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J Bacteriol. 2006 November; 188(21): 7500–7511. Published online 2006 August 25. doi: 10.1128/JB.01110-06. | PMCID: PMC1636251 |
Copyright © 2006, American Society for Microbiology Cell Envelope Stress Response in Bacillus licheniformis: Integrating Comparative Genomics, Transcriptional Profiling, and Regulon Mining To Decipher a Complex Regulatory Network ![[down-pointing small open triangle]](/corehtml/pmc/pmcents/x25BF.gif) Tina Wecke,1 Birgit Veith,2 Armin Ehrenreich,2 and Thorsten Mascher1* Department of General Microbiology, Georg-August-University, 37077 Göttingen, Germany,1 Department of Genomic and Applied Microbiology, Georg-August-University, 37077 Göttingen, Germany2 Received July 25, 2006; Accepted August 18, 2006. The envelope is an essential structure of the bacterial cell, and maintaining its integrity is a prerequisite for survival. To ensure proper function, transmembrane signal-transducing systems, such as two-component systems (TCS) and extracytoplasmic function (ECF) σ factors, closely monitor its condition and respond to harmful perturbations. Both systems consist of a transmembrane sensor protein (histidine kinase or anti-σ factor, respectively) and a corresponding cytoplasmic transcriptional regulator (response regulator or σ factor, respectively) that mediates the cellular response through differential gene expression. The regulatory network of the cell envelope stress response is well studied in the gram-positive model organism Bacillus subtilis. It consists of at least two ECF σ factors and four two-component systems. In this study, we describe the corresponding network in a close relative, Bacillus licheniformis. Based on sequence homology, domain architecture, and genomic context, we identified five TCS and eight ECF σ factors as potential candidate regulatory systems mediating cell envelope stress response in this organism. We characterized the corresponding regulatory network by comparative transcriptomics and regulon mining as an initial screening tool. Subsequent in-depth transcriptional profiling was applied to define the inducer specificity of each identified cell envelope stress sensor. A total of three TCS and seven ECF σ factors were shown to be induced by cell envelope stress in B. licheniformis. We noted a number of significant differences, indicative of a regulatory divergence between the two Bacillus species, in addition to the expected overlap in the respective responses. The bacterial cell envelope is a prime target for many antibiotics, due to its crucial function: it separates and protects the cell from its environment, counteracts the high inner osmotic pressure, and acts as a communication interface and molecular sieve ( 15). Therefore, maintaining its integrity is essential for survival in a complex habitat such as the soil, where production of and resistance to antibiotics is one aspect of the daily struggle to survive ( 38). The regulatory networks orchestrating cell envelope stress response are well understood in Escherichia coli and Bacillus subtilis. Two two-component systems (TCS) (CpxAR and BaeRS), one extracytoplasmic function (ECF) σ factor (σ E), and the PspA-controlled phage shock protein system orchestrate the response to perturbation of the cell envelope in E. coli ( 1, 2, 14, 44, 45, 48). While the corresponding regulatory network of B. subtilis is even more complex, it basically consists of the same signaling components; a total of four TCS (BceRS, LiaRS, YvcPQ, and YxdJK), two ECF σ factors (σ M and σ W), and the σ B-dependent general stress response are induced by cell envelope stress ( 12, 32, 42). Here, the phage-shock protein system is integrated in LiaRS-mediated TCS signaling ( 24). Additionally, transcriptomics was applied to investigate the response of two gram-positive pathogens to the presence of various cell wall antibiotics. The cell envelope stress stimulon of Staphylococcus aureus strains sensitive, tolerant, or resistant to vancomycin was the subject of three independent studies that identified genes responsive to the presence of bacitracin, d-cycloserine, oxacillin, and vancomycin ( 29, 34, 55). The vancomycin stress response was also studied in sensitive and tolerant strains of Streptococcus pneumoniae ( 17). These studies identified numerous genes that were induced in an antibiotic-specific manner, including some with known or predicted function in cell wall homeostasis and antibiotic resistance. But the regulatory systems mediating the differential expression of most envelope stress-induced genes are unknown, with a few exceptions, such as VraSR-regulated genes in S. aureus ( 28, 61), and no study has been performed so far that specifically addressed the question of the corresponding signal transduction and gene regulation in these organisms. In contrast to the wealth of knowledge on regulatory networks in B. subtilis, little is known about regulation in the closely related bacterium B. licheniformis, despite its great industrial relevance and potential. Moreover, some strains of this species synthesize bacitracin, a branched cyclic dodecylpeptide antibiotic also produced by some strains of B. subtilis ( 5, 23). The bacitracin biosynthesis locus, including a self-resistance module, has been described in B. licheniformis strain ATCC 10716 ( 37). Apart from that, very little is known about the response of this organism to cell envelope stress. Recently, the genome sequence of B. licheniformis strain DSM13 was finished, allowing the first in-depth insights into its metabolic and regulatory capabilities ( 57). While genome-wide expression tools were subsequently established, the number of functional analyses exploiting this wealth of genomic information available is still low ( 21, 58, 59): B. licheniformis DSM13, in contrast