stationary phase of growth, with maximal expression detected during post-exponential growth (Fig. 1A and B). In contrast, both RNAII and RNAIII transcript levels were below the detection limits of the system in agr (RN6911) and agr sarA (ALC842) mutant cells (Fig. 1A and B). Moreover, Affymetrix GeneChip software analysis (see Materials and Methods) of RNA samples from RN6911 and ALC842 cultures determined that the genes constituting RNAII (agrA, agrB, agrC, and agrD) and RNAIII transcripts were absent in all phases of growth (data not shown). As shown in Fig. 2A, these results correlate well with those obtained by Northern analysis, which indicated that RNAIII was induced 14.2-fold in RN27 cells during post-exponential growth but was not detectable in RN6911 cells. Furthermore, our transcription profiling data (Fig. 1B) demonstrated a ∼2.6-fold decrease in RNAIII production by a sarA mutant strain (ALC488) during stationary-phase growth, in comparison to wild-type transcript levels, confirming that SarA is required for wild-type levels of agr transcription. Collectively these results indicate that our transcription profiling data are in good agreement with previous observations and confirm that both RNAII and RNAIII are expressed in a cell density-dependent manner.

FIG. 1.

Transcription profiles of known RNAIII and SarA-regulated genes. Transcript levels for wild-type (□), agr mutant (▴), sarA mutant (■), and agr sarA double-mutant (⧫) cells were measured during the early-, mid-, and late-log and stationary phases of growth (x axis). Data points were plotted as relative intensity values (y axis) (as described in Materials and Methods). (A) Average signal intensities for genes constituting RNAII transcripts (agrB, agrD, agrC, and agrA). (B) Signal intensities of RNAIII transcripts. (C) Profiles of alpha-toxin (hla) transcript titers. (Inset) agr and agr sarA mutant results. (D) Profiles of protein A (spa) transcript titers.

FIG. 2.

Northern blot analysis of known agr-regulated genes. Shown are levels of RNAIII (A), hla (B), spa (C), and rRNA (D) transcripts during the early-, mid-, and late-log and stationary phases of growth of wild-type (RN27) or agr mutant (RN6911) cells. Signal intensities were determined by densitometry and normalized to rRNA signals. Normalized relative signal intensities are shown below each panel.

agr and SarA regulation of alpha-toxin.

To further validate the methodology and extend our knowledge about the transcription of the well-studied agr-regulated exoprotein alpha-toxin (encoded by hla), we investigated whether our transcript profiles correlated well with published hla expression patterns. It has been shown that RNAIII production promotes hla expression, with maximum levels being reached at the post-exponential phase of growth (6, 34). As shown in Fig. 1C, transcription profiling results demonstrated a 39-fold increase in hla transcription in wild-type (RN27) cells as they transitioned from the early-log to stationary phase of growth, and these results were in good agreement with the 20.9-fold increase seen in our Northern analysis (Fig. 2B). In contrast, the increase in transcription was reduced to 2.2-fold in RN6911 cells (Fig. 1C), confirming that a product of the agr locus mediates hla upregulation during the post-exponential phase. These results also indicate that an additional regulatory mechanism(s) may account for the slight (2.2-fold) increase in hla expression in agr mutant cells. Indeed, SarA binding sites have been identified within the hla promoter region, and SarA has been shown to bind directly to the promoter region and induce hla expression in an agr-independent manner (6, 12). To determine whether this modest increase in expression could be attributed to SarA, as opposed to an additional virulence regulator(s), the profiles of hla transcripts were compared for various growth phases of sarA and agr sarA mutant cells. As expected, hla expression was marginally induced in post-exponential sarA mutant cells (Fig. 1C). However, because SarA is necessary for complete expression of agr, the strain is effectively agr deficient and the observed reduction in hla expression may be a direct consequence of decreased production of RNAIII (10, 12). Yet hla was constitutively transcribed during various phases of growth in agr sarA mutant (ALC842) cells (Fig. 1C), confirming that SarA both directly and indirectly regulates alpha-hemolysin transcription. Interestingly, GeneChip analysis (see Materials and Methods) determined that hla transcripts were present in all phases of ALC842 growth (data not shown), implying that neither regulator is required for basal levels of alpha-hemolysin transcription. In addition, these results suggest that both RNAIII and SarA are required for wild-type levels of hla transcription during post-exponential aerobic growth.

agr and SarA regulation of protein A.

It has previously been shown that wild-type S. aureus transcribes spa during early-log-phase growth and that its transcription is reduced when RNAIII is produced. Conversely, spa is transcribed at a high level throughout all phases of growth in an agr-null strain (34). The spa promoter contains SarA boxes (12). Both Northern analysis and spa-reporter fusion studies have shown that SarA is a repressor of protein A transcription (4, 10). To determine whether transcription profiling could identify genes known to be downregulated by each effector, we compared the protein A (spa) transcript profiles of each strain. As shown in Fig. 1D, wild-type cells maximally expressed spa at early- and mid-log phases of growth, and transcription decreased 6.8-fold as cells entered stationary phase. A similar phenotype (a 7.2-fold decrease) was observed by Northern analysis (Fig. 2C). Comparison of signal strengths (see Materials and Methods) demonstrated that agr mutant cells expressed high levels of spa throughout all phases of growth (average normalized signal strength value, 1,031.5, as opposed to 170.8 in wild-type cells), confirming that the agr locus is a potent repressor (>5-fold) of spa transcription. Northern blot analysis (Fig. 2C) confirmed those observations. Transcription profile analysis also revealed that, like agr mutant cells, spa transcription is significantly increased, across all growth phases, in the sarA mutant (Fig. 1D). This finding is in good agreement with previously published spa expression levels in sarA mutant cells (8).

agr-stimulated ORF identification.

