- Research article
- Open Access
The entire organization of transcription units on the Bacillus subtilis genome
© Kobayashi et al; licensee BioMed Central Ltd. 2007
- Received: 22 January 2007
- Accepted: 28 June 2007
- Published: 28 June 2007
In the post-genomic era, comprehension of cellular processes and systems requires global and non-targeted approaches to handle vast amounts of biological information.
The present study predicts transcription units (TUs) in Bacillus subtilis, based on an integrated approach involving DNA sequence and transcriptome analyses. First, co-expressed gene clusters are predicted by calculating the Pearson correlation coefficients of adjacent genes for all the genes in a series that are transcribed in the same direction with no intervening gene transcribed in the opposite direction. Transcription factor (TF) binding sites are then predicted by detecting statistically significant TF binding sequences on the genome using a position weight matrix. This matrix is a convenient way to identify sites that are more highly conserved than others in the entire genome because any sequence that differs from a consensus sequence has a lower score. We identify genes regulated by each of the TFs by comparing gene expression between wild-type and TF mutants using a one-sided test. By applying the integrated approach to 11 σ factors and 17 TFs of B. subtilis, we are able to identify fewer candidates for genes regulated by the TFs than were identified using any single approach, and also detect the known TUs efficiently.
This integrated approach is, therefore, an efficient tool for narrowing searches for candidate genes regulated by TFs, identifying TUs, and estimating roles of the σ factors and TFs in cellular processes and functions of genes composing the TUs.
- False Discovery Rate
- Transcription Factor Binding Site
- Subtilis Genome
- Flagellar Synthesis
- Transcription Factor Binding Sequence
Recent progress in genome projects has generated a vast amount of nucleotide sequence data, and analyses of gene expression by global approaches have started to broaden our understanding of cell systems. As a useful model for systems biology and genomics, many studies use Bacillus subtilis, a spore-forming gram-positive bacterium whose genome sequence has been determined . The ultimate goal of post-genome analysis is to specify transcriptional regulation in the entire genome. Computational algorithms to locate transcription units (TUs) have been developed based on analysis of signal sequences that are located at the boundaries of TUs from promoters to terminators, homologous gene pairs on other genomes, intergenic distance, functional categories, and gene clusters conserved among various species [2–7]. In the present study, a string of one or more genes co-transcribed is defined as a TU .
Identification of transcription factors (TFs) and their binding sites on their target genes is an important element of transcriptome analysis in the post-genome-sequencing era. Until now, various approaches have been taken to identify specific DNA-binding sites of TFs. DNA-binding specificities have traditionally been determined by experimental techniques such as DNase I footprinting and electromobility shift assay [8, 9]. More recently, putative TF binding sites have been identified by computational techniques such as hidden Markov models (HMMs)  and position-weight matrices (PWMs) [11, 12]. The PWM has one column for each position in the binding site and one row for each nucleotide. Each of the matrix elements is proportional to the relative frequency of the corresponding nucleotide at each position, and the score for a particular site is the sum of the matrix values for the sequence. Therefore, PWM is often used to predict nucleotide-protein binding sites and is used in the TRANSFAC database, which covers many known TFs and binding sites . This approach is a convenient way to identify positions that are more highly conserved than others in a whole genome, because any sequence that differs from a consensus sequence has a lower score. The accuracy of detecting promoter sequences thus depends on the conservation of TF-binding sites.
We can now use complete genomic DNA sequences from several species and analyze massive amount of data on differential gene expression in microarray experiments . Using microarrays in various conditions, we can obtain co-expression patterns for adjacent genes, which is an important property for determining transcription units.
In the present study, we identify the TUs in B. subtilis using a combination of (i) a bioinformatics approach, using PWM methods that identify TF-binding sites by detecting statistically significant TF-binding sequences on the genome; and (ii) two DNA microarray analyses, one to predict co-expressed gene clusters by calculating Pearson correlation coefficients of expression profiles for neighboring genes, and the other to determine genes regulated by each of the TFs in the units by comparing gene expression between wild-type and TF deletion mutants in the genome.
The integrated strategy for TU prediction
Third, we derived significant expression change data from TF deletion mutant microarray data to identify genes regulated by each of the TFs (Fig. 1(3)). In these analyses, we applied a one-sided test to examine genes whose expression changed significantly in the normalized microarray data, and found candidate up-regulated genes for 28 TFs, including 11 σ factors and candidate down-regulated genes for 17 TFs. Furthermore, we used the false discovery rate (FDR) procedure to remove false-positive data from the candidates of significant expression change data and narrow the candidates for genes regulated by each of the TFs .
