Development and application of versatile high density microarrays for genome-wide analysis of Streptomyces coelicolor: characterization of the HspR regulon
- Giselda Bucca†1,
- Emma Laing†1,
- Vassilis Mersinias1, 3,
- Nicholas Allenby1,
- Douglas Hurd2,
- Jolyon Holdstock2,
- Volker Brenner2,
- Marcus Harrison2 and
- Colin P Smith1Email author
© Bucca et al.; licensee BioMed Central Ltd. 2009
Received: 2 August 2008
Accepted: 16 January 2009
Published: 16 January 2009
DNA microarrays are a key resource for global analysis of genome content, gene expression and the distribution of transcription factor binding sites. We describe the development and application of versatile high density ink-jet in situ-synthesized DNA arrays for the G+C rich bacterium Streptomyces coelicolor. High G+C content DNA probes often perform poorly on arrays, yielding either weak hybridization or non-specific signals. Thus, more than one million 60-mer oligonucleotide probes were experimentally tested for sensitivity and specificity to enable selection of optimal probe sets for the genome microarrays. The heat-shock HspR regulatory system of S. coelicolor, a well-characterized repressor with a small number of known targets, was exploited to test and validate the arrays for use in global chromatin immunoprecipitation-on-chip (ChIP-chip) and gene expression analysis.
In addition to confirming dnaK, clpB and lon as in vivo targets of HspR, it was revealed, using a novel ChIP-chip data clustering method, that HspR also apparently interacts with ribosomal RNA (rrnD operon) and specific transfer RNA genes (the tRNAGln/tRNAGlu cluster). It is suggested that enhanced synthesis of Glu-tRNAGlu may reflect increased demand for tetrapyrrole biosynthesis following heat-shock. Moreover, it was found that heat-shock-induced genes are significantly enriched for Gln/Glu codons relative to the whole genome, a finding that would be consistent with HspR-mediated control of the tRNA species.
This study suggests that HspR fulfils a broader, unprecedented role in adaptation to stresses than previously recognized - influencing expression of key components of the translational apparatus in addition to molecular chaperone and protease-encoding genes. It is envisaged that these experimentally optimized arrays will provide a key resource for systems level studies of Streptomyces biology.
Streptomycetes represent an unusual and complex bacterial genus. They display a mycelial 'multicellular' life cycle that culminates in sporulation  and possess remarkable metabolic diversity, both in their ability to catabolise complex substrates and in their prodigious capacity to produce chemically diverse 'secondary' metabolites, including the majority of naturally occurring antibiotics and other bioactive compounds used in medicine [2, 3]. These characteristics form the major justification for basic studies of streptomycete biology. Since the completion of the genome sequence of the principal model streptomycete, Streptomyces coelicolor A3(2) , numerous systems-level studies have been initiated, encompassing transcriptomic/proteomic approaches and genome scale metabolic network construction [5–8].
To date, Streptomyces DNA microarray-based studies have been restricted largely to the use of spotted PCR products or pre-synthesized long oligonucleotides, with a single probe representing each gene . Such arrays are not generally suitable for genome wide chromatin immunoprecipitation-on-chip (ChIP-on-chip) analysis of transcription factor binding sites . The ChIP-on-chip technique has become an essential tool for system wide analysis of biological systems (for example, [11–15]) since it provides a comprehensive assessment of the direct targets, in vivo, of the transcription factor/DNA-binding protein under investigation; this is a pre-requisite for reconstructing cellular transcription regulatory networks. Here we report the development of ink-jet in situ synthesized (IJISS) DNA arrays for ChIP-on-chip analysis of S. coelicolor.
Streptomycetes are unusual in possessing genomes of very high G+C content. The S. coelicolor genome is 72.4% G+C and individual coding sequences often exceed 80% G+C. This extreme base composition compromises the design of suitable probes for array-based detection of complementary nucleic acid sequences because G+C-rich probes often hybridize poorly with targets or they display a lack of specificity. Consequently, in this study we adopted an experimental approach to test a large collection of arrayed probes for sensitivity and specificity prior to selecting a subset for the final genome arrays. The objective was to produce a versatile experimentally optimized array that could be used for both genome-wide ChIP-on-chip analysis and global gene expression profiling.
