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Journal of Bacteriology, November 2005, p. 7826-7839, Vol. 187, No. 22
0021-9193/05/$08.00+0     doi:10.1128/JB.187.22.7826-7839.2005
Copyright © 2005, American Society for Microbiology. All Rights Reserved.

Catabolite Repression and Activation in Bacillus subtilis: Dependency on CcpA, HPr, and HprK

Graciela L. Lorca,1,{dagger} Yong Joon Chung,1,{ddagger} Ravi D. Barabote,1 Walter Weyler,2 Christophe H. Schilling,1,§ and Milton H. Saier Jr1*

Division of Biological Sciences, University of California at San Diego, La Jolla, California 92093,1 Genencor International, Inc., Palo Alto, California 943042

Received 1 July 2005/ Accepted 22 August 2005


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ABSTRACT
 
Previous studies have suggested that the transcription factor CcpA, as well as the coeffectors HPr and Crh, both phosphorylated by the HprK kinase/phosphorylase, are primary mediators of catabolite repression and catabolite activation in Bacillus subtilis. We here report whole transcriptome analyses that characterize glucose-dependent gene expression in wild-type cells and in isogenic mutants lacking CcpA, HprK, or the HprK phosphorylatable serine in HPr. Binding site identification revealed which genes are likely to be primarily or secondarily regulated by CcpA. Most genes subject to CcpA-dependent regulation are regulated fully by HprK and partially by serine-phosphorylated HPr [HPr(Ser-P)]. A positive linear correlation was noted between the dependencies of catabolite-repressible gene expression on CcpA and HprK, but no such relationship was observed for catabolite-activated genes, suggesting that large numbers of the latter genes are not regulated by the CcpA-HPr(Ser-P) complex. Many genes that mediate nitrogen or phosphorus metabolism as well as those that function in stress responses proved to be subject to CcpA-dependent glucose control. While nitrogen-metabolic genes may be subject to either glucose repression or activation, depending on the gene, almost all glucose-responsive phosphorus-metabolic genes exhibit activation while almost all glucose-responsive stress genes show repression. These responses are discussed from physiological standpoints. These studies expand our appreciation of CcpA-mediated catabolite control and provide insight into potential interregulon control mechanisms in gram-positive bacteria.


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INTRODUCTION
 
Catabolite repression (CR) and catabolite activation (CA) of gene expression are universal phenomena found in virtually all living organisms (39, 59). However, the mechanisms employed vary tremendously depending on the organism and gene (61). Large genome bacteria such as Escherichia coli and Bacillus subtilis exhibit multiple mechanisms of catabolite control (4, 60, 62). In low G+C gram-positive bacteria, including Bacillus subtilis, one primary mechanism involves the catabolite control protein A, CcpA, a transcription factor of the LacI-GalR family that is regulated by a complex sensory transduction pathway (40, 51, 69-72, 76). This pathway involves two glucose transporters (50), the enzymes of glycolysis (43, 44, 49), an ATP-dependent, metabolite-activated protein kinase, HprK (10, 12, 32, 48, 56, 57), two small protein targets of HprK action, HPr (the small phosphocarrier protein of the phosphoenolpyruvate:sugar phosphotransferase system [PTS]) and Crh (an HPr homologue) (10, 15, 16, 35, 41, 58), and the aforementioned transcription factor, CcpA (24, 25, 45), which binds to a DNA sequence in the control region of a target gene, a catabolite-responsive element (CRE) (26, 46).

CcpA mediates both CA and CR (21, 23, 74), but very few genes exhibiting CcpA-dependent CA have been studied. In fact many genes subject to CA lack a CRE, suggesting the mechanism established for CR is insufficient to explain CA (3, 47). The established sensory transduction pathway recognized for CcpA-mediated CR is depicted schematically in Fig. 1. This mechanism involves the phosphorylation of HPr or Crh on a seryl residue catalyzed by the HprK kinase and the subsequent interaction of these phosphorylated proteins with CcpA to promote DNA binding.



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  		FIG. 1. Proposed sensory transduction pathway by which exogenous glucose is believed to activate the CcpA transcription factor to promote catabolite repression (CR) or catabolite activation (CA) by binding to a catabolite responsive element (CRE) in the control region of a target gene. Proteins primarily involved are (i) the glucose phosphotransferase system, including enzyme I, HPr, and IIBCAGlc, (ii) glycolytic enzymes, (iii) the ATP-dependent fructose 1,6-bisphosphate-dependent HPr/Crh kinase/phosphatase HprK, and the two small PTS protein targets of HprK that independently bind to CcpA to activate it for binding to CREs, HPr and Crh (see references 4, 25, 51, 60, 62, 69, 70, and 77 for reviews).