to B. subtilis, is not naturally transformable, due to the lack of a comS homolog and a transposon insertion into the comP gene, rendering two crucial regulatory proteins for competence development dysfunctional ( 46). This trait prevents easy genetic manipulation and therefore a “standard” mutagenesis approach for subsequent in-depth studies on the mechanisms of signal transduction and gene regulation in this organism. Comparative genomics combined with genome-wide transcriptional profiling are powerful tools that have the prospect of (at least partially) overcoming these limitations, allowing researchers to study regulatory networks even in bacteria that are not accessible by classical genetic techniques. Here, we used such an approach to analyze the regulatory network orchestrating cell envelope stress responses in B. licheniformis DSM13. First, potential regulatory systems and their target genes were identified by sequence analysis, comparative genomics, and in silico regulon mining. The results of these analyses were subsequently validated by genome-wide DNA microarray studies of the cell envelope stress stimulon of B. licheniformis. The use of bacitracin and vancomycin as model envelope stress inducers enabled us to perform comparative transcriptomics analyses to substantiate our findings. Furthermore, the activity and stimulus specificity of each identified signaling system was investigated by in-depth transcriptional profiling. We identified seven ECF σ factors and three TCS that specifically respond to cell envelope stress in B. licheniformis. Some of these systems have corresponding orthologs in B. subtilis, but we also noted a number of alterations and significant differences in the response of the respective organisms to cell envelope stress. Finally, we compared the response and resistance of the bacitracin-producing strain ATCC 10716 and the nonproducing reference strain DSM13. Bacterial strains and growth conditions. B. licheniformis strain DSM13 or ATCC 10716 was routinely grown in Luria-Bertani (LB) medium at 37°C with aeration. For RNA isolation, the cultures were incubated until an optical density at 600 nm of ~0.35. The cultures were split; 100 ml served as an uninduced control, and 100 ml was induced with one of the following antibiotics (final concentration): bacitracin (300 μg/ml for DSM13; 1,000 μg/ml for ATCC 10716), vancomycin (1 μg/ml), d-cycloserine (30 μg/ml), fosfomycin (6 μg/ml), penicillin G (100 μg/ml), ampicillin (100 μg/ml), nisin (5 μg/ml), or gramicidin (200 μg/ml). After 10 min, 84 ml of the culture was mixed with 16 ml stop solution (95:5 ethanol:phenol), and the cells were harvested by centrifugation at 5,000 × g for 10 min at 4°C. Subsequently, the cells were washed in 50 mM KH2PO4, and the pellets were shock frozen in liquid nitrogen and stored at −70°C. Killing curve experiments. Fifty milliliters of LB medium was inoculated from a fresh overnight culture and grown until an optical density at 600 nm of ~0.5. The culture was split into 4-ml portions, and different concentrations of antibiotics were added. A sample was regularly taken over a period of 8 h to monitor the effect of the antibiotic on cell density. Based on the resulting growth (“killing”) curves, antibiotic concentrations were chosen for subsequent induction experiments and RNA preparation that arrested growth (i.e., exhibited detectable stress on the culture), but neither led to cell death (i.e., sublethal concentration) or affected growth during the first 30 min post induction (see Fig. for an example). RNA isolation. The pellet was resuspended in 200 μl Tris-EDTA buffer, and the cells were disrupted in a liquid nitrogen-cooled reservoir with a Micro-Dismembrator U (Braun Biotech) for 3 min at 1,600 rpm, 4 ml RLT buffer supplemented with β-mercaptoethanol was added, and RNA isolation was performed with the RNeasy Midi kit (QIAGEN) according to the manufacturer's protocol. The RNA was eluted with 300 μl RNase-free water. The RNA was then incubated with 100 U DNase I (Roche) for 90 min at 25°C to eliminate contaminating genomic DNA. The success of this treatment was verified by a lack of product in a standard PCR using the primers 37a and 37b (Table 1). DNase I was removed by phenol extraction, and after ethanol precipitation the quality of the RNA was verified by agarose gel electrophoresis and reverse transcriptase-PCR (RT-PCR) using the primers mentioned above. | TABLE 1.Oligonucleotides used in this study |
Microarray hybridization. A DNA microarray, representing more than 95% of the open reading frames longer than 300 bp of the genome of B. licheniformis DSM13, was used as previously described ( 21). For cDNA labeling, 25 μg of total RNA and 1 μg random nonamer primers (Amersham Biosciences) were mixed, and annealing was performed in a PCR machine by asymptotical cooling from 70°C to 22°C within half an hour. To monitor labeling efficiency, 1 μl of a reference label (Lucidea Universal Score Card; Amersham Biosciences) was included in each labeling reaction. After annealing of the primers, labeled cDNA was generated by reverse transcription as described previously ( 21) and subsequently purified with the CyScribe GFX Purification kit (Amersham Biosciences). The amount of incorporated fluorescent nucleotides was determined by measuring the absorption of the labeled cDNA at 550 nm for Cy3 and at 650 nm for Cy5. Incorporated fluorescent dye (50 to 150 pmol of each) was used for microarray hybridization. The labeled cDNA was denatured at 95°C for 2 min, 50 μl microarray hybridization buffer (Amersham Biosciences) and 100 μl of 100% (vol/vol) formamide were added, and the hybridization was carried out overnight at 42°C using an automatic slide processor (Lucidea SlidePro hybridization chamber; Amersham Biosciences). Each slide was washed, dried, and