We then integrated these analyses, and present a comparison of coverage (Fig. 2B1) and sensitivity (Fig. 2B2) between two integrated conditions (i.e. a 5% threshold at PWM and a 5% threshold in the deleted mutant array without FDR, and a 1% threshold at PWM and a 5% threshold in the deleted mutant array with FDR). We were able to narrow down the candidate genes composing TUs at the 5% PWM without FDR to 24.5% of the candidates (i.e. from 194 to 50 genes) when we changed the condition from the PWM 5% threshold without FDR to the PWM 1% threshold with FDR, with 87.1% of genes detected at 5% PWM without FDR also being detected at 1% PWM with FDR. Thus, the candidates can be effectively narrowed without remarkable loss of regulation-known genes under the condition of 1% PWM with FDR. The σL, PerR, and PurR TUs were efficiently detected. Regarding the σL TUs in particular, we could narrow down the 63 TU candidates for the PWM 5% threshold to 9 candidates for the PWM 1% threshold with FDR control without any loss of sensitivity. The detected TUs for the 1% PWM with FDR and known TUs regulated by each of the TFs are listed in Additional file 1.
Organization of TUs in B. subtilis
In the present study, we identified various sizes of TUs regulated by each TF and detected gene clusters consisting of part of well-known operons (yabPQ regulated by σE and divIC-yabR regulated by σX in yabMNOPQ-divIC-yabR operon, nasDEF regulated by GlnR in nasBCDEF operon, yjmEFGHIJ regulated by σE in yjmABCDEFGHIJ operon, spoVE-murG regulated by σE in murE-mraY-murD-spoVE-murG-murB-divIB-ylxWX-sbp operon, xynB regulated by XylR in ynaJ-xynB operon and yoxB-yoaA regulated by σB in yoxCB-yoaA operon mentioned in Additional file 1). They are known to be regulated by internal promoters and to constitute functional components , for instance, yabPQ regulated by σE that plays an important role in synthesis of the spore cortex and coat , and divIC-yabR regulated by σX which is essential for the initiation of vegetative septum formation [20, 21] in yabMNOPQ-divIC-yabR operon. Therefore, these gene clusters separated by internal promoters tend to be functional units.
The similarity of the roles in cellular process between individual TFs was estimated using Pearson correlation coefficients for the number of genes belonging to each of the COG categories (Fig. 4). The five σ factors associated with regulation of the sporulation process can be classified into three groups corresponding to the sporulation process Stage 0-III (σH, σF and σE) characterized by category [J; translation, ribosomal structure and biogenesis], Stage IV (σG) characterized by the category [G; carbohydrate transport and metabolism], and Stage V (σK) characterized by category [M; cell envelope biogenesis and outer membrane]. Gene expression under the σG control occurs in the prespore, and the main functions are to protect the spore from several hazardous conditions, high osmotic pressure , UV radiation and dry heat , and to prepare the spore for germination and outgrowth . In this process, σG regulates carbohydrate content in the cell, for example, by activating expression of the glucose dehydrogenase operon , controlling metabolism of the tricarboxylic acid cycle  and glucose uptake . σK is synthesized and becomes active in the mother cell, and directs formation of the spore coat and spore maturation . Therefore, these previous experimental studies are consistent with the present results. Moreover, we can observe that each TF in a cluster has one of the frequently detected functional categories (Fig. 4). The AraR protein is well known as a negative regulator of the L-arabinose metabolic operon , and most of the genes negatively regulated by AraR belong to [G] (Fig. 4). Almost all the genes up-regulated by SinR are in category [N], which consists of proteins controlling cell motility and secretion, while the down-regulated genes belong to category [M], which consists of proteins operating cell-wall and membrane biogenesis (Fig. 4). ComK synthesis is regulated by a series of reactions that involve quorum sensing; SinR is one of the activators in this cascade, acting negatively on rok transcription , and is known to be a potent repressor of biofilm formation . Thus, the analysis presented here agrees well with previous experimental data and enables us to assess the roles of the σ factors and TFs in cellular processes.
It can also be seen in another cluster composed of Fur, Zur, IolR, PurR, RocR, and GlnR (a broken line circle in Fig. 5A). Fur and Zur regulate the expression of ABC transporters and both TFs control iron and zinc uptake and homeostasis pathways in response to available metals [42, 43]. IolR and PurR also control transport systems. IolR regulates genes encoding inositol transporters and inositol uptake , while PurR regulates purine transport, metabolism, and biosynthetic pathways . In this cluster, RocR and GlnR relate to controlling nitrogen sources: RocR controls arginine catabolism  and the arginase pathway in which arginine is converted to glutamate , while GlnR regulates responses to nitrogen availability, such as nitrogen metabolism  and assimilation .