The CR mechanism shown in Fig. 1, in which CcpA binds to CRE sites only when serine-phosphorylated HPr [HPr(Ser-P)] or Crh(Ser-P) is complexed to it, is generally accepted (2, 11, 14, 15, 25, 41, 60, 69, 70). However, several reports have indicated that CcpA-mediated mechanisms of CR and CA are complex. (i) Convincing evidence suggests that the putative 14 bp CRE sequence is by itself insufficient to promote strong CR (46, 82). The composition of the flanking DNA sequences and also the position of that sequence relative to the promoter proved to be important (74, 82). In contrast, Kraus et al. (31) reported that the genomic context is not relevant. (ii) Presecan-Siedel et al. (52) reported that the presence of fructose-1,6-bisphosphate was alone without effect on the binding of CcpA to the DNA, but that it enhanced binding of the CcpA-HPr(Ser-P) complex to the pta gene control region (see also reference 15). (iii) In DNA footprint analyses, Kim et al. (30) reported that neither NADP nor fructose-1,6-bisphosphate alone could promote binding of CcpA to the amyO control region in the absence of HPr(Ser-P) and noted synergism of these two agents with HPr(Ser-P). (iv) Gösseringer et al. (19) reported cooperative and noncooperative DNA binding modes of CcpA to the xyl control region dependent on glucose and experimental conditions.

(v) Turinsky et al. (74) reported that two CRE sites are present in the control region of ackA, a gene that is subject to CA. However, only the CRE2 sequence that is closer to the promoter and the region upstream of CRE2 was shown to be required for CcpA-dependent transcriptional activation of ackA while CRE1 was not required. Not all predicted CRE sites have proven to be active (46, 74). (vi) Our earlier whole genome transcriptome analyses (47) have shown that the control and coding regions of many operons subject to CR exhibit multiple putative CRE sites (as many as eight per operon). Multiple functional binding sites for the homologous LacI, GalR and Cra proteins are well established (1, 53, 54). An analogous situation has been demonstrated for the mtl operon of E. coli where five CRP binding sites in the control region of the operon are functional (55). The importance of at least two CRE sites for CR of the xyl and gnt operons in Bacillus has been demonstrated (19, 45). (vii) Several other proteins have been shown to influence CR in B. subtilis, at least some of them by a CcpA-dependent mechanism. These include the AbrB transition state regulator (13, 65), the Mfd transcription-repair coupling factor (81), several operon-specific transcriptional activators and antiterminators (17, 66, 68, 69) and, the homologue of CcpA, CcpB (6). (viii) CcpA controls the gapA operon by an indirect mechanism that does not involve binding to a gapA operon CRE site (38, 42). (ix) CcpA mediates growth control due to effects on amino acid biosynthesis, thus providing a link between carbon and nitrogen metabolism (37, 75). (x) Effects of hprK mutations alter growth independently of their effects on CR (22). (xi) Several reports have demonstrated the existence of CcpA-independent CR in B. subtilis (7, 8, 64, 73, 78, 80).

All of these observations suggest that multiple mechanisms of CR/CA in B. subtilis exist, and that the CcpA-dependent mechanism shown in Fig. 1, while being correct in principle, is likely to be substantially more complex.

In this paper we report the effects of loss of CcpA, HprK and the HprK phosphorylatable serine in HPr (PtsH1) on the expression of genes in B. subtilis under uniform growth conditions in complex liquid medium, both in the presence and absence of glucose. We demonstrate remarkable consistency of glucose effects: genes encoding glycolytic enzymes involved in triose-phosphate metabolism, ribosomal proteins and RNA polymerase subunits are generally subject to weak CA while genes encoding proteins that initiate carbon metabolism, Krebs cycle enzymes and flagellar proteins are subject to CR. Electron transfer protein-encoding genes may be subject to either CR or CA, depending on the gene. We show that the CR regulated genes are subject to coordinate CcpA and HprK control while the CA regulated genes are not subject to coordinate control. Moreover, we identify sets of nitrogen and phosphorus metabolic genes as well as stress genes that exhibit CcpA-dependent expression, and their dependencies on HPr and HprK are presented. Surprisingly, we show that while nitrogen metabolic genes may be subject to either CA or CR, virtually all phosphorus metabolic genes are subject to CA while almost all stress response genes are subject to CR. The work reported greatly expands our appreciation of the scope of and mechanism of CcpA-dependent glucose regulation and provide clues to the mechanisms responsible for interregulon control in B. subtilis.


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MATERIALS AND METHODS
 
Bacterial strains and their construction. The bacterial strains used in this study include ST100 (wild type) (6), ST101 (ccpA), ST105 (ptsH1), and ST106 (hprK) (this study). ST106 was obtained by transfer to the hprK::spc mutation from the G202 genetic background into the ST100 background by transformation. All isogenic strains were constructed in a single genetic background (ST100) that included the spo0E mutation which prevents entry of cells into the sporulation cycle. The insertion mutations introduced were all confirmed by PCR.

Growth conditions (LB medium at 37°C, 250 rpm, with cells harvested during exponential growth [optical density at 600 nm = 0.5]) were as described by Moreno et al. (47) with the rationalization as described therein. These conditions were used throughout our studies with all mutant strains in order to provide a basis for direct comparison. For measurement of swarming, bacteria were stabbed into soft agar (LB plus 0.35% agar with and without glucose at 0.4%) and incubated at 30°C for 48 h.