The HspR heat-shock regulatory system of S. coelicolor  was exploited to test and validate the sensitivity and specificity of the IJISS arrays. HspR was selected because it represents a well-characterized repressor with a small number of known targets. Streptomycetes have adopted diverse strategies to rapidly adjust to sudden changes in the environment, for example, from heat stress or other physico-chemical and physiological stresses. As in all living organisms, they induce expression of many genes in response to heat stress, including the well characterized and universally conserved members of the hsp70 (dnaK) and hsp60 (groEL) gene families (see [16–18] for reviews). In Streptomyces and most Gram-positive and Gram-negative bacteria the heat shock stimulon is under the control of negative transcriptional regulators , unlike Escherichia coli where the heat shock stimulon is under the positive regulation of the alternative sigma factors σ32 and σ24 [20, 21]. The heat shock stimulon mostly comprises two major classes of genes encoding, respectively, molecular chaperones and proteases that are induced under conditions that cause protein misfolding/denaturation in order to maintain protein quality control, or eliminate protein aggregates or badly damaged proteins that would otherwise have a deleterious effect on cell survival.
Three negative transcriptional regulators have been characterized in Streptomyces species: HrcA, controlling the groES/EL1 operon and groEL2 (for a review, see ); RheA controlling hsp18 in Staphylococcus albus [23, 24]; and HspR controlling the dnaK operon, clpB molecular chaperone and lon protease-encoding genes [25–27].
The HspR repressor has since been identified in some other bacterial systems: Mycobacterium tuberculosis, where it controls the expression of the hsp70 operon, clpB and acr2 genes ; and Corynebacterium glutamicum , where it controls the clpP1/P2 operon together with two other regulators, ClgR and σH. Furthermore, the HspR system has been reported in other bacteria not belonging to the Actinomycetales family, such as the Gram-negative Helicobacter pylori, where HspR functions in conjunction with HrcA to regulate the groES/EL and hrcA-dnaK-grpE operons [30–32], Deinococcus radiodurans, where HspR controls two novel members of the regulon (hsp20 and ftsH) in addition to known members such as dnaK, dnaJ, grpE, lonB and clpB , Bifidobacterium breve  and Campylobacter jejuni .
In the present study we have optimized methods for chromatin immunoprecipitation and have produced optimized high density arrays for ChIP-on-chip analysis of S. coelicolor. Here we exploit this technology (to our knowledge applied for the first time with Streptomyces) to redefine the HspR regulon of S. coelicolor. The microarray design allows gene expression data to be superimposed for the same probes, enabling discrimination between indirect effects of either over-expressing or disrupting a regulator gene from the direct effect resulting from the in vivo binding of the respective regulator to its target genes. In addition to confirming dnaK, clpB and lon as in vivo targets of HspR, the ChIP-on-chip studies reported here indicate that HspR also has a role in regulation of expression of ribosomal RNA and specific transfer RNA genes, for incorporation of Gln and Glu, the latter potentially linked with tetrapyrrole biosynthesis. This suggests that HspR fulfils a broader role in adaptation to stresses, such as heat-shock, than was previously recognized - influencing expression of key components of the translational apparatus in addition to major molecular chaperone and protease-encoding genes. It is envisaged that these IJISS arrays will find wide application in systems level studies of Streptomyces biology.