Based on these previous studies, this result shows that we can cluster together homeostatic regulation TFs (Fig. 5A). Moreover, σ factors that regulate sporulation (σE, σF, σG, σH, σK) tend to exist near the y-axis in the region of lower first-principal component (PC1) values with negative PC2 values, and TreR, SinR, and CcpC are also plotted near the σ factors (Fig. 5A). TreR regulates trehalose as the sole carbon and energy source of B. subtilis during spore outgrowth , while SinR controls regulatory genes involved in the early stages of sporulation . Thus, sporulation-related TFs tend to have lower PC1 values and negative PC2 values, which may be evidence that category [1.9; sporulation] and [1.4; germination] are plotted in the area (Fig. 5B). Therefore, CcpC is known to be a regulator of the tricarboxylic acid cycle genes , but may also have a function in regulating sporulation genes.
This study presented the new approach to TU prediction in the bacterial whole genome using integrated analysis of microarray and DNA sequence data, and we efficiently detected genes composing TUs in B. subtilis genome. The results demonstrate that the combined approach is very useful for identifying unknown TUs in the genome, and also detecting internal operons in the known operons. This methodology should contribute to studies of predicting TU locations in the bacterial genome and estimating roles of TFs.
Bacterial strains, medium, growth conditions and RNA extraction
For expression profile analyses, B. subtilis 168 was grown in 13 different time-series growth conditions: anaerobic growth; competent medium; cold-shock experiments; DSM medium; DGG medium; glucose-limited medium; heat-shock experiment; LB medium; minimum-glucose medium; sodium-shock conditions; phosphate-starvation medium; and SOS stress experiments. For TF deletion mutant analyses, TF deletion mutants were grown at 37°C in different medium conditions: LB medium for sigB, L, M, W, X, araR, ctsR, hrcA, iolR, lmrA, rocR, sinR, xylR deletion mutants; LB medium with trace elements for fur and perR deletion mutants; DSM medium for sigD, E, F, G, H, K, treR deletion mutants; DSM medium with 2% Gln and 5% glucose for, respectively, glnR and resD deletion mutants; MC medium for the comK deletion mutant; MGM medium for the ccpC deletion mutant; and MGM with adenine and guanine for the purR deletion mutant. Cells were harvested by centrifugation at 1,000 g after adding the RNA-protecting Bacteria Reagent (Qiagen), and then stored at -80°C. Two independent samplings were performed. RNA was isolated using the RNA protectant, RNeasy Mini and RNase-free DNase kits (Qiagen) according to the manufacturer's instructions and stored at -80°C. Genomic contamination was estimated by gel electrophoresis.
For each labeling reaction, a total of 15 μg of RNA was used. First-strand cDNA synthesis was primed with 1.2-μg random primers (Invitrogen) in nuclease-free water (total volume: 31 μl) by heating at 70°C for 10 min and incubating at 25°C for an additional 10 min. Reverse transcription was performed by SuperScript III (Invitrogen) in reverse transcription buffer [1 × first-strand buffer, 10 mM DTT] in the presence of 5 mM dATP, 5 mM dUTP, 5 mM dCTP, 0.25 mM dTTP, and 0.25 mM AA-dUTP. Three amino-allyl-labeled nucleotides were incorporated into the cDNA. The reactions were incubated at 25°C for 10 min, 37°C for 60 min, 42°C overnight, and quenched by heating at 70°C for 10 min.
The RNA template was hydrolyzed by adding 20 μl of 1N NaOH followed by heating at 65°C for 30 min. Reactions were neutralized with 20 μl of 1N HCl. cDNA was purified using a CyScribe GFX Purification Kit (GE Healthcare) according to the manufacturer's directions. NHS ester forms of Cy3 and Cy5 dyes were added to the cDNA solution and incubated for 4 hr. Coupling reactions were quenched by the addition of 15 μl of 4 M hydroxylamine and incubated at room temperature for 15 min in the dark. Labeled cDNA was purified using the CyScribe GFX Purification Kit again.
Hybridization and spot detection
Prehybridization of the array slides was performed for 3 hr in filtered prehybridization solution [25% formamide, 5× SSC, 10 mg BSA (fraction V), 0.1% SDS] at 42°C. Slides were briefly washed in milliQ water and 80% ethanol and dried by centrifugation at 1,000 g for 5 min. Hybridization of the probe was performed using hybridization solution (25% formamide, 5× SSC, 0.1% SDS, 0.1 μg poly(A), 1 × Denhardt's solution and 100 pmol Cy3 and Cy5 combined probe). The hybridization solution containing the Cy-dye-labeled cDNA was heated to 95°C for 3 min and hybridization was performed in an Advalytix hybridization machine (ArrayBooster) at 42°C for 16 hr. After hybridization, the slides were washed and dried by centrifugation at 1,000 g for 5 min and then analyzed using a Fuji FLA-8000 scanner and Array Gauge ver.2.0 software (Fuji Film).