DNA array design. The Bacillus subtilis antisense DNA arrays used in this work were custom designed and manufactured by Affymetrix (Santa Clara, CA). All open reading frames (ORFs) published by Kunst et al. (33) are represented on the array as a collection of 25-mer oligonucleotides chosen as described in Lockhart et al. (36) using proprietary procedures (Affymetrix, Santa Clara, CA). On the average, every 1500 bp of each ORF is represented by 20 unique oligonucleotide probe pairs analogous to work described previously (36, 79). The minimal number of probe pairs per ORF is 20. The antisense Bacillus subtilis custom GeneChip is a "high"-density array with each probe feature measuring 24 µm by 24 µm. Antisense GeneChip indicates that cDNA rather than mRNA is hybridized to the array.

RNA extraction and cDNA labeling. Total RNA was extracted from cells as described by Moreno et al. (47). cDNA was labeled with biotinylated-dATP as described by de Saizieu et al. (9). The protocol was scaled down to 20 µg of starting RNA, and the amount of Superscript II reverse transcriptase (GIBCO/BRL Life Tech 18064-022) was reduced to 100 U/µg RNA. The buffer supplied with the reverse transcriptase was used at a 1x concentration. Random hexamer primer (GIBCO/BRL LifeTech 48190-011), dithiothreitol, deoxytrinucleotides, and biotin-dATP (NEN LifeScience Products NEL-506 -special order 10 mM solution) concentrations were as stated.

After standard DNA precipitation, cDNA was taken up in 30 µl of 10 mM Tris acetate (Tris acetate), pH 7.5, and 12 µg of product was fragmented with 0.026 U of DNase I (Roche/Boehringer 776 785). Fragmentation was carried out in 10 mM Tris.acetate, pH 7.5 (pH adjusted at 100 mM concentration and at room temperature), 50 mM K OAc, and 10 mM Mg OAc in a final volume of 33 µl. Incubation was for 30 min in a water bath set at 25°C followed by 90°C for 10 min to inactivate the DNase I. The optimal fragment distribution for hybridization was obtained when about 60% of the fragments fell between 20 and 200 bp with the fragment size distribution centered at about the 70-bp size marker (DNA ladders, 10 and 100 bp, GIBCO/BRL LifeTech 10821-015 and 15628-019, respectively). This protocol was optimized with respect to DNase I concentration for each lot of DNase used. Fragment size analysis was carried out on precast 1-mm-thick Novex 4 to 20% Tris-borate-EDTA acrylamide gels (Invitrogen, USA) run at 150 mV (constant), developed with SYBR Green I (Molecular Probes, USA), and analyzed with a Molecular Dynamics Storm Imager and ImageQuant v.5 software. The resulting DNA fragment solution was used without further treatment in the hybridization to the DNA array.

Hybridization, staining, and scanning procedures. Hybridization, staining, and scanning procedures were carried out as described by de Saizieu et al. (9) with modifications as described in Caldwell et al. (5) and noted here. Total fragment amounts hybridized to each array were 8 to 10 µg delivered to the array in a volume of 200 µl. Hybridization was carried out for 20 to 24 h at 40°C while rotating the array at 60 rpm. At the end of this period, the hybridization solution was replaced with fluidics station wash buffer, and the array was washed and stained on the semiautomated Affymetrix GeneChip Fluidics Station 400 employing the manufacturer's EukGE-WS2 protocol. Briefly, staining is a three-step procedure. Streptavidin is first bound to biotin, introduced during cDNA synthesis. This is followed by conjugation of poly biotinylated antistreptavidin antibody, and finally by binding of streptavidin coupled to phycoerythrin to the secondarily introduced biotin molecules. Phycoerythrin is the fluorophor providing detection. After washing and staining is complete, arrays are scanned at 570 nm and 3-µm resolution with the Hewlett Packard GeneArray scanner to produce an image for fluorescence intensity analysis. Affymetix recently suggested and we implemented a gain reduction for the scanner to overcome saturation of the signal. This modification leads to a 10-fold reduction in signal size and has eliminated nonlinearity of the highest signals. There is no loss in sensitivity as the average background of the array also drops by a factor of about 10. All data for this work were collected with the new gain setting.

Data manipulations. All experimental time points were collected and analyzed in triplicate. Images were analyzed with Affymetrix Software to provide raw expression intensity values by a procedure previously described (9, 36). The raw data were imported into an Excel spreadsheet for further manipulation. Intensity values range between –1 x 101 and –3 x 101 and between 3 x 104 and 5 x 104. All intensities of ≤5 were replaced by the value of 5, and all data sets were scaled to a median value of 500. Triplicates were averaged, and expression ratios of averages were calculated for relevant experiments. Only genes that exhibited a signal intensity equal to or greater than 120 and minimally a threefold effect of glucose were included in this study.