scanned for fluorescence intensities with a resolution of 10 μm/pixel using a GenePix 4000B array scanner (Axon Instruments). The signal intensities from each spot in the array were collected using the GenePix Pro software version 6.0 (Axon Instruments). Microarray datasets were filtered to remove genes that (i) were not significantly expressed under both conditions or (ii) showed an average deviation of ≥30% between the calculated ratio of means, ratio of medians, and regression ratio ( 21). The complete datasets are available at http://wwwuser.gwdg.de/~genmibio/mascher/wecke_2006.htm. Quantitative real-time RT-PCR. Measurement of transcript abundance was performed by quantitative real-time RT-PCR using the QuantiTect SYBRgreen RT-PCR kit (QIAGEN) according to the manufacturer's procedure with minor modifications. In brief, 400 ng of DNA-free total RNA was used in a total reaction volume of 25 μl with 0.5 μM of each primer (Table 1). The amplification reaction was carried out in an iCycler (Bio-Rad) using the following program: initial incubation step at 50°C for 30 min, followed by a 95°C denaturing/activation step for 15 min, followed by 40 cycles of 94°C for 15 s, 55°C for 30 s, and 72°C for 30 s. After a subsequent incubation step (55°C for 1 min), the set point temperature was increased in 80 cycles (10 s each) by 0.5°C/cycle, starting from 55°C, to determine the melting temperatures of the PCR products. Expression of rpsJ and mdh (encoding ribosomal protein S10P and malate dehydrogenase, respectively) was monitored as a constitutive reference. These genes were chosen due to their stable expression behavior under various growth and stress conditions in both B. subtilis and B. licheniformis (data not shown). Expression of the ECF σ factors was calculated as fold changes using the following formula: fold change = 2 −ΔΔCt, where −ΔΔCt = (Ct gene x − Ct constitutive gene) condition I − (Ct gene x − Ct constitutive gene) condition II ( 53). Probe preparation and Northern blot analysis. Internal fragments of ~500 nucleotides were amplified by PCR using the primer pairs listed in Table 1. The PCR fragments were purified using the QIAGEN PCR Purification kit, and 100 ng of each fragment was labeled with [α- 32P]dATP (3,000 Ci/mmol; 10 mCi/μl; Hartmann Analytic) by random oligonucleotide-primed synthesis using the Klenow fragment of DNA polymerase (usb) according to protocol 3.5.9-10 ( 4). Unincorporated [α- 32P]dATP was removed by NucAway spin columns (Ambion). For Northern blot analysis, 5 μg of total RNA was denatured and loaded on a formaldehyde agarose gel. After electrophoresis, the RNA was transferred to a nylon membrane (Roche) in a downward transfer using 20× SSC as transfer buffer (1× SSC is 0.15 M NaCl plus 0.015 M sodium citrate). The RNA was cross-linked by exposing the damp membrane to UV light. The blot was prehybridized at 42°C for 1 h with Ultrahyb buffer (Ambion), and the labeled probe (preheated to 98°C for 10 min) was added to the hybridization tube. Hybridization was performed overnight at 42°C. On the next day, the membrane was washed twice with low-stringency buffer (2× SSC plus 0.1% sodium dodecyl sulfate [SDS]) at room temperature for 5 min, followed by two high-stringency washes (0.1× SSC plus 0.1% SDS) at 42°C for 15 min. The blot was wrapped in plastic wrap, exposed to a phosphor screen (Molecular Dynamics), and analyzed using a PhosphorImager (Molecular Dynamics). Comparative genomics analyses. Multiple sequence alignments were performed using ClustalW and phylogenetic trees were generated with TreeView, both implemented in the BioEdit program package ( 18). Domain-based analysis of histidine kinases to identify cell envelope stress-sensing TCS were performed using the SMART database ( 50) at http://smart.embl-heidelberg.de/. The identification of ECF σ factors in the genome sequence of B. licheniformis was performed using the ERGO database, which is available through Integrated Genomics, Inc. ( http://www.integratedgenomics.com), and maintained by the GenoMIK center, Göttingen, Germany. The promoter sequence of the ECF σ factors was used to screen the genome for putative target genes with help of the virtual footprint algorithm ( 36), implemented into the Prodoric database ( 35) at http://www.prodoric.de/vfp/. Identification of potential cell envelope stress-sensing regulatory systems and their target genes in the genome sequence of B. licheniformis. Two regulatory principles orchestrate the cell envelope stress response in gram-positive bacteria: two-component systems (TCS) and extracytoplasmic function (ECF) σ factors ( 32). Both systems consist of two proteins, a membrane-anchored sensor (histidine kinase or anti-σ factor, respectively) that perceives a specific stimulus from the environment and a cytoplasmic transcriptional regulator (response regulator or σ factor, respectively) that mediates the cellular response through differential expression of its target genes. The genome sequence of B. licheniformis DSM13 harbors 27 gene pairs encoding classical TCS. Analysis of the cell envelope stress stimulon of B. subtilis revealed that all histidine kinases involved in perceiving envelope stress are very small proteins characterized by the lack of an extracytoplasmic sensor domain between the two putative membrane-spanning regions ( 32, 42). These proteins belong to the subclass of so-called intramembrane-sensing histidine kinases and are thought to sense their stimulus at or within the membrane interface ( 31). Based on this unique domain architecture, we recognized 5 of the 27 histidine kinases as potential envelope stress sensors (Fig. ). YvqE and its cognate response regulator YvqC are orthologous to the LiaRS system of B. subtilis. This TCS is a conserved cell envelope stress sensor in gram-positive bacteria with a low