Normalization in microarray experiments
Gene expression levels are evaluated by scanning the fluorescence intensity for each spot, and there is usually some experimental variation that occurs in every microarray experiment. It is, therefore, important to minimize experimental variation, and although several methods of microarray normalization have been developed [53, 54], there are usually some false-positive data arising when analyzing gene expression data collected via microarrays.
Normalization of the logarithmic ratio of expression intensity between target (Ri) and control (Gi) experiments was carried out based on MA plots , which can show the intensity-dependent ratio of raw microarray data using TREBAX . The plots differed in the axes used. The MA plot used Mi (log10 (Ri/Gi)) as the y-axis and Ai (log10 ) as the x-axis. By plotting values of Ai on the abscissa and Mi on the ordinate of a coordinate system, it was possible to evaluate the bias error with respect to the average logarithmic intensities. The normalized log ratio M"i was estimated as the difference between Mi and baseline M'i. Here, using the relation between Mi and Ai (Mi = f (Ai) + ε i , where ε i is the difference between Mi and f (Ai) for gene i) for the MA plot, the baseline for the i th gene was estimated by M'I = f (Ai). Genes whose signal intensity was regarded as zero were eliminated from the present analysis. With this methodology, it is assumed that there was no large error due to expression intensity in the majority of the spots.
Prediction of co-expressed gene clusters
Co-expressed gene clusters were predicted based on expression profiles and genomic locations. The expression profile of the i th position gene is represented by vector x i , consisting of logarithmic ratios for microarray experiments. The algorithm for predicting co-expressed gene clusters is as follows: we selected a series of genes transcribed in the same direction with no intervening gene transcribed in the opposite direction. The genes were denoted g1, g2, ... gi, ..., gM from their 5' to 3' termini. Here, gi and gi+1 (i = 1, 2, ..., M-1) are adjacent genes on the same DNA strand. First, Pearson correlation coefficients (rst) were estimated for all pairs of vectors x s and x t (s = 1, 2, ..., M; t = 1, 2, ..., M). Second, a pair of genes was assigned to a candidate group. Gene gs always belonged to group Gs (s = 1, 2, ..., M). All the genes gs+1, gs+2, ..., gs+Ts, whose correlations rs(s+1), rs(s+2), ..., rsTs were statistically significant in a t-test at the 5% significance level, were classified into Gs. In the same manner, all the genes gs-1, gs-2, ..., gs-Us, whose correlations rs(s-1), rs(s-2), ..., rsUs were statistically significant in a t-test at the 5% significance level, were also classified into Gs. Thus, altogether Ts + Us + 1 genes were classified into group Gs. Finally, all members of group Gs (s = 1, 2, ..., M) were compared. We counted the number of groups consisting of identical members among Gs (s = 1, 2, ..., M) and selected the group having the highest count as the first co-expressed gene cluster T1. After excluding the T1 genes from all the groups (g1 to gM), we selected the next-highest identical group as the next co-expressed gene cluster T2. This procedure was carried on until the number of members in Tv was zero, or until all positions j (j = 1, 2, ..., M) were occupied by genes belonging to Tv.
Identification of promoter sequences by PWM
DNA sequences recognized by TFs consist of consensus regions. We searched for sequences highly homologous to those known to be recognized by TFs using PWM. First, we prepared datasets of training sequences consisting of experimentally determined promoters from DBTBS  and "B. subtilis and Its Closest Relatives: from Genes to Cells" , which were aligned on the basis of their consensus regions. PWMs for individual TFs were constructed by the frequencies F Ak , F Tk , F Gk , and F Ck of the four nucleotides (A, T, G, C) in the k th position, including the consensus regions and the five bases upstream and downstream. We determined the score by multiplying all the frequencies corresponding to a given sequence. Second, the thresholds for the binding sites were determined as follows: 4,000 DNA sequences each comprising 300 nucleotides were generated randomly based on the GC content of B. subtilis. The threshold was defined by the value below which the lowest 95% of the maximum scores in individual DNA sequences were excluded. Third, within the 300-nucleotide sequence upstream of the protein-coding region, individual TF binding sites were predicted by the maximum PWM score above the threshold because about 95% of TF binding sites were known to exist in these regions. We chose optimal matrices for each random sequence, and regarded sequences that exceeded the threshold as being regulated by the TF. Therefore, we used these sequences to search for other sequences highly similar to those recognized by TFs. This was done by calculating scores for the partial sequences in the stretch of 300 nucleotides upstream of the protein-coding regions of all B. subtilis genes. Sequences whose scores exceeded a threshold were regarded as TF-binding sites.