Statistical methods. Each experiment was carried out in triplicate shake flasks. Chip results were compared pair-wise using the Pearson function in Microsoft Excel using the formula r = [n({Sigma}XY) – ({Sigma}X) ({Sigma}Y)]/{[n{Sigma}X2 – ({Sigma}X)2][n{Sigma}Y2 – ({Sigma}Y)2]}1/2. This gave r values for regression lines ranging from 0.905 to 0.993. Samples leading to r values of less than 0.967 were rejected. Figure S1 shows scatter plots for the best-correlated samples (r = 0.993) as well as the worst-correlated samples (r = 0.967) used to average either duplicates or triplicates to estimate expression level (see Fig. S1 on our website http://biology.ucsd.edu/~msaier/regulation2/). The observed scatter of the data is consistent with the capability of the current Affymetrix chip method (34). The raw data for all 4,098 genes of B. subtilis upon which the results presented in this report are based can also be found on this website.

CRE search of the B. subtilis genome. The B. subtilis genome was screened for CRE-like sequences using the GRASP-DNA program (63). This program was designed to screen fully sequenced prokaryotic genomes for putative DNA-binding sites of a particular transcription factor or of other DNA-binding molecules. We used eight well-known, experimentally established CRE consensus sequences of 14 bases (those located in acsA, acuA, amyE, gntR, hutP, licS, xylA, and ackA) to generate the consensus sequence (in which all nucleotides at a particular position are weighted according to frequency) that was used to search for CRE-like sequences in the B. subtilis genome. This program ranks each sequence retrieved according to the degree of approximation to the weighted consensus sequence and indicates the position of the retrieved sequence in relation to the start site of the two nearest genes (63). These data are included in Table 2, 3, 5, and 6 here and Tables S3 and S4 on our website.


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  		TABLE 2. Glucose-repressed genesa


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  		TABLE 3. Glucose-activated genesa


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  		TABLE 5. Expression of genes encoding enzymes of glycolysis, the pyruvate dehydrogenase complex, and the Krebs cyclea


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  		TABLE 6. Electron transfer carrier-encoding genes

Reverse transcription-real-time quantitative PCR. cDNAs were synthesized using the Superscript first-strand synthesis kit (Invitrogen) exactly according to the manufacturer's instructions and stored at –20°C. Real time quantitative PCR was carried out on the LightCycler instrument (Roche Diagnosis Corporation) following the manufacturer's recommended protocol. Their recommended data analysis procedure was also used. Amplification of the hisS and yacO genes was used as internal controls. These two genes were chosen since both showed high level and comparable expression both in the microarray data and in the reverse transcription (RT)-PCR.

Agreement between microarray and classical data. We have compared our microarray data with values obtained by conventional means for the 23 genes included in Table 8 of Moreno et al. (47). Of these, only four genes (acsA, amyE, mtlA, and yobO) showed discrepant wild-type/ccpA ratios in the presence of glucose using the two methods. Examination of the absolute signals for the microarray experiment revealed that for all four of these discrepant genes, the intensity values were far below (≤325) the threshold (≥1000) used in our studies (see Table S5 on our website). In fact, the signals observed for these four genes were the lowest of the 23 examined. These low signals provide an explanation for the few discrepancies observed.


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  		TABLE 8. Comparison of induction ratios (LB with glucose/LB) obtained by RT-PCR and microarray hybridization for representative glucose-repressed genesa


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RESULTS
 
Statistical analysis of CR and CA. Table 1 presents the numbers of genes that are subject to >3-fold regulation by glucose in wild-type B. subtilis and shows the effects of null mutations in various genes known to be concerned with CR and CA in this organism. Genes subject to CR in the wild-type (ST100) genetic background are about twice as numerous as those subject to CA. Most of the genes subject to CR (>96%) are subject to CcpA control. Thus, the ccpA mutation usually abolishes CR. Loss of serine-46 in HPr (HPr S46A; ptsH1) (6) resulted in a significant decrease in the numbers of genes subject to glucose control, but the hprK mutation essentially eliminated the glucose responses.


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  		TABLE 1. Numbers of genes subject to regulation by glucosea

The results reported in this paper were all conducted with cells grown under uniform conditions in rich LB medium. It is important to note that many genes are subject to multiple forms of regulation and that no single growth condition can be expected to reveal all of these regulatory features. For example, genes may be induced or repressed by components in LB medium, while others are not induced because the requisite inducers are either lacking or are antagonized by constituents of the LB medium. Thus, under other conditions, genes not subject to regulation under the conditions used here may exhibit regulatory properties and vice versa.