Given the level of correlation between the transcription profiling data obtained in this study and previously reported results for RNAII, RNAIII, hla, and spa expression, we felt comfortable searching for genes that were previously unrecognized as being regulated by the agr and/or sarA locus. To identify genes upregulated by a product of the agr locus, we took advantage of the observation that transcription of most identified agr-upregulated virulence genes, such as hla, increases with cell density in wild-type cells but does not increase in a density-dependent manner in agr mutant cells. Using GeneSpring software, we were able to compare transcript profiles of genes by analyzing RNA samples taken during various phases of growth from wild-type cells and agr (RN6911), sarA (ALC488), and agr sarA (ALC842) mutant cells. Genes demonstrating transcript patterns expected of those upregulated in an RNAIII-dependent manner were identified by querying for genes that were upregulated at least 2- and 1.5-fold as wild-type and ALC488 cells, respectively, moved from the early-log to the stationary phase of growth but were not upregulated 2-fold or were expressed at background levels in RN6911 and ALC842 cells. Alternatively, genes that were constitutively expressed in wild-type cells but were expressed at twofold-lower levels in both RN6911 and ALC842 cells were likely to be stimulated by a product of the agr locus. In each case the gene also had to be determined to be present in at least one wild-type stationary-phase sample, according to Affymetrix GeneChip software restrictions (Table 2). As expected, genes constituting the agr RNAII transcript (agrB, agrD, agrC, and agrA) produced the looked-for transcript profiles. Likewise, RNAIII (hld, encoding delta-hemolysin) appeared to be induced in an agr-dependent manner. Several known RNAIII-inducible genes were also identified, including splA, splB, splD, and splF (encoding serine protease) and hla (encoding alpha-toxin) (30, 31). Other extracellular virulence factors, such as hlgB and hlgC (encoding gamma-hemolysin) and geh (encoding lipase), were also identified.

TABLE 2.