G+C content ( 24). It autoregulates its own expression from a promoter upstream of liaIH (i.e., yvqIH in B. licheniformis; see Fig. ) ( 33). The genes of the other four potential intramembrane-sensing histidine kinases, ycbM, ytsB, Bli04270, and yxdK, are localized directly upstream of genes encoding ABC transporters. A functional and regulatory connection between TCS and neighboring ABC transporters is well established for Firmicutes bacteria. In these detoxification modules, the TCS responds to the presence of a harmful compound, i.e., an antibiotic such as bacitracin, and strongly induces the expression of the neighboring ABC transporter, which in turn facilitates removal ( 25, 26, 32, 39). Note that both Bli04270 and yxdK encode orthologs to B. subtilis YxdK. The yxdKLM locus of B. licheniformis is most likely nonfunctional due to a confirmed frameshift mutation in the gene encoding the cognate response regulator, which is therefore annotated as two genes, Bli04143 and Bli04144 (Fig. ). Since yxdJ-Bli04268-Bli04270 seems to encode the functional ortholog to the yxdJKLM locus of B. subtilis, we renamed the corresponding genes yxdM2, yxdL2, and yxdK2, respectively (Fig. ). A fifth candidate of this type, the BacRS-BcrABC system, mediates self resistance in the bacitracin-producing strain B. licheniformis ATCC 10716 ( 37). This system is not present in strain DSM13 (Fig. ). ECF σ factors form a phylogenetically distinct group within the σ 70 protein family. They can be easily discriminated from other σ factors by the lack of region 3 (σ 3), which makes them smaller by more than 50 amino acids ( 30). Typically, their corresponding genes are cotranscribed with and located upstream of a gene encoding a membrane protein that functions as the cognate anti-σ factor ( 20). Based on these criteria, we identified eight genes in the genome sequence of B. licheniformis DSM13 that encode putative ECF σ factors (Fig. ). The gene products of six of them are orthologous to proteins from B. subtilis and were therefore named σ M, σ V, σ W, σ X, σ Y, and σ ylaC. No homolog of B. subtilis σ Z can be found. Instead, the genome of B. licheniformis encodes two novel putative ECF σ factors, Bli03891 and Bli04171, designated σ ecfG and σ ecfH hereafter. With the exception of ylaC, all genes encoding ECF σ factors are located upstream of and are most likely cotranscribed with genes encoding putative transmembrane proteins homologous to known or putative anti-σ factors (Fig. ). ECF σ factors recognize alternative promoter sequences and normally autoregulate their own expression ( 20). It was therefore possible to identify candidate DNA-binding sites for each individual ECF σ factor by analyzing the promoter regions directly upstream of the respective gene and by comparison to the known ECF-promoter counterparts from B. subtilis. For the identification of putative ECF-regulated genes, the “Virtual Footprint” algorithm ( 36), implemented into the Prodoric database ( 35), was used. Our analysis was based on nucleotide position weight matrices. These matrices reflect the degree of conservation of the four nucleotides at each position of a known regulator DNA-binding site, as derived from experimental evidence ( 36). Initially, preexisting position weight matrices for B. subtilis σ W and σ X were used to screen intergenic regions in the genome sequence of B. subtilis to optimize the search parameters and stringency settings. Using these settings, we were able to retrieve ~80% of the known ECF target genes in B. subtilis. The promoters of all missing genes were either located in upstream coding sequences (and therefore omitted from the analysis) or contained nucleotide exchanges in highly conserved core residues (data not shown). Subsequently, the genome of B. licheniformis was analyzed with the same settings, using the two position weight matrices described above and an additional matrix derived from the promoters upstream of all ECF σ factors from both bacteria. The putative ECF-binding sites retrieved from those screens were further analyzed with regard to relative positions of the start codon and stacking energy peaks (as an indication for the presence of a transcriptional start site) to remove redundant and false-positive hits. We identified 35 potential target loci, preceded by an ECF-type promoter. The putatively ECF-regulated genes and their promoter sequences are listed in Table 2. Note that it was not possible to assign any of the promoters to a specific ECF σ factor by sequence analysis alone, due to their overall similarity. This problem is underscored by the observed regulatory overlap of ECF σ factors in B. subtilis ( 9, 20, 22, 62). Almost 60% (20 out of 35) of these genes are orthologs of genes known to be regulated by one or more ECF σ factors in B. subtilis (Table 2), indicative of the reliability of our approach. | TABLE 2.Promoter sequence of putative ECF-regulated genes and their induction by bacitracin and vancomycin in B. licheniformis and B. subtilis |