The B. subtilis genome sequencing group (33) designated all functionally uncharacterized genes with a three-letter code starting with y (e.g., ysf), but functionally characterized genes were designated according to the conventions used previously. Thus, the latter genes do not have a y designation. We conducted statistical analyses of all genes starting with y compared with all other B. subtilis genes and found that for all strains studied, the average apparent expression level for functionally characterized genes is about 2.7x higher than for the uncharacterized y genes, regardless of whether cells were grown with or without glucose. The presence of glucose did not appreciably alter the average expression level for either class of genes. Specific hybridization efficiencies for the various genes should average out when large numbers of genes are considered, rendering these observations biologically meaningful. Thus, these results show that on the average, functionally characterized genes are more highly expressed than uncharacterized genes. Almost all subsequent analyses reported here concern only the former group of genes.

Glucose-repressed genes exhibiting a >3-fold dependency on glucose. Table 2, presents a list of functionally characterized glucose-repressed genes which exhibit high signals and a greater than threefold dependency on glucose. The table presents the gene designations (first column) and the gene functional assignments (column labeled "Product"). The relative hybridization/expression ratios ± glucose are presented for wild-type (WT) cells (ST100) (column 2), the ccpA mutant (ST101) (column 3), the ptsH1 mutant (ST105) (column 4) and the hprK mutant (ST106) (column 5). Binding sites identified for CcpA are presented in the right hand column. The same general format of presentation is used for Tables 3 to 6, below and for Tables S1 to S4 on our web site (http://biology.ucsd.edu/~msaier/regulation2/). It should be noted that most of the genes tabulated encode proteins concerned with carbon and energy metabolism. The excellent agreement observed for different genes within single operons is also noteworthy (3, 47, 80).

Table 2 also records the effects of the ptsH1 and hprK mutations on CR. While the hprK mutation abolishes CR for most genes, the ptsH1 mutation usually reduces the magnitude of CR without abolishing it. Residual CR in the ptsH1 mutant is presumably due to the activity of Crh(Ser-P) (16, 41). A few anomalous values are reported (e.g., for bofC, csbX, glp, and opp).

Glucose-activated genes exhibiting a >3-fold dependency on glucose. Table 3 lists functionally characterized genes that exhibit a >3-fold dependency for glucose activation in the wild-type genetic background. The majority of genes concerned with respiratory, carbon and energy metabolism are regulated fully by CcpA, but genes concerned with purine and pyrimidine biosynthesis are not. Glutamate synthase is regulated by CcpA (see also reference 75). It is interesting that the hprK mutation abolishes and often reverses CA while the ptsH1 mutation has a less dramatic and variable effect.

Dependency of CR and CA on CcpA and HprK. If the primary mechanism of CcpA action requires the participation of the coeffectors HPr(Ser-P) and Crh(Ser-P), both of which are phosphorylated exclusively by HprK, then there should be a good correlation between CcpA- and HprK-dependent regulation. This possibility was tested for several different classes of genes: (i) all genes, (ii) nitrogen metabolic genes, (iii) phosphorus metabolic genes, and (iv) stress-related genes. For this purpose, we selected genes that showed (i) a high signal in the microarray data (see reference 83), and (ii) at least a twofold change in response to glucose in the wild-type genetic background. In all cases we separated the genes subject to CR from those subject to CA.

When all genes subject to strong CR or CA were analyzed, the results shown in Fig. 2A and B were obtained. For the glucose-repressed genes, regression analysis revealed an excellent linear correlation between the CcpA- and HprK-regulated genes (Fig. 2A) with only three genes (oppA, xylA, and ydaS) showing markedly deviant behavior. The line made to intercept the origin was almost identical to the best-fit line, and the R2 values were therefore the same. The R2 value of 0.59 indicates a fairly good fit of the lines to the data. These observations clearly argue that all or at least most of the glucose-repressed genes utilize a mechanism involving both CcpA and HprK.



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  		FIG. 2. Correlation between CcpA- and HprK dependencies for the glucose effects on gene expression using regression analysis. All genes showing greater than 2-fold glucose effects with large signals (83) were used for the analysis. A: Catabolite-repressed genes; B: catabolite-activated genes. Derived averaged values obtained from the microarray experiments were plotted as indicated on the x and y axes. Best-fit lines (dashed lines) as well as best-fit lines that pass through the origin (solid lines) are shown. The R2 values for each line are presented. Larger R2 values (maximal value of 1.0) indicate greater conformity of the lines to the data.

When the same analysis was performed for genes exhibiting strong CA, a very different result was obtained (Fig. 2B). The points representing the different genes scattered widely, and the best-fit line was very different from the one obtained when it was forced to pass through the origin. Further, the R2 value for the two lines were poor, the former being 0.0001, the latter being of a negative value. We conclude that most genes subject to CA were not regulated by a mechanism coordinately dependent on both CcpA and HprK. A similar suggestion has been made by Blencke et al. (3).

When the nitrogen metabolic genes were subjected to the same type of analysis the glucose repressed genes again showed an excellent parallel dependency on CcpA and HprK (Fig. 3). The R2 values were 0.84 to 0.85 regardless of whether the line was forced to go through the origin. However, the glucose-activated genes showed a poorer correlation with lower R2 values (see Table 4).



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  		FIG. 3. Correlation between CcpA and HprK dependencies for nitrogen-metabolic genes subject to CR. The format of presentation is as in Fig. 2.