agr-upregulated genes

ORFa	Geneb	Patternc	SarA effectd	Functione	Fold changef	N315 ORFg	Role categoryh	Sar boxi
373	pyrAA	1 (1)	Up	Carbamoyl-phosphate synthetase	3.3	SA1045	Nucleic acid metabolism
591		1 (1)	Up	Cytidylate kinase	2.3	SA0515	Nucleic acid metabolism
3298	cap5J	1 (2)	Up	Capsule gene	3.6	SA0153	Adaptation
831	dps	2 (2)	Indirect	General stress protein 20U	3.0	SA1941	Adaptation
4897	fmt	1 (2)	Indirect	Methionyl-tRNA formyltransferase	2.8	SA1059	Aminoacyl tRNA synthetases
4084	arcA	2 (2)	Indirect	Arginine deaminase	5.7	SA2428	Amino acid metabolism
422	arcB	1 (1)	Indirect	Aspartate/ornithine carbamoyltransferase	8.7	SA2427	Amino acid metabolism
1220	arcC	2 (3)	Down	Carbamate kinase	7.9	SA2425	Amino acid metabolism
1321	aroC	1 (1)	Up	Chorismate synthetase	3.1	SA1299	Amino acid metabolism
5609	cpsA	1 (1)	Indirect	Amino acid amidohydrolase	2.6	SA0507	Amino acid metabolism
3360	gltB	1 (2)	Indirect	Glutamate synthase	2.3	SA0431	Amino acid metabolism
997	hutG	1 (1)	Up	Arginase	4.5	SA2125	Amino acid metabolism	Yes
774	hutH	2 (3)	Indirect	Histidine ammonia-lyase	4.0	SA0008	Amino acid metabolism
1001	hutI	2 (2)	Indirect	Imidazolone-5-propionate hydrolase	41.5	SA2121	Amino acid metabolism	Upstream
1000	hutU	1 (1)	Indirect	Uroconate hydratase	23.3	SA2122	Amino acid metabolism	Yes
4969	ocd	1 (1)	Indirect	Ornithine-cyclodecarboxylase	2.5	SA0113	Amino acid metabolism
2628	pepF	2 (3)	Up	Peptidase	3.4	SA1216	Amino acid metabolism
6	sdhA	1 (3)	Indirect	l-Serine deaminase	6.5	SA2318	Amino acid metabolism
7	sdhB	1 (2)	Up	l-Serine dehydratase beta subunit	7.6	SA2319	Amino acid metabolism
1765	epiF	1 (2)	Up	ABC-type multidrug transport system	2.4		Antibiotic production
4746	epiP	1 (2)	Up	Serine protease	2.5		Antibiotic production
3699		1 (1)	Up	Antibacterial peptide synthesis	5.1	SA0173	Antibiotic production
1142		1 (2)		Bacteriophage gene	2.3	SA1786	Bacteriophage related
1132		1 (1)	Indirect	Putative integrase activator	3.6	SAS062	Bacteriophage related
5455	femB	1 (4)		mec resistance	2.2	SA1207	Cell wall
2618		1 (1)		UTP-glucose-1-phosphate uridylyltransferase	2.6		Cell wall
4659	moaB	1 (2)	Indirect	Molybdenum cofactor	2.7	SA2070	Coenzyme metabolism
3390	recQ	1 (6)	Indirect	Probable DNA helicase	2.8	SA0676	DNA replication
3585	rocA	1 (2)	Indirect	NAD-dependent aldehyde dehydrogenases	2.7	SA2341	Electron transport
4103	pckA	1 (2)	Indirect	Phosphoenolpyruvate carboxykinase	3.6	SA1609	Glycolysis
3898	crtN	1 (2)	Up	Putative phytoene dehydrogenase	2.1	SA2351	Lipid metabolism
882		1 (2)	Indirect	Acyl-coenzyme A dehydrogenase	2.2	SA2080	Lipid metabolism
1399	chiB	1 (1)	Indirect	Predicted chitinase B	2.8	SA0914	Miscellaneous
1398		1 (1)	Indirect	Predicted chitinase B	2.7	SA0914	Miscellaneous
5163		1 (3)	Indirect	Putative GTPase	2.7	SA1086	Miscellaneous
1044		1 (1)	Indirect	Cysteine sulfinate desulfinase	2.9	SA1450	Miscellaneous
2444	agrA	1 (1)	Indirect	agr response	5.8	SA1844	Signal transduction	Upstream
1426	agrB	1 (1)	Indirect	agr response	3.4	SA1842	Signal transduction	Yes
2443	agrC	2 (2)	Indirect	agr response	4.8	SA1843	Signal transduction	Upstream
1425	agrD	1 (1)	Indirect	agr response	5.9	SAS066	Signal transduction	Upstream
884	odhA	6 (6)	Indirect	2-Oxoglutarate dehydrogenase	2.3	SA1245	TCA cycle
885	odhB	2 (2)	Indirect	2-Oxoglutarate dehydrogenase	3.1	SA1244	TCA cycle
1221	arcR	1 (1)	Down	Transcriptional regulator, Crp/Fnr family	18.9	SA2424	Transcriptional regulation
4367	pyrR	1 (2)	Indirect	Transcriptional attenuator, pyrimidine biosynthesis	2.8	SA1041	Transcriptional regulation
3806		2 (3)	Indirect	Transcriptional regulator, RpiR family	2.5	SA0187	Transcriptional regulation	Upstream
5259		1 (3)	Up	Transcriptional regulator, GntR family	5.2	SA0434	Transcriptional regulation
2852		1 (1)	Up	Putative transcriptional regulator, GntR family	2.4	SA1120	Transcriptional regulation
5005		1 (2)	Indirect	Acetyltransferases	3.0	SA2052	Translation
421	arcD	2 (2)	Indirect	Arginine/ornithine antiporter	6.4	SA2426	Transport
4309	fhuD	1 (2)	Up	Ferrichrome-binding periplasmic proteins	3.2	SA2079	Transport
5224	gltP-2	2 (4)	Indirect	Proton/sodium-glutamate transport	4.3	SA0368	Transport	Yes
2883	lysP	1 (1)	Indirect	Lysine-specific permease	2.3	SA1505	Transport
2606	opp-1F	1 (3)	Indirect	Oligopeptide transporter, ATPase domain	2.3	SA2251	Transport
2601	opp-2B	1 (3)	Up	Oligopeptide transporter	2.4	SA1214	Transport	Yes
3147	potD	1 (2)	Indirect	Spermidine/putrescine transport protein D	2.3	SA0953	Transport
2978		1 (1)	Indirect	ABC-type Mn/Zn transporter	2.5	SA0588	Transport
922		2 (2)	Indirect	ABC-type Mn/Zn transport system component	2.7	SA0589	Transport
2096	veg	1 (1)	Indirect		2.7	SA0452	Unknown
987		1 (1)	Indirect	Conserved hypothetical	12.6	SA0170	Unknown
992		1 (3)	Up	Conserved hypothetical	14.3	SA0174	Unknown
2756		2 (4)	Indirect	Conserved hypothetical	7.0	SA0184	Unknown	Yes
3090		2 (2)	Indirect		2.2	SA0185	Unknown	Upstream
4927		2 (2)		Conserved hypothetical	3.0	SA0271	Unknown
4370		1 (2)	Up		3.4	SA1037	Unknown
2479		1 (2)		Conserved hypothetical	7.0	SA1275	Unknown
2782		1 (2)	Up	Hypothetical protein	2.3	SA1319	Unknown
2054		1 (1)	Indirect	Predicted membrane protein	2.4	SA1379	Unknown
2065		1 (2)	Indirect	Conserved hypothetical	3.0	SA1436	Unknown
2394		1 (1)	Indirect	Conserved hypothetical	2.2	SA1684	Unknown
2122		1 (2)	Indirect	Conserved hypothetical	3.4	SA2128	Unknown
1555		1 (2)	Up	Acetyltransferase	2.0	SA2161	Unknown
9		1 (1)	Up	Hypothetical protein	3.6	SA2321	Unknown
37		1 (1)	Up	Hypothetical protein	17.3	SA2343	Unknown
3982		1 (2)	Up	Conserved hypothetical	4.7	SA2491	Unknown
237		1 (1)	Indirect	Hypothetical protein	2.1	SAS013	Unknown
644		1 (1)	Up	Hypothetical protein	5.7	SAS016	Unknown
203		1 (1)	Down	Hypothetical protein	3.2		Unknown
1494		1 (1)	Indirect	Hypothetical protein	2.3		Unknown
2275		1 (1)	Up	Hypothetical protein	2.4		Unknown
2617		1 (2)	Indirect	Hypothetical protein	2.1		Unknown
2827		1 (1)	Indirect	Hypothetical protein	4.3		Unknown
2968		1 (1)	Indirect	Hypothetical protein	2.3		Unknown
5500		1 (3)	Indirect	Hypothetical protein	2.3		Unknown
3653	aur	1 (5)	Indirect	Aureolysin	2.8	SA2430	Virulence factors	Upstream
396	clfB	3 (6)	Indirect	Clumping factor B	2.7		Virulence factors
583	geh	2 (2)	Indirect	Lipase (glycerol ester hydrolase)	3.5	SA0309	Virulence factors
4061	hla	1 (1)	Up	Alpha-toxin	38.8	SA1007	Virulence factors	Yes
1427	hld	2 (2)	Indirect	Delta-hemolysin	7.9	SAS065	Virulence factors	Yes
1928	hlgB	1 (1)	Up	Gamma-hemolysin component B	48.6	SA2209	Virulence factors	Upstream
1927	hlgC	1 (1)	Up	Gamma-hemolysin component C	6.3	SA2208	Virulence factors	Yes
667	lip	2 (2)		Lipase	3.4	SA2463	Virulence factors	Yes
1175	pls	1 (2)	Up	Related to surface protein LPXTG motif	3.3	SA2284	Virulence factors
2036	set8	1 (3)	Indirect	Exotoxin 2	2.2	SA0384	Virulence factors
2928	splA	2 (2)	Indirect	Serine protease	2.5	SA1631	Virulence factors	Yes
2929	splB	1 (1)	Up	Serine protease	9.6	SA1630	Virulence factors	Upstream
324	splD	2 (2)	Up	Serine protease	ND	SA1628	Virulence factors	Upstream
327	splF	2 (2)	Up	Serine protease	11.3	SA1627	Virulence factors	Upstream
2175	sspC	1 (1)	Down	Protease	2.4	SA0899	Virulence factors
2927		1 (10)		Similar to streptococcal adhesin Emb	3.0	SA1268	Virulence factors
1029		1 (2)	Up	Related to map protein-surface protein	7.7	SA2006	Virulence factors
4374		2 (2)	Up	Surface protein map	3.1	SA2006	Virulence factors
5147		3 (15)	Indirect	Homolog of streptococcal hemagglutinin	3.1	SA2447	Virulence factors
4523		2 (2)	Up	Phenol-soluble modulin beta 2	6.2		Virulence factors

^aDesignated S. aureus GeneChip ORF number. 