Results and discussion
DNA microarray design
Two different S. coelicolor IJISS DNA microarrays were designed, featuring, respectively, 22,000 (Sco-Chip2-v1) and 44,000 (Sco-Chip2-v2) 60-mer oligonucleotide probes. In each case the same experimental optimization approach was used (Figure 1) where a large set (approximately 1 million) of 60-mer probes were printed in parallel with corresponding probes that had a 3-nucleotide mismatch. Cyanine-3 (Cy3) and Cyanine-5 (Cy5)-labeled S. coelicolor genomic DNA was hybridized against the test arrays and the probe performance was scored using the following equations. Firstly the Cy3 and Cy5 background-subtracted signals, designated 'g' and 'r', respectively, obtained by feature extraction of the arrays using the Agilent feature extraction software (Version 188.8.131.52) were entered into the following formula:
where the signal from the perfectly matched probe is designated 'PM' while that from the corresponding mismatched probe is designated 'MM'. For values of A greater than 1, A was set to 1 before entering it into the second equation:
R = [1 - arctan (A × π/2)] × [1 - exp(- (gPM + rPM)/2000)]
The resulting R-value was used to rank all tested probes. The higher the value, the better the probe performance. This method of ranking probes was developed within Oxford Gene Technology Ltd and has been applied to various prokaryotic organisms for empirical microarray probe design.
Sets of probes within a defined region, either gene or intergenic, were ranked. All probes were considered relative to each other without applying thresholds and the desired density of probe coverage was achieved by selecting top-ranked probes where possible. Performing the above experimental optimization approach is, in our opinion, a necessary step, given that approximately 40% of the in silico designed probes failed quality control. Sufficient probe coverage was obtained using fewer than 5% of probes ranked below the median value of the ranking distribution. The remaining 95% of the optimized probe set were picked from probes performing above average with a strong bias for very well performing probes. For both array formats the probes were deposited at random positions on the slide surfaces to minimize the risk of any position-specific artifacts.
All possible 60-mer probes for all targets (both coding and non-coding sequences) in the S. coelicolor genome (based on the S. coelicolor A3(2) [EMBL:AL645882.2]) were designed. For this version of the array all non-coding sequences upstream of protein-encoding genes were selected (sequences where transcription factors are most likely to bind) and multiple 60-mer probes targeting those regions were selected from the 'all possible probes' set. Following this initial selection, a total of 84,268 probes were experimentally tested and the best performing 21,064 probes that represented all upstream intergenic regions (an average of 3 approximately 110 bp spaced probes to each upstream site) in the genome were synthesized on the array. As this array design was developed specifically for ChIP-on-chip experiments, all probe sequences corresponded to one strand only (that in [EMBL:AL645882.2]) since the particular DNA strand was unimportant. (Note that intergenic regions flanked by transcription terminators for convergently transcribed genes were not selected for this array.)
From the 'all possible probes' set (see above), 964,820 60-mer probes were selected and printed to target all coding and non-coding sequences with minimal distance between the probes and maximal coverage of the genome. Following experimental validation of probe signal and specificity, 43,798 of the best performing probes were selected to give broad coverage. Probes within protein coding sequences corresponded to the mRNA strand for (cDNA-based) detection of gene expression. For intergenic regions the probe sequences corresponded to one strand only (that in [EMBL:AL645882.2]). The average spacing of probes in the genome was approximately 135 bp.
Genome-wide identification of in vivoHspR binding sites
The experimentally optimized Sco-Chip2-v1 and Sco-Chip2-v2 arrays were used consecutively to identify in vivo targets of HspR. The latter array was designed to also enable quantification of gene expression. In order to validate the sensitivity and specificity of these arrays, we chose the well-studied transcriptional repressor HspR, which was previously known to bind to only three promoter regions in the genome of S. coelicolor: upstream of the dnaK operon; the protease-encoding gene lon; and the clpB gene, which is transcribed in an operon with SCO3660. These results were based on transcriptome analysis of an hspR disruption mutant and complementary in silico genome wide searches for HspR binding sites .
The respective nucleotide sequences of the new putative stable RNA targets of HspR had been excluded from Sco-Chip2-v1 because they are non-protein-coding and are positioned between convergently transcribed genes. The discovery that HspR may regulate specific tRNA and rRNA genes is unprecedented and suggests a more global role for HspR in the stress response of Streptomyces. The HspR-specific probe enrichments in the previously known and the new putative HspR targets are shown in Figure 3 (and Additional data file 3).