Expression analysis of TF deletion mutants of B. subtilis
The normalized fluorescence intensity data were analyzed using a one-sided test to compare the results of the deletion mutant to the control samples, and genes whose expression exceeded the threshold were regarded as TF-regulated genes. In lower one-sided tests, we considered genes of decreased expression as being up-regulated by the TF, whereas genes of increased expression were considered as down-regulated by the TF in upper one-sided tests.
False discovery rate
For separating inactive genes from those that were deemed active in the expression analysis of TF deletion mutants, we used the false-discovery rate, an alternative approach to multiple testing . On the assumption that we conducted m multiple tests, the null hypothesis that each gene is differentially expressed is true for m0 tests, and the alternative hypothesis is true for m 1 (= m - m0). Among the m0 null hypotheses, U hypotheses were declared false-negative and V (= m0 - U) hypotheses were declared true-positive. Among the m 1 alternative hypotheses, T hypotheses were called true-negative and S (= m 1 - T) hypotheses were called false-positive. R (= V + S) is the total number of hypotheses rejected and an observable random variable. The FDR was defined as π0 = P (R > 0) E (V/R | R > 0), and we thus regarded R (1 - π0) as the number of true active genes.
We wish to thank the reviewer for appropriate and kind comments, and Ian R. L. Smith of Nara Institute of Science and Technology for his valuable discussions. This work was supported by a Grant-in-Aid for Scientific Research on Priority Areas, "Systems genomics", from the Ministry of Education, Culture, Sports, Science and Technology of Japan.
- Kunst F, Ogasawara N, Moszer I, Albertini AM, Alloni G, Azevedo V, Bertero MG, Bessieres P, Bolotin A, Borchert S: The complete genome sequence of the gram-positive bacterium Bacillus subtilis. Nature. 1997, 390 (6657): 249-256. 10.1038/36786.PubMedView ArticleGoogle Scholar
- Ermolaeva MD, White O, Salzberg SL: Prediction of operons in microbial genomes. Nucleic Acids Res. 2001, 29 (5): 1216-1221. 10.1093/nar/29.5.1216.PubMed CentralPubMedView ArticleGoogle Scholar
- Huerta AM, Salgado H, Thieffry D, Collado-Vides J: RegulonDB: a database on transcriptional regulation in Escherichia coli. Nucleic Acids Res. 1998, 26 (1): 55-59. 10.1093/nar/26.1.55.PubMed CentralPubMedView ArticleGoogle Scholar
- Salgado H, Moreno-Hagelsieb G, Smith TF, Collado-Vides J: Operons in Escherichia coli : genomic analyses and predictions. Proc Natl Acad Sci USA. 2000, 97 (12): 6652-6657. 10.1073/pnas.110147297.PubMed CentralPubMedView ArticleGoogle Scholar
- Wang L, Trawick JD, Yamamoto R, Zamudio C: Genome-wide operon prediction in Staphylococcus aureus. Nucleic Acids Res. 2004, 32 (12): 3689-3702. 10.1093/nar/gkh694.PubMed CentralPubMedView ArticleGoogle Scholar
- Westover BP, Buhler JD, Sonnenburg JL, Gordon JI: Operon prediction without a training set. Bioinformatics. 2005, 21 (7): 880-888. 10.1093/bioinformatics/bti123.PubMedView ArticleGoogle Scholar
- Yada T, Nakao M, Totoki Y, Nakai K: Modeling and predicting transcriptional units of Escherichia coli genes using hidden Markov models. Bioinformatics. 1999, 15 (12): 987-993. 10.1093/bioinformatics/15.12.987.PubMedView ArticleGoogle Scholar
- Ogasawara N, Moriya S, Yoshikawa H: Structure and function of the region of the replication origin of the Bacillus subtilis chromosome. IV. Transcription of the oriC region and expression of DNA gyrase genes and other open reading frames. Nucleic Acids Res. 1985, 13 (7): 2267-2279. 10.1093/nar/13.7.2267.PubMed CentralPubMedView ArticleGoogle Scholar