^bPreviously described gene name. 

^cMany genes are represented on the S. aureus GeneChip in a redundant manner as full or partial fragments. Values are the number of times a gene transcript was identified as fitting a pattern (number of times the gene was either partially or completely represented on the GeneChip). 

^dExpected SarA contribution to agr-regulated gene transcription. 

^ePreviously described gene product function. 

^fFold change in expression of agr-regulated genes as cells transition from the early-log to the stationary phase of growth. 

^gCorresponding designated S. aureus strain N315 gene (20). 

^hExpected metabolic role. TCA, tricarboxylic acid. 

ⁱYes, putative Sar box identified within the gene's promoter region. Upstream, the gene is expected to be part of an operon with a potential Sar box located in an upstream promoter region. 

Identification of agr-repressed ORFs.

Table 3 lists 64 genes that produced transcript patterns expected of genes downregulated in an agr-dependent manner. These genes appeared to be transcribed at levels at least twofold higher in stationary-phase agr mutant cells than wild-type cells. Additionally, genes were considered present in at least one wild-type stationary-phase sample, according to GeneChip software restrictions. As expected, known RNAIII-downregulated genes, such as spa (34), were detected. Several additional cell wall-associated proteins, such as dlt (involved in teichoic acid linkage to cell wall and resistance to defensins), isaA (cell wall secretory protein), and mnhA, mnhF, and mnhG (constituting a heterologous surface receptor) are potentially repressed by agr.

TABLE 3.

agr-downregulated genes

ORFa	Geneb	Patternc	SarA effectd	Functione	Fold changef	N315 ORFg	Role categoryh	Sar boxi
2515	bsaA	1 (2)		Glutathione peroxidase	5.8	SA1146	Adaptation
1889	ald	2 (2)		Alanine dehydrogenase	11.9	SA1272	Amino acid metabolism
5061	bfmBAB	1 (2)		Thiamine pyrophosphate-dependent dehydrogenases	26.8	SA1347	Amino acid metabolism
626	nasE	1 (3)		Assimilatory nitrite reductase	115.7	SA2187	Amino acid metabolism
1888	tdcB	2 (2)	Down	Threonine dehydratase	33.8		Amino acid metabolism
1664	ddh	1 (2)		d-specific d-2-hydroxyacid dehydrogenase	2.5	SA2312	Carbohydrate metabolism
482	treA	1 (1)		Alpha-glucosidase	10.8	SA0433	Carbohydrate metabolism
4571	folD	1 (2)		5,10-Methylene-tetrahydrofolate dehydrogenase	6.5	SA0915	Coenzyme metabolism
2000	hemL	1 (4)		Glutamate-1-semialdehyde aminotransferase	10.9	SA1491	Coenzyme metabolism
625	sirB	1 (3)		S-adenosyl-l-methionine	ND	SA2186	Coenzyme metabolism
4186	thiD	1 (2)	Down	Hydroxymethylpyrimidine/phosphomethylpyrimidine kinase	3.7	SA0537	Coenzyme metabolism
5593	dinG2	1 (4)		Putative ATP-dependent DNA helicase	4.7	SA1288	DNA replication
777	gyrA	2 (7)		DNA gyrase	6.2	SA0006	DNA replication
5293	mnhA	1 (7)		Multisubunit Na+/H+ antiporter	5.1	SA0813	Electron transport
2092	mnhF	1 (1)		Multisubunit Na+/H+ antiporter	11.5	SA0808	Electron transport
3524	narG	2 (9)		Anaerobic dehydrogenases	93.4	SA2185	Electron transport
622	narJ	1 (2)		Nitrate reductase	ND	SA2183	Electron transport
4539	hit	1 (2)		Diadenosine tetraphosphate (Ap4A) hydrolase	2.7	SA1656	Miscellaneous
1366		1 (2)		Protease related to collagenase	3.0	SA1441	Miscellaneous
483		1 (3)		Transcriptional regulator, GntR family	6.5	SA0434	Transcriptional regulation
1505	pth	1 (2)		Peptidyl-tRNA hydrolase	2.2	SA0460	Translation elongation
1732	dltD	1 (3)		DttD protein	58.3	SA0796	Transport
4405	glpF	1 (2)	Down	Glycerol uptake facilitator	3.9	SA1140	Transport
952	opuD	1 (2)		Choline-glycine betaine transporter	3.0	SA1183	Transport	Yes
481	treP	1 (1)		Phosphotransferase system IIC component	13.1	SA0432	Transport
4806		2 (4)		Putative efflux membrane protein	18.4	SA1269	Transport
1887		2 (3)		Amino acid permease	ND	SA1270	Transport
2206		1 (2)		Putative glucarate transporter	4.6	SA2300	Transport
121		1 (3)		Conserved hypothetical	ND	SA0212	Unknown
100		1 (2)	Down	Conserved hypothetical	34.0	SA0412	Unknown
4122		1 (2)	Down	Conserved hypothetical	11.5	SA2378	Unknown	Yes
4081	spa	2 (2)	Down	Protein A	4.4	SA0107	Virulence factors	Yes
3910	ssaA	1 (2)		Secretory antigen precursor	17.0	SA2093	Virulence factors
1941		3 (3)		Myosin cross-reactive antigen	2.8	SA0102	Virulence factors	Yes

^aDesignated S. aureus GeneChip ORF number. 

^bPreviously described gene name. 

^cMany genes are represented on the S. aureus GeneChip in a redundant manner as full or partial fragments. Values are the number of times a gene transcript was identified as fitting a pattern (number of times the gene was either partially or completely represented on the GeneChip). 

^dExpected SarA contribution to agr-regulated gene transcription. 

^ePreviously described gene product function. 

^fFold change in expression of agr-regulated genes as cells transition from the early-log to the stationary phase of growth. ND, not determined. 

^gCorresponding designated S. aureus strain N315 gene (20). 