The heat-shock stimulon of S. coelicolor
The versatility of the Sco-Chip2-v2 design allowed us to detect gene expression using the same probe set. Thus, in Figure 3 the expression data from two pairs of comparisons are also superimposed on the ChIP enrichment data: the ratio of expression from hspR disruption mutants relative to the wild-type strain; and the ratio of expression from cultures heat-shocked at 42°C relative to non-heat-shocked control cultures. It is noted that the observed reduction of relative transcript levels of operonic genes more distal from the operon promoter (as in the dnaK operon; Figure 3a) is consistent with general observations of polarity of expression of Streptomyces operons .
The gene expression studies were conducted using RNA samples from strains cultivated on supplemented minimal medium agar plates, rather than rich liquid medium. This was for several reasons. First, the magnitude of the heat-shock response is relatively lower and less reproducible in heat-shocked mycelium cultivated in the rich YEME+10.3% sucrose liquid medium, compared with the heat-shock response of the surface-grown minimal medium cultures. Second, the hspR disruption mutants used in this study are unstable because hspR is an essential gene [6, 27]. The disruption of hspR is via a single integration event of a non-replicating plasmid and there is a strong selective pressure for its excision. Thus, in liquid culture the mycelium in which the disruption plasmid has excised outgrows the mutant mycelium, which is at a growth disadvantage, leading to a dominant wild-type revertant phenotype. In surface-grown cultures this reversion is markedly attenuated, where a low frequency of reversion maintains viability. Third, the RNA samples used here for comparison with the ChIP data have been extensively validated by other methods [6, 27]. The above experiment allowed, for the first time, a comprehensive identification of the heat-shock stimulon of S. coelicolor at the transcriptome level, where rank products analysis revealed 119 up-regulated genes (based on a probability of false prediction (pfp) threshold of <0.15 (see Materials and methods)) as a result of heat-shock (Additional data file 4). The use of such thresholds has been reported elsewhere [7, 37].
Two genes on the heat-shock list with relatively high pfp values (SCO3202, pfp = 0.12; SCO4157, pfp = 0.13) were selected for independent validation by quantitative real time PCR (qPCR) to confirm true heat-shock induction (Additional data file 9); furthermore, SCO3660, a known member of the HspR regulon , had a pfp value of 0.12. This justified the use of the pfp threshold adopted here. The significantly up-regulated heat shock genes include all members of the dnaK operon, clpB, lon and the chaperonin-encoding groES-groEL1 operon and groEL2. Two more protease-encoding genes are also present in the heat-shock list: SCO4157, encoding the homologue of E. coli HtrA, a serine protease involved in degradation of periplasmic misfolded proteins, and SCO6515. Notably, eight oxidoreductase-encoding genes are present, some of them being strongly up-regulated by heat-shock and transcribed in an operon (SCO1131-SCO1134). The operon encoding different subunits of the nitrate reductase (SCO0216-SCO0219) and the nitrite/nitrate transporter-encoding gene SCO0213 are also heat-inducible together with the principal 'gas vesicle protein'-encoding operon (SCO6499-SCO6508) , the cytochrome oxidase-encoding genes SCO3945-SCO3946 and two genes of sigE operon (sigE (SCO3356) and the lipoprotein-encoding gene cseA (SCO3357)) . It is of interest that more than 10% of the heat-shock-induced genes (14) encode transcriptional regulators and included SCO0174 (the most induced), five sigma factors (HrdD (SCO3202), SigB (SCO0600), SigE (SCO3356), SigL (SCO7278) and SigM (SCO7314)) and an anti-sigma factor antagonist (SCO7325). A separate, complementary analysis of the heat-shock response in wild-type S. coelicolor cultivated under identical conditions in YEME to those used for the ChIP-on-chip studies demonstrated that most of the above 119 heat-shock induced genes (102/119) were also heat-induced in the YEME medium (Additional data file 11); however, the level of induction of the well-known molecular chaperone-encoding genes was attenuated relative to the surface grown SMMS cultures. It is interesting to note that 16 of the 17 genes not heat-induced in the YEME cultures are clustered in a discrete region at the left end of the chromosome between SCO162 and SCO219; this may reflect the differences in the widely different nutritional compositions of the two growth media and these genes may require additional transcription factors for their induction.