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  		TABLE 4. Correlation of CcpA and HprK dependency for various classes of genes subject to either CR or CA

The other classes of genes analyzed were phosphorus metabolic (pho) genes and stress response gene (Table 4). Almost all pho genes were activated by glucose; only two were repressed. For the former genes, a negative R2 value indicated a poor correlation. For the stress-related genes, the R2 values were low for both the glucose-repressed and glucose-activated genes. It is therefore probable that CcpA and HprK function primarily to mediate CR of carbon and nitrogen metabolic genes. Regulation of other classes of genes may be more complex.

Functional classes of carbon metabolic genes subject to CR or CA. We specifically analyzed expression of groups of key genes that are central to bacterial physiology and function together in a unified fashion (Tables 5 and 6, also Tables S1 to S3). Table 5 shows that the lower branch of glycolysis and the pyruvate dehydrogenase complex genes are weakly activated by glucose while the Krebs cycle genes are strongly repressed. The results obtained for the glycolytic gapA operon are in agreement with those reported by Ludwig et al. (38) and Meinken et al. (42). The ccpA and hprK mutations abolished all glucose effects, but the ptsH1 mutation selectively abolished regulation of the pdh genes without showing appreciable effects on the glycolytic and Krebs cycle genes.

Table 6 reveals that some electron carriers are subject to glucose activation while others are subject to glucose repression. For example, the cydAB operon, the narGHI operon and the hmp gene are positively regulated while the qcrABC and ctaCDEF genes are repressed by glucose. Interestingly, the ctaA and ctaB genes are coordinately regulated quite differently from the ctaCDEF genes. The implication is that ctaAB comprise an operon distinct from ctaCDEF. This result is contrary to the conclusion of Liu and Taber (35). Most of the glucose effects (CR and CA) on electron transport carrier genes are abolished by the ccpA mutation. The ptsH1 and hprK mutations diminish, abolish or reverse these glucose effects (see Table 6).

Regulation of nitrogen metabolic genes. Nitrogen metabolic genes exhibiting large signals and showing about 2-fold or greater responses to glucose are tabulated in Table 7. For the glucose-repressed genes, the degree of repression varied between about 2-fold and 10-fold (see also Fig. 3). The largest category of nitrogen genes subject to CR encode catabolic enzymes, although many encode biosynthetic enzymes and several encode proteins that cannot be categorized as either catabolic or anabolic (Table 7). In most cases, the ccpA and hprK mutations had similar effects, abolishing CR. The ptsH1 mutant often exhibited residual CR as expected since Crh(Ser-P) can often substitute for HPr(Ser-P) (18, 41, 77). In just two cases (ansA and ansB encoding asparaginase), neither the ccpA nor the hprK mutation diminished the intensity of CR. Relief of CR by the ptsH1 mutation for these genes is anomalous.


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  		TABLE 7. Responses of nitrogen-metabolic genes to glucose CR or CA

Of the nitrogen-metabolic genes subject to CA, a large majority encode biosynthetic enzymes (Table 7). All of these genes exhibit a diminution of the CA response in both the ccpA and the hprK genetic backgrounds, and in only one case (glnR) does the ptsH1 mutation have a negligible effect. It is therefore clear that these proteins influence CA as well as CR, even through the data do not allow us to establish direct versus indirect effects.

The dramatic effect of glucose and the ccpA mutation on gltAB expression has been studied by Wacker et al. (75). These investigators concluded that glucose activation of this operon depends on cytoplasmic metabolites of glucose. Further, the ccpA mutation prevents accumulation of these intermediates because of defective activity of the glucose phosphotransferase system (75).

Regulation of phosphorus metabolic genes. No phosphorus metabolic genes proved to be subject to strong CR, but many of these genes, particularly purine and pyrimidine biosynthetic genes, were subject to strong CA. For all of these genes, the ccpA mutation diminished the glucose effect, but it still exhibited appreciative CA suggesting that CcpA does not alone mediate these responses (see Table S1 on our website). The pur genes were in general sensitive to the ptsH1 mutation, but the pyr genes were not. The mechanistic implications of these findings have yet to be investigated.

Regulation of iron metabolic genes. We have demonstrated a strong expression dependency of many iron uptake and metabolic genes in E. coli on the cyclic AMP receptor protein, the principal transcriptional carbon regulator in this organism (83). We examined recognized iron metabolic genes in B. subtilis and found relatively few genes to be regulated by CcpA and glucose. The most striking examples were (i) the siderophore 2,3-dihydroxybenzoate synthesis genes dhbABCEF, which were subject to about 3-fold CcpA-dependent glucose repression, and (ii) the iron uptake system genes feuABC, which were subject to about 2-fold CcpA-dependent glucose repression. Other known or putative iron genes, fhuBCDG, yclNOPQ, yfhAC, yfiYZ, and yusV, either showed little response to glucose or exhibited signals too low to trust.