^hExpected metabolic role. 

ⁱYes, putative Sar box identified within the gene's promoter region. Upstream, the gene is expected to be part of an operon with a potential Sar box located in an upstream promoter region. 

Effects of SarA on agr-regulated genes.

As shown in Fig. 1A and B, SarA is required for wild-type levels of agr transcription. More specifically, RNAII and RNAIII transcripts were decreased ∼2.6-fold (±0.6-fold) in sarA mutant (ALC488) cells. Extending this observation, we identified potential agr-stimulated genes (Table 2) that demonstrate a similar fold reduction (within 2 standard deviations) in ALC488 cells. Genes meeting this criterion are presumed to be indirectly regulated by SarA and are indicated in Table 2. Likewise, genes suspected to be downregulated by agr (Table 3) that demonstrated a similar fold increase in transcript levels in ALC488 cells may be indirectly regulated by SarA, as indicated in Table 3. However, it should be kept in mind that RNAIII production has different regulatory effects on responsive genes (i.e., hla is dramatically upregulated in the presence of RNAIII, whereas expression of other genes, such as tst [encoding toxic shock syndrome toxin 1], is stimulated to a lesser extent) (26). Therefore, caution should be exercised in further interpreting these results. Yet comparison of genes determined to be directly regulated by SarA (described below) is in good agreement with our indirect SarA analysis.

Identification of SarA-upregulated genes.

A series of transcription profile comparisons were performed to identify genes expected to be upregulated by SarA in a density-dependent manner. First, genes upregulated at least twofold as wild-type cells transition from early-log to stationary phase were identified. Additionally, transcripts had to be considered present within wild-type stationary-phase samples by Affymetrix GeneChip analysis. From this list, genes that were expressed at twofold-lower levels in stationary-phase sarA mutant cells than in wild-type cells were identified. We rationalized that this decrease in expression within the sarA mutant could most likely be due to either (i) a direct loss of SarA activator function or (ii) an indirect consequence of the sarA mutation, which decreases production of RNAIII and/or other transcriptional activators and reduces transcription of an agr-responsive gene. To distinguish between these two possibilities, transcription profiles (of genes fitting the criteria above) of agr and agr sarA mutant cells were compared. Genes directly activated by SarA could be identified as transcripts showing twofold reduction in stationary-phase agr sarA double-mutant cells relative to agr mutant cells. Several ORFs, including known SarA-activated genes, demonstrated background expression levels in agr mutant cells and were considered potentially activated by SarA (Table 4). Additionally, genes transcribed at twofold-higher levels in stationary-phase agr mutant cells are listed in Table 4. As expected our analysis identified several known SarA-upregulated virulence genes, including agrB, agrD, agrC, agrA, hld, and hla (6, 8, 13, 15). Additionally, other virulence factors, such as gamma-hemolysin, were determined to be upregulated by SarA. Potential SarA contributions, as defined by the above criteria, to the agr-regulated genes are listed in Tables 2 and 3.

TABLE 4.