Identification of the cell envelope stress stimulon of B. licheniformis DSM13 by DNA microarray analysis and comparative transcriptomics. To experimentally evaluate our in silico predictions, we analyzed the cell envelope stress response of B. licheniformis by applying genome-wide transcriptome analysis. A full-genome DNA microarray, with PCR products representing more than 95% of all genes larger than 300 bp ( 21), was used for competitive hybridization of total RNA prepared from cultures with (test) and without (control) addition of sublethal concentrations of bacitracin and vancomycin, respectively. The two antibiotics were chosen for the initial screens due to the presence of corresponding data sets from B. subtilis ( 12, 32), allowing direct comparison of the stimulons between the two species (“comparative transcriptomics”). The optimal antibiotic concentrations for induction (300 μg/ml for bacitracin, 1 μg/ml for vancomycin) were determined by killing curve experiments (data not shown) (see Material and Methods for experimental details). Cultures were induced at mid-log phase, and cells were harvested 10 min postinduction and washed. RNA and sample preparation was performed essentially as described previously ( 56) and outlined in the Material and Methods section. Two-component systems. A detailed analysis of the bacitracin stimulon by comparative transcriptomics, as illustrated in Fig. , demonstrates similarities as well as a number of significant differences between the responses of B. licheniformis and B. subtilis. In both organisms, the genes most strongly induced are controlled by three TCS. The genes yvqIH and ytsCD are orthologs of liaIH and bceAB, regulated by YvqEC/LiaRS and YtsAB/BceRS, respectively. A second putative YvqEC target locus, the yhcYZ-yhdA operon, is also significantly induced by bacitracin. In B. subtilis, the homologous operon is expressed in a LiaR-dependent manner, and a putative LiaR-binding site was recently identified in the yhcY promoter region of B. licheniformis ( 24). The BceRS-BceAB system is an important bacitracin resistance determinant ( 32, 39). The third TCS strongly responding to bacitracin, YxdJ/YxdK2, is one of the two YxdJK homologs (see above). In B. subtilis, the genes encoding the corresponding ABC transporter, yxdLM, are followed by a third small gene of unknown function, yxeA, which is part of the same transcriptional unit. This gene seems to be missing downstream of the homologous genes yxdL2-yxdM2 (Fig. ). Interestingly, a closer examination of their genomic context together with the DNA microarray data revealed the presence of a small open reading frame (Bli04272) directly upstream and divergently oriented from yxdJ that shows significant homology to yxeA and is also strongly induced by bacitracin (Fig. ; also see Fig. ). These findings strongly suggest a duplication, genomic rearrangement, and functional divergence of the B. subtilis yxdLM-yxeA operon in B. licheniformis, with simultaneous maintenance of the regulation of all three genes by the corresponding TCS. Addition of vancomycin specifically induced the YvqEC system, with its primary target, yvqIH, being induced 27-fold (Table 3). None of the other cell envelope stress-sensing TCS identified above responded to the presence of this antibiotic. | TABLE 3.Marker genes of the bacitracin and vancomycin stimulons of B. licheniformis that are not regulated by ECF σ factors |
ECF σ factors. In B. subtilis, σ M responds to the presence of bacitracin, and expression of its target genes is induced ( 32). Likewise, the sigM-yhdLK operon and a number of putative ECF target genes are also induced in B. licheniformis (Fig. ; Table 2), indicative of a similar role of this ECF σ factor in counteracting cell envelope stress in both bacteria. Addition of vancomycin resulted in an overall stronger response of ECF σ factors compared to bacitracin, most notably sigM (14-fold), sigV (5.2-fold), and sigW (3.5-fold). Accordingly, a larger number of the putative ECF target genes were also significantly induced in the corresponding stimulon and were induced to higher fold changes relative to bacitracin (Table 2). Additional marker genes. A list of additional marker genes (induction rate, ≥5-fold) of both stimulons, not associated with the cell envelope stress-sensing systems described above, is given in Table 3. For some of these systems, such as the pyr and ytrABCDEF operons, nonspecific induction by envelope and other stress conditions has also been described in B. subtilis ( 12, 32). Interestingly, homologs of genes of the CtsR-mediated class III heat shock response of B. subtilis are strongly induced by vancomycin in B. licheniformis but not in B. subtilis. Other vancomycin-inducible genes, such as the minCD-mreB operon, yxeG, ponA, and Bli04025, have known or predicted functions in cell envelope homeostasis (Table 3). Additionally, the genes of another TCS, YcbAB, were also significantly induced by bacitracin in B. licheniformis (Table 3) but not in B. subtilis. Two regulons that strongly respond to the presence of bacitracin in B. subtilis are not induced at all in B. licheniformis: (i) the CzrA-regulated zinc stress response and (b) the large regulon of the σB-dependent general stress response. Additional differences include a number of genes that were specifically induced only in one of two organisms due to the lack of a homolog in the other genome (Fig. ). Stimulus specificity of the identified cell envelope stress-sensing TCS. To verify the results obtained by DNA microarray analysis and further investigate the range of inducing conditions, Northern analyses were performed on target genes of the identified TCS mediating envelope stress response in B. licheniformis. First, we investigated the expression of the yvqIHGFEC locus with a yvqIH-specific probe (Fig. ), using 5 μg of total RNA from cultures that were induced with sublethal concentrations of various cell wall antibiotics. No expression of the yvq locus was detectable in unstressed cultures. The strongest induction was observed with bacitracin. Weaker induction was obtained with vancomycin and d-cycloserine, whereas no induction occurred in the presence of fosfomycin and β-lactams (Fig. , left side) (data not shown). We noticed the presence of three different transcripts, sized 1.1, 2.5, and 4.6 kb, respectively (Fig. ). The homologous lia locus of B. subtilis is expressed as two different transcriptional units due to partial termination at a stem-loop structure downstream of the second gene, liaH. The primary transcript includes liaIH (~1.1 kb), but there is also a significant amount of readthrough, resulting