- Schujman GE, Paoletti L, Grossman AD, de Mendoza D: FapR, a bacterial transcription factor involved in global regulation of membrane lipid biosynthesis. Dev Cell. 2003, 4 (5): 663-672. 10.1016/S1534-5807(03)00123-0.PubMedView ArticleGoogle Scholar
- Moreno-Campuzano S, Janga SC, Perez-Rueda E: Identification and analysis of DNA-binding transcription factors in Bacillus subtilis and other Firmicutes – a genomic approach. BMC genomics. 2006, 7: 147-10.1186/1471-2164-7-147.PubMed CentralPubMedView ArticleGoogle Scholar
- Stormo GD: DNA binding sites: representation and discovery. Bioinformatics. 2000, 16 (1): 16-23. 10.1093/bioinformatics/16.1.16.PubMedView ArticleGoogle Scholar
- Qiu P, Qin L, Sorrentino RP, Greene JR, Wang L, Partridge NC: Comparative promoter analysis and its application in analysis of PTH-regulated gene expression. J Mol Biol. 2003, 326 (5): 1327-1336. 10.1016/S0022-2836(03)00053-6.PubMedView ArticleGoogle Scholar
- Heinemeyer T, Wingender E, Reuter I, Hermjakob H, Kel AE, Kel OV, Ignatieva EV, Ananko EA, Podkolodnaya OA, Kolpakov FA: Databases on transcriptional regulation: TRANSFAC, TRRD and COMPEL. Nucleic Acids Res. 1998, 26 (1): 362-367. 10.1093/nar/26.1.362.PubMed CentralPubMedView ArticleGoogle Scholar
- Kobayashi K, Ogura M, Yamaguchi H, Yoshida K, Ogasawara N, Tanaka T, Fujita Y: Comprehensive DNA microarray analysis of Bacillus subtilis two-component regulatory systems. J Bacteriol. 2001, 183 (24): 7365-7370. 10.1128/JB.183.24.7365-7370.2001.PubMed CentralPubMedView ArticleGoogle Scholar
- Fujita Y, Fujita T: The gluconate operon gnt of Bacillus subtilis encodes its own transcriptional negative regulator. Proc Natl Acad Sci USA. 1987, 84 (13): 4524-4528. 10.1073/pnas.84.13.4524.PubMed CentralPubMedView ArticleGoogle Scholar
- Henikoff S, Haughn GW, Calvo JM, Wallace JC: A large family of bacterial activator proteins. Proc Natl Acad Sci USA. 1988, 85 (18): 6602-6606. 10.1073/pnas.85.18.6602.PubMed CentralPubMedView ArticleGoogle Scholar
- Pawitan Y, Michiels S, Koscielny S, Gusnanto A, Ploner A: False discovery rate, sensitivity and sample size for microarray studies. Bioinformatics. 2005, 21 (13): 3017-3024. 10.1093/bioinformatics/bti448.PubMedView ArticleGoogle Scholar
- Gao G, Le D, Huang L, Lu H, Narumi I, Hua Y: Internal promoter characterization and expression of the Deinococcus radiodurans pprI-folP gene cluster. FEMS Microbiol Lett. 2006, 257 (2): 195-201. 10.1111/j.1574-6968.2006.00169.x.PubMedView ArticleGoogle Scholar
- Asai K, Takamatsu H, Iwano M, Kodama T, Watabe K, Ogasawara N: The Bacillus subtilis yabQ gene is essential for formation of the spore cortex. Microbiology. 2001, 147 (Pt 4): 919-927.PubMedView ArticleGoogle Scholar
- Huang X, Helmann JD: Identification of target promoters for the Bacillus subtilis sigma X factor using a consensus-directed search. J Mol Biol. 1998, 279 (1): 165-173. 10.1006/jmbi.1998.1765.PubMedView ArticleGoogle Scholar
- Levin PA, Losick R: Characterization of a cell division gene from Bacillus subtilis that is required for vegetative and sporulation septum formation. J Bacteriol. 1994, 176 (5): 1451-1459.PubMed CentralPubMedGoogle Scholar
- Hecker M, Schumann W, Volker U: Heat-shock and general stress response in Bacillus subtilis. Mol Microbiol. 1996, 19 (3): 417-428. 10.1046/j.1365-2958.1996.396932.x.PubMedView ArticleGoogle Scholar
- Wiegeshoff F, Beckering CL, Debarbouille M, Marahiel MA: Sigma L is important for cold shock adaptation of Bacillus subtilis. J Bacteriol. 2006, 188 (8): 3130-3133. 10.1128/JB.188.8.3130-3133.2006.PubMed CentralPubMedView ArticleGoogle Scholar
- Marquez-Magana LM, Chamberlin MJ: Characterization of the sigD transcription unit of Bacillus subtilis. J Bacteriol. 1994, 176 (8): 2427-2434.PubMed CentralPubMedGoogle Scholar