SarA-upregulated genes

ORFa	Geneb	Patternc	Functiond	N315 ORFe	Role categoryf	Sar boxg
3298	capJ	1 (2)	Capsule gene	SA0153	Adaptation
1321	aroC	1 (1)	Chorismate synthetase	SA1299	Amino acid metabolism
997	hutG	1 (1)	Arginase	SA2125	Amino acid metabolism	Yes
452	metK	1 (2)	S-Adenosylmethionine synthetase	SA1608	Amino acid metabolism
2628	pepF	1 (4)	Peptidase	SA1216	Amino acid metabolism
7	sdhB	1 (2)	l-Serine dehydratase	SA2319	Amino acid metabolism
821		1 (2)	Acetyltransferase	SA1931	Amino acid metabolism	Yes
1765	epiF	1 (2)	Epidermin-like biosynthetic cluster		Antibiotic production
4746	epiP	1 (2)	Serine protease		Antibiotic production
4442		1 (2)	Bacteriophage protein	SA1786	Bacteriophage related
3688		1 (7)	Predicted membrane protein	SA2436	Bacteriophage related
1973	rodA	1 (3)	Cell division membrane protein	SA1888	Cell division
184	femA	1 (2)	mec resistance	SA1206	Cell wall
2497	tagF	1 (2)	Glycosyl/glycerophosphate transferases	SA0244	Cell wall
3495	uppS	1 (4)	Undecaprenyl diphosphate synthase	SA1103	Cell wall
600		1 (3)	Putative glycosyltransferases	SA0523	Cell wall
4757		1 (2)	6-Pyruvoyl-tetrahydropterin synthase	SA0666	Coenzyme metabolism
3095	dnaG	1 (2)	DNA primase	SA1391	DNA replication
3733	BH2391	1 (1)	Short-chain alcohol dehydrogenases	SA1123	Lipid metabolism
3898	crtN	1 (2)	Putative phytoene dehydrogenase	SA2351	Lipid metabolism
797	purA	1 (3)	Adenylosuccinate synthase	SA0016	Nucleic acid metabolism
373	pyrAA	1 (1)	Carbamoyl-phosphate synthetase	SA1045	Nucleic acid metabolism
783	rpmH	1 (1)	Ribosomal protein L34	SAS093	Ribosomal proteins
1118	queA	2 (4)	S-Adenosylmethionine	SA1466	RNA modification
2444	agrA	1 (1)	agr response	SA1844	Signal transduction	Upstream
1426	agrB	1 (1)	agr response	SA1842	Signal transduction	Yes
2443	agrC	2 (2)	agr response	SA1843	Signal transduction	Upstream
1425	agrD	1 (1)	agr response	SAS066	Signal transduction	Upstream
2944	phoP	1 (1)	Response regulator	SA1516	Transcriptional regulation	Yes
5259		1 (3)	Transcriptional regulator, GntR family	SA0434	Transcriptional regulation
2852		1 (2)	Putative transcriptional regulator	SA1120	Transcriptional regulation
1545	prfA	1 (1)	Protein chain release factor A	SA1920	Translation termination
4309	fhuD	1 (2)	Ferrichrome-binding periplasmic protein	SA2079	Transport
614	gltS	1 (1)	Glutamate permease	SA2135	Transport
2601	opp-2B	1 (4)	Putative oligopeptide transporter	SA1214	Transport	Yes
271		1 (3)	Putative permease	SA0099	Transport
272		1 (2)	Putative transport	SA0100	Transport	Upstream
4602		1 (2)	Bmr-like protein	SA2241	Transport
4200		1 (2)		SA0085	Unknown
992		1 (3)	Conserved hypothetical	SA0174	Unknown
243		1 (1)	Conserved hypothetical	SA0350	Unknown
2095		1 (1)	Hypothetical protein	SA0453	Unknown	Yes
2634		1 (3)	Unknown	SA0754	Unknown
4370		1 (2)		SA1037	Unknown
890		1 (2)	Conserved hypothetical	SA1240	Unknown
2479		1 (2)	Conserved hypothetical	SA1275	Unknown
2782		1 (2)	Hypothetical protein	SA1319	Unknown
2785		2 (6)	Conserved hypothetical	SA1388	Unknown
5283		1 (2)	Conserved hypothetical	SA1611	Unknown
5066		1 (2)	Conserved hypothetical	SA1684	Unknown
4929		1 (2)	Hypothetical protein	SA1889	Unknown
5521		1 (2)	Hypothetical protein	SA1928	Unknown
2505		1 (2)	Hypothetical protein	SA1944	Unknown
1555		1 (2)	Acetyltransferase	SA2161	Unknown
9		1 (1)	Hypothetical protein	SA2321	Unknown
37		1 (1)	Hypothetical protein	SA2343	Unknown
3982		1 (2)	Conserved hypothetical	SA2491	Unknown
644		1 (1)	Hypothetical protein	SAS016	Unknown
2105		1 (2)	Conserved hypothetical		Unknown
2275		1 (1)	Hypothetical protein		Unknown
4044	fnbA	1 (7)	Fibronectin binding protein A	SA2291	Virulence factors
3636	fnbB	1 (3)	Fibronectin binding protein B	SA2291	Virulence factors
4061	hla	1 (1)	Alpha-toxin	SA1007	Virulence factors	Yes
4578	hld	2 (2)	Delta-hemolysin	SAS065	Virulence factors	Yes
1928	hlgB	1 (5)	Gamma-hemolysin component B	SA2209	Virulence factors	Upstream
1927	hlgC	1 (2)	Gamma-hemolysin component C	SA2208	Virulence factors	Yes
366	hsa	1 (X)	Homolog of streptococcal hemagglutinin	SA2447	Virulence factors
4374	map	3 (3)	Map protein	SA2006	Virulence factors
1175	pls	1 (2)	Related to Pls and accumulation-associated proteins, LPXTG motif	SA2284	Virulence factors
2343	sdrc	2 (2)	Surface protein (SdrC)		Virulence factors
2037	set9	1 (4)	Staphylococcal exotoxin	SA0385	Virulence factors
5329	splA	1 (2)	Serine protease	SA1631	Virulence factors	Yes
2929	splB	1 (1)	Serine protease	SA1630	Virulence factors	Upstream
324	splD	1 (1)	Serine protease	SA1628	Virulence factors	Upstream
327	splF	2 (2)	Serine protease	SA1627	Virulence factors	Upstream
4523		2 (2)	Phenol soluble modulin beta 2		Virulence factors

^aDesignated S. aureus GeneChip ORF number. 

^bPreviously described gene name. 

^cMany genes are represented on the S. aureus GeneChip in a redundant manner as full or partial fragments. Values are the number of times a gene transcript was identified as fitting a pattern (number of times the gene was either partially or completely represented on the GeneChip). 

^dPreviously described gene product function. 

^eCorresponding designated S. aureus strain N315 gene (20). 

^fExpected metabolic role. 

^gYes, putative Sar box identified within the gene's promoter region. Upstream, the gene is expected to be part of an operon with a potential Sar box located in an upstream promoter region. 

Identification of SarA-downregulated genes.

SarA-downregulated genes were identified as producing at least twofold-higher transcript levels in stationary-phase sarA mutant cells than in wild-type cells. Additionally, genes were considered present in at least one wild-type stationary-phase sample, as determined by GeneChip analysis. Genes with transcript levels meeting these criteria could include (i) genes that are directly repressed by SarA, in which case the absence of SarA allows for increased gene expression, or (ii) genes repressed by RNAIII, whereby decreased agr transcription allows derepression of gene expression. To distinguish between these two possibilities, transcript patterns of these genes were further compared in agr and agr sarA mutant cells. Genes directly repressed by SarA could be identified as producing elevated transcript levels in stationary-phase double-mutant cells compared to agr mutant cells (or at background levels). Table 5 lists genes that were determined to be downregulated by SarA in a cell density-dependent manner.

TABLE 5.