in a full-length transcript of ~4.5 kb covering all six genes ( liaIHGFSR) ( 33). Sequence analysis revealed the presence of two inverted repeats within the yvq locus: (i) a weak stem-loop directly downstream of yvqH, a situation reminiscent of B. subtilis, and (ii) a stronger stem-loop within the coding sequence of yvqF that is not present in B. subtilis (Fig. ). Additional Northern blot analyses with yvqG- and yvqE-specific probes support a possible role of these inverted repeats in transcriptional termination (Fig. ). Envelope stress-inducible expression of the lia operon in B. subtilis is mediated by a strictly LiaR-dependent promoter upstream of liaI ( 33). Sequence comparison demonstrates that all relevant features are conserved in the corresponding yvqI promoter region, including the identified LiaR-binding site (Fig. ). Therefore, we conclude that the yvq locus of B. licheniformis is expressed from a strictly YvqC-dependent promoter in the presence of cell wall antibiotics such as bacitracin and vancomycin. The range of inducers is therefore very similar to that observed in B. subtilis ( 33). Similar analyses were performed for ytsCD, yxdL2-yxdM2, and Bli04272, verifying the strong bacitracin-dependent and -specific induction of both loci observed by DNA microarray. None of the other cell wall antibiotics tested induced either system (Fig. ; data not shown). Additionally, we also investigated the inducibility of the remaining putative envelope stress-sensing TCS by different cell wall antibiotics by using Northern hybridization. No expression of yxdLM was observed under any condition tested, most likely due to a frameshift mutation within the coding sequence of the yxdJ-homologous response regulator (annotated as two genes, Bli04143 and Bli04144, in Fig. ), as noted above. The same negative result was obtained for the YcbLM-dependent genes ycbNO and Bli00295 (data not shown). Taken together, we were able to verify that three of the five putative cell envelope stress-sensing TCS of strain DSM13 respond to the presence of cell wall antibiotics, as observed by the induction of their corresponding target genes (the function and activation of the sixth TCS, BacRS, in strain ATCC 10716 is described below). Transcriptional profiling of ECF σ factor activation. The bcrC gene of B. subtilis is controlled by at least four different ECF σ factors, σ M, σ V, σ W, and σ X ( 9, 10, 40, 62). Its homolog in B. licheniformis, ywoA, is preceded by a well-conserved ECF-type promoter and is strongly induced by both bacitracin and vancomycin (Table 2). Therefore, it was chosen as a marker gene to further investigate ECF-dependent gene expression by Northern blot analysis. Its expression is strongly induced in the presence of bacitracin, vancomycin, and β-lactam antibiotics, such as ampicillin and penicillin G (Fig. ), indicative for the activation of some ECF σ factors in the presence of these antibiotics. As noted above, activation of an ECF σ factor by an external stimulus normally leads to an induction of its own transcription due to positive autoregulation. To obtain a more detailed picture of the ECF response to the presence of bacitracin, vancomycin, and penicillin, we performed real-time RT-PCR with primer pairs specific to all eight genes encoding putative ECF σ factors. We also included a noninducer, d-cycloserine, as a control. The results are shown in Fig. . Expression was observed for seven of the eight ECF σ factors. No signals were detected for ylaC under any condition tested, indicative of a lack of expression of σylaC in B. licheniformis. σM strongly responds to the presence of all three inducing compounds, resulting in a 20- to 50-fold induction of sigM expression, but not to the presence of d-cycloserine. This antibiotic also failed to induce the expression of any other ECF gene. In contrast, distinct induction patterns could be observed for the remaining ECF σ factors (Fig. ): σV and σY are strongly induced by vancomycin (about 15-fold), but they are also induced by bacitracin and penicillin, albeit to a lesser extent. Expression of sigW and sigX is only weakly induced by any of the three antibiotics. Again, vancomycin gives the strongest response, resulting in an approximately fivefold increased expression level relative to the uninduced control. The novel ECF σ factor σecfG specifically responds to the presence of β-lactams (~15-fold induction), whereas expression of ecfH is induced by vancomycin and bacitracin (seven- and fivefold, respectively). These results clearly demonstrate that ECF σ factors play an important role in the regulatory network orchestrating cell envelope stress response of B. licheniformis. Bacitracin resistance and response in a producing (ATCC 10716) and a nonproducing (DSM13) strain of B. licheniformis. The peptide antibiotic bacitracin inhibits bacterial cell wall biosynthesis by tightly binding the lipid carrier molecule C55-isoprenol (undecaprenol) in its pyrophosphate form (UPP) and preventing its dephosphorylation to the monophosphate form ( 51, 52). This step is necessary for the recycling of the lipid carrier molecule for further rounds of reloading at the inner face of the cytoplasmic membrane. Therefore, bacitracin ultimately blocks cell wall biosynthesis by arresting the lipid carrier in its inactive pyrophosophate form. Bacitracin resistance can be achieved by induction of (i) a bacitracin efflux pump (ABC transporter) facilitating removal, (ii) a UPP-specific pyrophosphorylase, or (iii) by de novo synthesis of undecaprenol monophosphate. The first two mechanisms have been described for B. subtilis: BceRS-BceAB, a bacitracin-specific TCS/ABC transporter ( 39), and BcrC, the UPP pyrophosphorylase ( 6). The corresponding genes are controlled by envelope stress-sensing systems and are induced by bacitracin ( 9, 