Regulation of stress response genes. In contrast to the pho genes, almost all stress genes subject to glucose regulation were repressed. Only four stress genes showed CA, and without exception, CA of these genes was diminished and largely abolished by the ccpA ptsH1 and hprK mutations (see Table S2). Of the many stress genes subject to CR, almost all were subject to apparent control by the CcpA/HPr(Ser-P)-dependent control mechanism (Table S2).

Glucose effects on genes encoding ribosomal proteins and RNA polymerase subunits. Table S3 on our website summarizes the glucose effects for 30 ribosomal protein genes and 4 RNA polymerase subunit ({alpha}, ß, ß', and {delta}) genes. These genes are consistently activated by glucose in the wild-type strain (average 64%), and these effects are largely abolished in the ccpA mutant. The ptsH1 and hprK mutations diminished and abolished, respectively, the glucose effects on virtually all of these genes. It is important to note that transcriptome data do not reveal whether the effects observed are primary or secondary consequences of the mutations studied. Although potential CcpA binding sites were identified in a few of the ribosomal protein genes (see Table S3), secondary effects such as effects due to altered growth rate may in part account for the glucose activation observed (38).

Glucose effects on genes encoding chemotaxis and flagellar proteins. Table S4 summarizes the glucose effects on 42 chemotaxis (che and mcp) and flagellar (flg, flh, fli, hag and mot) genes. These genes are consistently repressed by glucose in the wild-type strain, on an average to 42% of the nonrepressed level. CR is fully reversed by the loss of CcpA. Similarly, loss of HprK fully abolished CR while the loss of HPr partially reversed CR. It should be noted that three putative CRE sites were identified near flagellar/chemotaxis genes (cheR, flhF, and mcpB; see Table S4), but many of the CR responses observed could be secondary consequences of altered PTS sugar transport (38).

We examined wild-type and isogenic mutant strains of B. subtilis for swarming in soft agar (LB medium plus 0.35% agar) (35). The wild-type and ptsH1 mutants showed comparable motility (colony diameters: 3.7 ± 0.2 and 3.5 ± 0.3 cm, respectively), but mild repression of swarming in the presence of glucose was observed (colony diameters: 2.5 ± 0.1 and 2.7 ± 0.2 cm, respectively). Under the same conditions, swarming by the ccpA and hprK mutants was largely abolished (colony diameters: 1.5 ± 0.1 and 1.7 ± 0.3 cm, respectively), while the presence of glucose in the soft agar may have increased the motility slightly (1.6 ± 0.1 and 1.9 ± 0.1 cm for the ccpA and hprK mutants, respectively).

Confirmation of microarray data using real time quantitative PCR. We confirmed the results for nine representative genes obtained with the microarrays using RT-PCR. Table 8, summarizes the data for five glucose-repressed genes, while Table 9 shows the data for four glucose-activated genes. The results of the two methods are in good agreement.


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  		TABLE 9. Comparison of induction ratios obtained by RT-PCR and microarray hybridization for glucose-activated genes


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DISCUSSION
 
In this paper, we report analyses of relative gene expression levels when wild-type and various isogenic mutant B. subtilis cells known to be defective in CR or CA are actively growing in LB with and without glucose. Many of the genes examined had not been studied previously using conventional approaches although some of them had been reported by Moreno et al. (47) and Blencke et al. (3) using different technologies or different growth conditions and fewer mutant strains. The mutants examined in this study possessed defects in the ccpA, ptsH, and hprK genes. Of these, the ccpA and hprK mutants are knockouts, but in the ptsH1 mutant, the serine phosphorylation site is changed to alanine.

Several important cautionary technical conclusions can be drawn from the microarray data presented here and on our website. (i) For highly expressed genes in wild-type B. subtilis, there is reasonable agreement between Affymetrix chip data and Sigma-Genosys membrane data (compare the results reported here with the data in reference 47; see also references 3 and 80). The excellent agreement between the chip data and the RT-PCR results as reported here and by Gosset et al. (20) and by Zhang et al. (83) also provides confidence that the results are trustworthy. (ii) Ratios of values are in general more reliable than absolute values, and (iii) ratios of values obtained for a single strain grown in LB with and without glucose are more reliable than ratios obtained when comparing two different isogenic strains under the same growth conditions (iv) Confidence is bolstered when functionally related genes show similar trends (v) Functionally characterized genes are in general more highly expressed than uncharacterized genes. Many of the latter genes may be either cryptic or expressed under selective conditions not represented under our standard laboratory conditions. (vi) Ratios of twofold or more for highly expressed genes are reliable; however, when many genes giving lower responses are coordinately regulated, meaningful data can clearly be obtained with lower ratios. (vii) It is important to remember that while transcriptome data provide an overview of regulatory phenomena, as exemplified by the present study, they do not, by themselves, allow detailed conclusions regarding mechanism. Most importantly, they cannot be used to distinguish primary from secondary effects without further information.