SarA-downregulated genes

ORFa	Geneb	Patternc	Functiond	N315 ORFe	Role categoryf	Sar boxg
2462	clpL	2 (2)	ATPase/chaperone	SA2336	Adaptation
1220	arcC	2 (2)	Carbamate kinase	SA2425	Amino acid metabolism
1191	gudB	1 (2)	Glutamate dehydrogenase/leucine dehydrogenase	SA0819	Amino acid metabolism	Upstream
4470	rocD	1 (3)	Ornithine aminotransferase	SA0818	Amino acid metabolism	Yes
1075		2 (2)	Proline dehydrogenase	SA1585	Amino acid metabolism
1888		1 (2)	Threonine dehydratase		Amino acid metabolism
2369	aldA	1 (3)	NAD-dependent aldehyde dehydrogenases	SA0162	Carbohydrate metabolism
4567	fhs	1 (5)	Formyl-tetrahydrofolate synthetase	SA1553	Carbohydrate metabolism
1416		2 (3)	Dihydroxyacetone kinase	SA0605	Carbohydrate metabolism
5238	atl	1 (5)	Autolysin	SA0905	Cell division
2381	pbp3	1 (2)	Penicillin-binding protein 3	SA1381	Cell wall
4186	thiD	1 (2)	Hydroxymethylpyrimidine/phosphomethylpyrimidine kinase	SA0537	Coenzyme metabolism
2912		1 (3)	Homology to N-carbamoylsarcosine amidohydrolase	SA2438	Coenzyme metabolism
4407	mutL	1 (4)	DNA mismatch repair protein	SA1138	DNA repair
3933	ndhF	1 (3)	NADH-ubiquinone oxidoreductase subunit 5	SA0411	Electron transport
35	rocA	3 (4)	1-Pyrroline-5-carboxylate dehydrogenase	SA2341	Electron transport
4603		1 (3)	Carboxylesterase type B	SA2240	Lipid metabolism
123	IrgB	1 (2)	Holin-like protein	SA0253	Miscellaneous
2047	purM	1 (3)	Phosphoribosylaminoimidazol synthetase	SA0923	Nucleotide and nucleic acid metabolism
1996	citC	1 (1)	Isocitrate dehydrogenase	SA1517	TCA cycle
265		1 (6)	Putative transcription antiterminator, BgIG family	SA1961	Transcription termination
1221	arcR	1 (1)	Transcriptional regulator, Crp/Fnr family	SA2424	Transcriptional regulation
3985		1 (3)	Transcriptional regulator, RpiR family	SA0187	Transcriptional regulation	Upstream
4405	glpF	1 (2)	Glycerol uptake facilitator	SA1140	Transport
2839	gntP	1 (4)	Gluconate permease	SA2293	Transport
205		1 (2)	Phosphotransferase system IIB components	SA0186	Transport	Upstream
4259		1 (2)		SA0021	Unknown
3420		1 (3)	Conserved hypothetical	SA0212	Unknown
2103		1 (2)	Conserved hypothetical	SA0271	Unknown
2424		1 (2)		SA0363	Unknown
100		1 (2)	Conserved hypothetical	SA0412	Unknown
2823		2 (3)	Conserved hypothetical	SA1618	Unknown
827		1 (1)	Conserved hypothetical	SA1937	Unknown
4122		1 (1)	Conserved hypothetical	SA2378	Unknown	Yes
367		1 (2)	Conserved hypothetical	SA2448	Unknown
203		1 (1)	Hypothetical protein		Unknown
2577		1 (2)	Hypothetical protein		Unknown
426	aur	1 (5)	Aureolysin	SA2430	Virulence factors	Upstream
427	isaB	1 (2)	Immunodominant antigen B	SA2431	Virulence factors	Yes
4197	lip	1 (2)	Lipase	SA2463	Virulence factors	Yes
2574	nuc	1 (1)	Nuclease	SA0746	Virulence factors	Yes
4081	spa	2 (2)	Protein A	SA0107	Virulence factors	Yes
2174	sspB	1 (1)	Cysteine protease precursor	SA0900	Virulence factors
2175	sspC	1 (1)	Protease	SA0899	Virulence factors

^aDesignated S. aureus GeneChip ORF number. 

^bPreviously described gene name. 

^cMany genes are represented on the S. aureus GeneChip in a redundant manner as full or partial fragments. Values are the number of times a gene transcript was identified as fitting a pattern (number of times the gene was either partially or completely represented on the GeneChip). 

^dPreviously described gene product function. 

^eCorresponding designated S. aureus strain N315 gene (20). 

^fExpected metabolic role. TCA, tricarboxylic acid. 

^gYes, putative Sar box identified within the gene's promoter region. Upstream, the gene is expected to be part of an operon with a potential Sar box located in an upstream promoter region. 

DISCUSSION

Using oligonucleotide microarray technology for genome-wide transcription profiling, we have identified S. aureus genes with expression patterns expected of genes either up- or downregulated in a cell-density agr- and/or SarA-dependent manner. Given that most known S. aureus virulence factors are temporally regulated by one, if not both, of these effectors, it is likely that the genes identified within this study include previously unrecognized virulence determinants.

Although investigators have successfully used transcription profiling technology to study biological processes in a number of other organisms, we initially set out to validate the methodology used by determining whether the results obtained with the S. aureus GeneChip correlated with our Northern blot data and previous reports. As expected, profile analysis of the effector of the agr response (RNAIII) demonstrated that transcript titers increased in a cell density-dependent manner within wild-type cells but were undetectable in agr mutant strains. Results also confirmed that RNAIII expression is diminished in a sarA mutant, further establishing that SarA is required for wild-type levels of RNAIII production or that the absence of SarA delays the onset of RNAIII expression. Likewise, the transcription profiles of alpha-toxin produced results that are consistent with other reports and confirmed that it is upregulated by each effector. Interestingly, profiling indicated that hla expression in RN6911 cells increased 2.2-fold in a growth phase-dependent manner. This increase was not detected by Northern analysis (Fig. 2B) but has also been reported by others (6), suggesting that the GeneChip technology used may be more reliable than Northern blot analysis. Profiling data for protein A also supported both previous reports, as well as our Northern blot data, and confirmed that genes that are downregulated by each effector can be detected in our system. The protein A transcription profiles indicate that the observed derepression of spa transcription occurs throughout all growth phases of agr and sarA mutant cells, implying that either RNAIII or SarA production may repress expression of an activator of protein A in wild-type cells. Indeed, it has recently been found that either agr or sarA mutant cells produce elevated amounts of SarS (also known as SarHI), an activator of spa transcription (11, 33). More specifically, in those studies it was shown that sarA cells produced more sarS transcripts than did agr mutant cells, which nicely correlates with the spa transcript patterns observed here. In fact, our transcription profiling results indicate that (i) sarS is constitutively expressed at low levels in wild-type cells, (ii) the gene is transcribed above wild-type levels in both RN6911 and ALC842 cells during early growth phases, but transcript levels decrease to wild-type amounts at later growth phases, and (iii) SarS is constitutively expressed at levels 10-fold higher than wild-type levels in sarA cells (data not shown). These results suggest that the mechanisms of sarS derepression differ for sarA and agr cells. This has subsequently been confirmed by additional transcription profiling studies (unpublished data). Results of the present study also demonstrated that the profiles of at least 10 additional genes correlated with the work of other groups, including splA, splB, splC, and splD (31); sarS (11); agrA, agrB, agrC, and agrD (RNAII [7, 13, 19, 27]); and fnbA (35). Collectively these results provide a strong indication that the transcription profiling procedure used correlates well with previous agr and/or sarA studies, and they verify the methodology.