32, 39, 40). Homologs are present in the genome of B. licheniformis ( ytsABCD and ywoA, respectively), and bacitracin-specific induction could be observed (Fig. , , and ). So far, all the work presented herein was done in the sequenced reference strain DSM13, a nonproducer for bacitracin. The bacitracin biosynthesis gene cluster of B. licheniformis strain ATCC 10716 includes another bacitracin resistance determinant (Fig. ). The BacRS TCS senses the presence of bacitracin and induces the expression of the bcrABC operon, encoding a bacitracin-specific efflux pump ( 37). This system is missing in the genome of strain DSM13 ( 57). We therefore decided to compare the response of these two strains to the presence of bacitracin. Killing curve experiments demonstrated that strain ATCC 10716 is significantly more resistant to bacitracin than strain DSM13. Growth of DSM13 was arrested at a final bacitracin concentration above 100 μg/ml, while it was unaffected even at 1,000 μg/ml in strain ATCC 10716 (Fig. ). The presence of the ytsABCD and ywoA genes on the chromosome of strain ATCC 10716 was checked initially by genomotyping, i.e., hybridization of total genomic DNA to the DSM13 microarray, and verified by specific PCR amplification of the respective genomic regions (data not shown). Northern hybridization with DNA probes specific for bcrA, ytsC, and ywoA (Fig. ) demonstrated a strong bacitracin induction of all three loci in strain ATCC 10716, whereas only ytsCD and ywoA are strongly induced in strain DSM13, as expected. Therefore, we conclude that the further increase of bacitracin resistance in the producing strain ATCC 10716 is the result of the induction of three bacitracin resistance determinants (instead of two in DSM13): the presumably ECF-dependently expressed UPP pyrophosphorylase YwoA and two bacitracin efflux pumps, YtsCD and BcrABC, regulated by the TCS YtsAB (based on the analogy to the corresponding B. subtilis system), and BacRS, respectively. The genomic era has provided microbiologists with an overwhelming wealth of sequence information: about 350 microbial genomes are completely sequenced to date, with about twice as many under way (as of July 2006). A majority of these bacteria cannot be studied directly due to the lack of appropriate molecular tools. These limitations can be overcome to some extent by applying transcriptomics or proteomics to study the differential expression of genes or proteins, respectively, on a genome-wide scale. Especially DNA microarrays have found widespread use to study the transcriptional response of numerous bacterial species to a variety of different growth conditions and stimuli ( 13, 16, 49, 60). While a thorough analysis of the (putative) function of differentially expressed genes gives some insight into the biology of an adaptational response, it rarely gives hints regarding the regulatory processes underlying such differential gene expression. This limitation is unfortunate, since knowledge of the regulatory systems mediating differential gene expression in response to a certain stimulus is a prerequisite to understand and predict the reactions of a bacterial system to changes of environmental parameters. In this paper, we report a comprehensive analysis of a complex regulatory network, based solely on comparative genomics, regulon mining, comparative transcriptomics, and subsequent in-depth transcriptional profiling of the identified regulatory systems. We used the wealth of knowledge on the regulation of cell envelope stress response in a laboratory model bacterium, B. subtilis, to decipher the corresponding system in a wild-type strain of B. licheniformis, a related organism with great industrial relevance but lacking established genetic tools for easy manipulation. We believe that such an approach is very useful and generally applicable to analyze regulatory mechanisms in many bacterial species for which a genome sequence is available but where molecular biological tools have not (yet) been developed. Comparative genomics and regulon mining. Initially, we screened the genome sequence of B. licheniformis DSM13 for potential envelope stress-sensing systems based on sequence homology, protein domain architecture, and genomic context of known envelope stress sensors from gram-positive and -negative bacteria. A total of 13 candidate systems were identified (eight ECF σ factors and five TCS with intramembrane-sensing histidine kinases; Fig. ), 10 of which were direct orthologs to corresponding systems in B. subtilis. A sixth TCS, BacRS, is present only in the genome of the bacitracin-producing strain ATCC 10716 but not in the reference strain DSM13. One ECF σ factor of B. subtilis, σZ, was missing in the B. licheniformis genome, which harbors two novel members of that protein family (σecfG and σecfH). Likewise, the B. licheniformis genome lacks one known TCS of B. subtilis, YvcPQ, but instead harbors a duplication of another envelope stress-sensing TCS, YxdJK. Potential target genes of these systems were identified based on genomic context conservation (in the case of TCS target genes) or the presence of a conserved regulator binding site upstream of the start codon (for ECF-controlled genes). Identification of potential regulon members based on the presence of cis-acting regulatory elements is a very useful method that has been successfully applied for a number of alternative (ECF) σ factors, such as σ W and σ X of B. subtilis ( 8, 10). A promoter prediction model for E. coli σ E, based on transcriptional profiling and subsequent bioinformatics analysis, was used to identify putative σ E-controlled genes in eight related genomes ( 47). A comparable computational method to predict regulons across genomes is based on the use of position weight matrices of cis-regulatory elements in combination with regulon