- Horsburgh MJ, Moir A: Sigma M, an ECF RNA polymerase sigma factor of Bacillus subtilis 168, is essential for growth and survival in high concentrations of salt. Mol Microbiol. 1999, 32 (1): 41-50. 10.1046/j.1365-2958.1999.01323.x.PubMedView ArticleGoogle Scholar
- Turner MS, Helmann JD: Mutations in multidrug efflux homologs, sugar isomerases, and antimicrobial biosynthesis genes differentially elevate activity of the sigma(X) and sigma(W) factors in Bacillus subtilis. J Bacteriol. 2000, 182 (18): 5202-5210. 10.1128/JB.182.18.5202-5210.2000.PubMed CentralPubMedView ArticleGoogle Scholar
- Tatusov RL, Koonin EV, Lipman DJ: A genomic perspective on protein families. Science. 1997, 278 (5338): 631-637. 10.1126/science.278.5338.631.PubMedView ArticleGoogle Scholar
- Tovar-Rojo F, Cabrera-Martinez RM, Setlow B, Setlow P: Studies on the mechanism of the osmoresistance of spores of Bacillus subtilis. J Applied Microbiol. 2003, 95 (1): 167-179. 10.1046/j.1365-2672.2003.01958.x.View ArticleGoogle Scholar
- Setlow P: Mechanisms for the prevention of damage to DNA in spores of Bacillus species. Annu Rev Microbiol. 1995, 49: 29-54. 10.1146/annurev.mi.49.100195.000333.PubMedView ArticleGoogle Scholar
- Hilbert DW, Piggot PJ: Compartmentalization of gene expression during Bacillus subtilis spore formation. Microbiol Mol Biol Rev. 2004, 68 (2): 234-262. 10.1128/MMBR.68.2.234-262.2004.PubMed CentralPubMedView ArticleGoogle Scholar
- Nakatani Y, Nicholson WL, Neitzke KD, Setlow P, Freese E: Sigma-G RNA polymerase controls forespore-specific expression of the glucose dehydrogenase operon in Bacillus subtilis. Nucleic Acids Res. 1989, 17 (3): 999-1017. 10.1093/nar/17.3.999.PubMed CentralPubMedView ArticleGoogle Scholar
- Magill NG, Cowan AE, Leyva-Vazquez MA, Brown M, Koppel DE, Setlow P: Analysis of the relationship between the decrease in pH and accumulation of 3-phosphoglyceric acid in developing forespores of Bacillus species. J Bacteriol. 1996, 178 (8): 2204-2210.PubMed CentralPubMedGoogle Scholar
- Lorca G, Winnen B, Saier MH: Identification of the L-aspartate transporter in Bacillus subtilis. J Bacteriol. 2003, 185 (10): 3218-3222. 10.1128/JB.185.10.3218-3222.2003.PubMed CentralPubMedView ArticleGoogle Scholar
- Sa-Nogueira I, Mota LJ: Negative regulation of L-arabinose metabolism in Bacillus subtilis : characterization of the araR (araC) gene. J Bacteriol. 1997, 179 (5): 1598-1608.PubMed CentralPubMedGoogle Scholar
- Hoa TT, Tortosa P, Albano M, Dubnau D: Rok (YkuW) regulates genetic competence in Bacillus subtilis by directly repressing comK. Mol Microbiol. 2002, 43 (1): 15-26. 10.1046/j.1365-2958.2002.02727.x.PubMedView ArticleGoogle Scholar
- Kearns DB, Chu F, Branda SS, Kolter R, Losick R: A master regulator for biofilm formation by Bacillus subtilis. Mol Microbiol. 2005, 55 (3): 739-749. 10.1111/j.1365-2958.2004.04440.x.PubMedView ArticleGoogle Scholar
- Sonenshein AL, Hoch JA, Losick RM: Bacillus subtilis and Its Closest Relatives: from Genes to Cells. ASM Press. 2001Google Scholar
- Mirel DB, Chamberlin MJ: The Bacillus subtilis flagellin gene (hag) is transcribed by the sigma 28 form of RNA polymerase. J Bacteriol. 1989, 171 (6): 3095-3101.PubMed CentralPubMedGoogle Scholar
- Derre I, Rapoport G, Devine K, Rose M, Msadek T: ClpE, a novel type of HSP100 ATPase, is part of the CtsR heat shock regulon of Bacillus subtilis. Mol Microbiol. 1999, 32 (3): 581-593. 10.1046/j.1365-2958.1999.01374.x.PubMedView ArticleGoogle Scholar