In all, 104 genes were revealed to be upregulated in a cell density- and agr-dependent manner. Among these genes (Table 2) were 20 putative virulence determinants, including 14 genes involved in extracellular factor production. Because their expression corresponds directly with agr transcription, presumably these genes were induced directly in response to RNAIII. In contrast, 34 genes appeared to be downregulated in an agr-dependent manner (Table 3). Two of these genes, spa (protein A) and ORF1941 (cross-reactive antigen), encode putative cell surface virulence factors. One potential extracellular virulence factor, sspA (secretory antigen precursor), was found to be downregulated by agr.

There appears to be a trend in the expression of agr-regulated virulence factors. Collectively our results suggest that an agr determinant upregulates extracellular virulence factors but downregulates cell surface virulence factors. Although this has long been a common hypothesis among investigators, the present body of work provides an unprecedented corroboration of this proposal. An immediate question that remains unanswered is why cell surface virulence factors are expressed during early phases of growth whereas extracellular proteins are produced at higher cell densities.

Traditionally, it has been hypothesized that during early stages of host invasion staphylococci produce MSCRAMMs (microbial surface components recognizing adhesive matrix molecules) and other cell surface proteins, which promote attachment to both host cell and foreign (i.e., catheter) surfaces as well as avoidance of host defensive machinery. Once the organism has reached a critical threshold number, virulence factor expression switches to a more invasive system in which the organism produces extracellular proteins capable of degrading host cells, their products (such as antibacterial agents), or their infrastructure (such as phagocytes). This is an attractive yet, based on our SarA findings, potentially oversimplified hypothesis.

As shown in Tables 4 and 5, 76 genes were found to be upregulated in a cell density- and SarA-dependent manner. Among these genes were 16 putative virulence factors, including 10 extracellular and 6 cell surface virulence determinants. Several known SarA-upregulated genes, such as hla and fnbA, are included in this list (6, 12, 35). A total of 44 genes, including 5 extracellular and 3 cell surface virulence factors, were found to be downregulated in a SarA-dependent manner. These results suggest that SarA-regulated virulence genes do not necessarily follow the reported reciprocal expression patterns that have been established between agr-regulated cell surface and extracellular proteins. Several of the genes identified as being regulated by SarA have been found to harbor putative Sar boxes within their promoter regions, as indicated in each table.

Interestingly, Cheung and colleagues have previously demonstrated that fnbA is regulated by SarA (35). Although attempts were made in that study, no fnbB signal was detected in Northern blot analysis. Conversely, our profiling results suggest that both fnbA and fnbB are expressed in a SarA-dependent manner and indicate that GeneChip technology is more sensitive than Northern blot analysis, in that instance. Similarly, numerous attempts to study the transcription of components of both the arginine deiminase and histidine utilization operons (both of which were identified as potentially being regulated by agr in our transcription profiling studies) by Northern analysis did not demonstrate any signal, further illustrating the sensitivity of the GeneChip technology.

Many of the genes identified as being regulated by agr and/or SarA encode putative virulence factors, yet the majority do not. Although a number of other investigators have proposed that each effector is likely to regulate “nonvirulence genes,” data substantiating this hypothesis are sparse. It is likely that because the GeneChip technology used appears to provide far more sensitivity in studying biological processes than conventional approaches, others have not identified many of the genes presented in the present study as being regulated by each effector. Among genes not previously known to be stimulated in an agr- and cell density-dependent manner were genes encoding the members (arcA, arcB, arcC, and arcD) and the activator (arcR) of the arginine deiminase pathway. Activation of this pathway allows Bacillus licheniformis to grow anaerobically in the presence of arginine (24), an ability that could be beneficial to S. aureus during pathogenesis. Additionally, utilization of arginine via the arginine deiminase pathway produces ammonia, which has been shown to protect bacteria from the deleterious effects associated with acidic environments (4). However, further in vivo studies are required to determine whether components of the arginine deiminase pathway contribute to S. aureus pathogenesis.

Members of the histidine utilization pathway (hutG, hutH, hutI, and hutU), which constitute a single transcript in Bacillus subtilis that is induced primarily by the presence of l-histidine, also appeared to be expressed in an agr- and cell density-dependent manner (14). It is difficult to resolve how transcription of this operon contributes directly to S. aureus pathogenesis. Given the number of genes upregulated by each effector that are involved in amino acid metabolism and transport pathways, it is tempting to speculate that each regulator not only mediates virulence factor production but may also poise the cell to scavenge for nutrients. Another alternative is that proteases that are directly produced in response to agr and/or SarA degrade secreted S. aureus factors and activate transport and metabolic processes; as a consequence, these processes may be indirectly inducible by each effector in laboratory cultures. Moreover, it is likely that unrecognized agr- and/or SarA-mediated processes that occur during mid-log-phase growth perturb the cell and affect biological processes, which in turn mask the relevance of biological processes determined to be regulated by each effector in the present body of work.

A number of genes identified as regulated by either agr or SarA or both have not been previously characterized. It is likely that some, if not many, of these genes contribute to S. aureus pathogenesis. Moreover, understanding the functions of these genes may further clarify the biological processes identified here as being stimulated by either effector. Finally, it is important to recognize that pathogenesis is not likely to be a static process; rather, the invading bacterium must cope with fluxes in its immediate environment, such as changes in pH, changes in nutrient levels, differences in cell densities, and interactions with host factors. Therefore, the work presented here should be considered a snapshot of gene expression and a cataloging of the network of genes that are expected to be regulated by each effector in a cell density-dependent manner. We are actively characterizing networks of genes that are regulated by other elements, such as host factors, in an effort to better understand the pathogenic processes of staphylococci.

Because this technology provides transcriptional profiles for each gene tiled onto a gene chip, yet analysis of each individual gene is beyond the scope of this report, we have elected to provide this data to the scientific community, so that others may analyze genes involved in other biological processes. The transcriptional profiling data obtained in these studies can be accessed at the Network on Antimicrobial Resistance in Staphylococcus aureus (NARSA) website (http://narsaweb.narsa.com).