In addition to the technical conclusions discussed above, several basic microbiological questions, relevant to Bacilli, and probably to other low G+C gram-positive bacteria, have been answered by the use of the transcriptome approach presented here. (i) Carbon metabolic genes are primarily regulated in response to glucose by CcpA, but the correlation between the CcpA- and the HprK dependencies was good for genes subject only to CR, not to CA. This fact suggests that genes subject to CR may be primarily regulated by CcpA complexed to HPr(Ser-P) or Crh(Ser-P), but that genes subject to CA may be regulated primarily by other mechanisms. (ii) Whole operons and sometimes whole regulons often exhibit similar regulatory responses, both to glucose and to the loss of the various transcription factors and coeffectors. This fact suggests that transcriptome data can provide evidence favoring a specific operonic organization and can clearly be used to define the extent and targets of regulons controlled by specific pleiotropic transcriptional regulators. The consistency of the results also provides confidence in the transcriptome approach.

(iii) While nitrogen-metabolic genes showed comparable numbers exhibiting CR versus CA (Table 7), almost all glucose regulated phosphorus metabolic genes were subject to CA (Table S1), and greater than 80% of glucose-responsive stress response genes were subject to CR (Table S2). These observations must reflect both physiological and mechanistic constraints. Moreover, nitrogen catabolic genes were more frequently subject to CR while nitrogen biosynthetic genes were more frequently subject to CA. (iv) All lower glycolytic enzyme-encoding genes were weakly induced by glucose while all Krebs cycle enzyme-encoding genes were strongly repressed. This observation suggests that the fermentative mode of carbon metabolism is dominant over the respiratory mode in B. subtilis. (v) Virtually all flagellar genes were weakly repressed by glucose while all ribosomal protein and RNA polymerase subunit genes were weakly activated by glucose in a CcpA-dependent manner. The former observation emphasizes the importance of motility for carbon scavenging while the latter observation presumably reflects the needs of a more rapidly dividing cell.

The results reported are in general internally consistent and make excellent physiological sense. For example, genes encoding Krebs cycle enzymes as well as most enzymes concerned with the initiation of the metabolism of carbon sources other than glucose are subject to CR, but those encoding glycolytic enzymes, including pyruvate dehydrogenase, are subject to CA (see also references 3, 29, and 38). Indirect effects of glucose and ccpA mutations on growth rate, which are dramatic in minimal medium but much less dramatic in complex medium, have been considered and explained previously (38, 75).

The glucose effects on ribosomal and RNA polymerase genes correlate with the known effects of growth rate on expression of the few of these genes that have been studied using classical approaches under comparable growth conditions (28). Moreover, the responses of the nitrogen, phosphorus, and stress regulons can be explained. First, carbon and nitrogen metabolic processes are complex and interdependent, and these genes may be involved in either catabolism or biosynthesis. Therefore it is not surprising that they may be subject to either CR or CA. Second, the availability of rich energy sources should relieve the need for stringent stress responses since energy availability allows the bacteria to repair damage caused by stress conditions, thus explaining glucose repression. Third, energy availability would be expected to stimulate growth only in the presence of utilizable sources of phosphorus. Consequently, glucose should activate pho genes as we have observed.

The work reported in this paper represents just the tip of the iceberg. We have presented analyses of results that proved to be of particular interest to us. However, the full transcriptome data now available on our website provides a rich source of information for anyone concerned with the use of bacilli for fermentative technologies, for the interconnections between transcription and metabolism, and for an understanding of poorly defined interregulon control mechanisms. We hope that these data will prove useful to microbiologists for years to come.


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ACKNOWLEDGMENTS
 
This work was supported by NIH grant GM55434 from the National Institute of General Medical Sciences. G.L.L. was supported by a fellowship from the Pew Latin American Program in the biomedical sciences.

We thank Matthew Moreno and Claudia Chagneau for helpful discussions and Anne Galinier, Joseph Deutscher, and Wolfgang Hillen for providing bacterial strains. We also thank Mary Beth Hiller for her assistance in the preparation of the manuscript. Erin Ha Khanh Nguyen and Tyrone Nguyen provided technical assistance.


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FOOTNOTES
 
* Corresponding author. Mailing address: Division of Biological Sciences, University of California at San Diego, La Jolla, CA 92093-0116. Phone: (858) 534-4084. Fax: (858) 534-7108. E-mail: msaier{at}ucsd.edu. Back

{dagger} Present address: Banting and Best, Department of Medical Research, Room 24, 112 College Street, Toronto, Ontario M5G 1L6, Canada. Back

{ddagger} Present and permanent address: Department of Life Science, Jeonju University, Chonju, Korea. Back

§ Present address: Genomatica, Inc., 5405 Morehouse, Suite 210, San Diego, CA 92121. Back


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Journal of Bacteriology, November 2005, p. 7826-7839, Vol. 187, No. 22
0021-9193/05/$08.00+0     doi:10.1128/JB.187.22.7826-7839.2005
Copyright © 2005, American Society for Microbiology. All Rights Reserved.



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