- Msadek T, Dartois V, Kunst F, Herbaud ML, Denizot F, Rapoport G: ClpP of Bacillus subtilis is required for competence development, motility, degradative enzyme synthesis, growth at high temperature and sporulation. Mol Microbiol. 1998, 27 (5): 899-914. 10.1046/j.1365-2958.1998.00735.x.PubMedView ArticleGoogle Scholar
- Guillen N, Weinrauch Y, Dubnau DA: Cloning and characterization of the regulatory Bacillus subtilis competence genes comA and comB. J Bacteriol. 1989, 171 (10): 5354-5361.PubMed CentralPubMedGoogle Scholar
- Gaballa A, Helmann JD: Identification of a zinc-specific metalloregulatory protein, Zur, controlling zinc transport operons in Bacillus subtilis. J Bacteriol. 1998, 180 (22): 5815-5821.PubMed CentralPubMedGoogle Scholar
- Ollinger J, Song KB, Antelmann H, Hecker M, Helmann JD: Role of the Fur regulon in iron transport in Bacillus subtilis. J Bacteriol. 2006, 188 (10): 3664-3673. 10.1128/JB.188.10.3664-3673.2006.PubMed CentralPubMedView ArticleGoogle Scholar
- Yoshida KI, Aoyama D, Ishio I, Shibayama T, Fujita Y: Organization and transcription of the myo-inositol operon, iol, of Bacillus subtilis. J Bacteriol. 1997, 179 (14): 4591-4598.PubMed CentralPubMedGoogle Scholar
- Ebbole DJ, Zalkin H: Bacillus subtilis pur operon expression and regulation. J Bacteriol. 1989, 171 (4): 2136-2141.PubMed CentralPubMedGoogle Scholar
- Gardan R, Rapoport G, Debarbouille M: Expression of the rocDEF operon involved in arginine catabolism in Bacillus subtilis. J Mol Biol. 1995, 249 (5): 843-856. 10.1006/jmbi.1995.0342.PubMedView ArticleGoogle Scholar
- Belitsky BR, Sonenshein AL: An enhancer element located downstream of the major glutamate dehydrogenase gene of Bacillus subtilis. Proc Natl Acad Sci USA. 1999, 96 (18): 10290-10295. 10.1073/pnas.96.18.10290.PubMed CentralPubMedView ArticleGoogle Scholar
- Fisher SH: Regulation of nitrogen metabolism in Bacillus subtilis : vive la difference!. Mol Microbiol. 1999, 32 (2): 223-232. 10.1046/j.1365-2958.1999.01333.x.PubMedView ArticleGoogle Scholar
- Magasanik B: Genetic control of nitrogen assimilation in bacteria. Annu Rev Genet. 1982, 16: 135-168. 10.1146/annurev.ge.16.120182.001031.PubMedView ArticleGoogle Scholar
- Kennett RH, Sueoka N: Gene expression during outgrowth of Bacillus subtilis spores. The relationship between gene order on the chromosome and temporal sequence of enzyme synthesis. J Mol Biol. 1971, 60 (1): 31-44. 10.1016/0022-2836(71)90445-1.PubMedView ArticleGoogle Scholar
- Cervin MA, Lewis RJ, Brannigan JA, Spiegelman GB: The Bacillus subtilis regulator SinR inhibits spoIIG promoter transcription in vitro without displacing RNA polymerase. Nucleic Acids Res. 1998, 26 (16): 3806-3812. 10.1093/nar/26.16.3806.PubMed CentralPubMedView ArticleGoogle Scholar
- Hanson RS, Cox DP: Effect of different nutritional conditions on the synthesis of tricarboxylic acid cycle enzymes. J Bacteriol. 1967, 93 (6): 1777-1787.PubMed CentralPubMedGoogle Scholar
- Quackenbush J: Microarray data normalization and transformation. Nat Genet. 2002, 496-501. 10.1038/ng1032. 32 SupplGoogle Scholar
- Yang YH, Dudoit S, Luu P, Lin DM, Peng V, Ngai J, Speed TP: Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res. 2002, 30 (4): e15-10.1093/nar/30.4.e15.PubMed CentralPubMedView ArticleGoogle Scholar
- Dudoit S, Fridlyand J, Speed T: Comparison of discrimination methods for the classification of tumors using gene expression data. J Am Stat Ass. 2002, 97 (457): 77-87. 10.1198/016214502753479248.View ArticleGoogle Scholar
- TREBAX. [http://kanaya.naist.jp/~skanaya/Web/software/trebax/trebax2.html]
- DBTBS. [http://dbtbs.hgc.jp/]
- Benjamini Y, Hochberg Y: Controlling the false discovery rate – a practical and powerful approach to multiple testing. J Roy Stat Soc B Met. 1995, 57 (1): 289-300.Google Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.