conservation across genomes to predict likely regulator-binding sites and target genes ( 3). This concept is implemented into Prodoric (prokaryotic database of gene regulation) ( 35), which was used here to identify potential ECF target genes of B. licheniformis via the “Virtual Footprint” algorithm ( 36). By applying this algorithm, we identified 43 operons harboring an ECF-type promoter signature in B. licheniformis (35 ECF target genes plus eight promoters upstream of the regulators themselves), with 26 homologous loci being described ECF targets in B. subtilis (Table 2). Unfortunately, we were not able to go beyond the identification of potential ECF target genes: none of the prediction tools available at the moment ( 3, 36, 47) can accurately discriminate between the individual ECF-type promoters from bacilli and predict the corresponding regulons of a specific ECF σ factor. The induction of more than one ECF σ factor for any given stimulus tested and the negligible differences in their respective promoter sequences observed for B. subtilis ( 20) and B. licheniformis (Table 2) both account for that problem. This even seems to be of relevance in vivo, as judged by the significant amount of regulatory overlap observed for the ECF σ factors in B. subtilis ( 9, 20, 22, 62). Comparative transcriptomics and transcriptional profiling. The reliability of our predictions was underscored by comparative transcriptomics (the direct comparison of DNA microarray data between two [or even more] related species, performed under similar conditions): 32 of the putative ECF target loci of B. licheniformis were induced by vancomycin, 24 of which were also induced in B. subtilis under comparable conditions (Table 2). Induction of about two-thirds of the initially predicted putative ECF target loci (based on the presence of well-conserved ECF-type promoter sequences) by envelope stress indicates that these genes might indeed be controlled by ECF σ factors in B. licheniformis. A comparable percentage was observed for the cell envelope stress-sensing TCS and their target genes. The target genes from four (out of six, including BacRS, which is present only in the genome of strain ATCC 10716; Fig. ) TCS were strongly induced in the presence of sublethal amounts of bacitracin (Table 3; Fig. , , and ). The most significant difference in the response of the two bacilli to bacitracin was the lack of induction of the σ B-dependent general stress response in B. licheniformis, a regulon which is strongly induced under similar conditions in B. subtilis ( 32). The presence of a functional sigB operon in the genome of B. licheniformis and its σ B-dependent induction by heat or ethanol shock has already been demonstrated ( 7). Surprisingly, not a single σ B-dependent promoter was activated by bacitracin in B. licheniformis (Fig. ) (data not shown). A lack of induction of the general stress response in B. licheniformis was also noticed in a recent publication on the phosphate starvation response of B. licheniformis ( 21), again in contrast to results obtained for B. subtilis ( 19). Comparative genomics analyses revealed that, while the core of the σ B cascade is present, most of its sensory components are absent in the genome of B. licheniformis DSM13; no homolog of the rsbQP operon, encoding the sensor of the energy stress pathway, and only one of the five RsbR paralogs, which function collectively as input devices of the environmental stress signaling pathway ( 43), could be identified (data not shown). Therefore, we reason that a number of known stimuli for σ B-dependent gene expression in B. subtilis, such as bacitracin or phosphate starvation, are not able to induce the general stress response in B. licheniformis due to the lack of suitable sensory components. This hypothesis will require further investigations. Our results on the stimulus specificity revealed a strong dominance of σ M induction over any other ECF σ factor in B. licheniformis (Fig. ). The stimulus preference of σ M in B. subtilis is rather unspecific; it is activated in response to various cell wall antibiotics, ethanol shock, heat shock, acid stress, and superoxide stress, among others ( 54). In fact, the range of inducers for σ M is surprisingly similar to a subset of stimuli triggering the general stress response in B. subtilis. Therefore, it is very tempting to speculate that the strength of σ M activation in B. licheniformis is, at least in part, due to the lack of the σ B-dependent induction of the general stress response that could help to counteract the damages caused by envelope stress. Outlook. One interesting finding of our analysis is the very specific induction of σ ecfG, one of two novel ECF σ factors in the B. licheniformis genome, by β-lactam antibiotics (Fig. ). What are the genes regulated by this ECF σ factor? While a direct connection between signaling systems and their target genes is rather simple in the case of the four cell envelope stress-sensing TCS due to the genomic context conservation, we cannot link the identified ECF target genes to their specific regulators based on the data obtained in our analysis, as discussed above. This limitation necessitates the use of in vitro approaches, such as runoff transcription with subsequent microarray analysis, to identify target genes of the ECF σ factors from B. licheniformis. This technique was developed and successfully applied to identify target genes of σ W, σ X, and σ Y of B. subtilis ( 8, 10, 11) and can easily be applied to alternative σ factors from any bacterial species. This approach will hopefully enable us to discriminate between specificity and regulatory overlap of the ECF-regulated genes identified in this study. We gratefully acknowledge the generous support of Gerhard Gottschalk. 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