The list of 55 genes significantly up-regulated in an hspR disruption mutant relative to the wild-type is presented in Additional data file 5 (cut-off pfp < 0.15). It includes all previously known members of the HspR regulon and other notable genes that, on the basis of the ChIP-on-chip analysis, are not considered to be directly controlled by HspR; their induction could be a consequence of the up-regulation of molecular chaperone or protease-encoding gene expression in the HspR mutant. Genes for five putative transcriptional regulators are represented in the list.
New putative targets of HspR
The sensitivity of the IJISS arrays was deduced to be high since all previously known HspR targets were identified on both array designs. SCO5639 was not identified as belonging to the HspR regulon in a previous study . SCO5639 encodes a hypothetical protein of 176 amino acids in length and, unusually for streptomycete genes, has a low G+C content (approximately 53%) and is flanked by genes also of low G+C content: SCO5638 (55% G+C), which encodes an integral membrane protein, and SCO5640 (54% G+C), which encodes a hypothetical protein. Moreover, the adjacent gene, SCO5641, encodes a putative transposase, suggesting that SCO5639 could have been laterally acquired recently. Pfam  searches of the deduced amino acid sequence of SCO5639 returned the 'domain of unknown function' DUF1863, corresponding to a domain that adopts the 'flavodoxin fold' with "a probable role in signal transduction as a phosphorylation-independent conformational switch protein". Other proteins that contain this domain (37 known in total, including another actinomycete, Corynebacterium efficiens) are also uncharacterized. Similarly, blastp [41, 42] results identified further hypotheticals (at E-value < 1e-10) and found a similarity, albeit low (35% identity), to the phosphorylation site of the calcineurin temperature suppressor (Cts1) of Cryptococcus neoformans (a yeast), which is responsible for restoring growth of calcineurin mutant strains at 37°C among other functions such as cell separation and hyphal elongation . This link with temperature would be consistent with SCO5639 being a target of HspR.
The HspR binding motif
In vitroanalysis of new HspR targets
Stable RNA genes as putative targets for HspR
The observation that HspR appears to bind to the promoter region of the rrnD operon and to multiple sequences within the five-tRNAGln/Glu cluster is unprecedented. Other than the previously known HspR targets, these were the only two regions identified as statistically significant by the data clustering method reported in this study for the Sco-Chip2-v2-derived data. Previous studies would not have identified stable RNA genes as potential targets because representative probes had not been printed on the arrays. Furthermore, the typical 'transcription factor binding site' consensus sequence identification technique is based on searching the upstream regions of protein-encoding genes and/or a set threshold is applied in silico, which may not be able to simulate the true in vivo binding that occurs. Indeed, the in silico analysis carried out in this study (discussed in Materials and methods), revealed only partial recognition of the defined HspR consensus (Additional data file 6) whilst the in vivo data (ChIP-on-chip enrichment ratios) indicate HspR binding. Thus, it is likely that other transcription factors also bind to these regions, or that other DNA sequences facilitate HspR-binding, and it is possible that such factors could positively influence the binding of HspR, relieving its dependence on a substantial consensus sequence match. In this context it is notable that the most highly enriched probes flank both the beginnings and ends of the rrnD operon and five-tRNA cluster (Figure 3d,e, respectively); it is conceivable that HspR forms a looped complex at both of these stable RNA-encoding regions. A MEME analysis (see Materials and methods) revealed a non-palindromic motif shared between the HspR targets identified with the Sco-Chip2-v2 array (Additional data file 7); the biological significance of this motif is not clear.
HspR regulates the DnaK chaperone machine, a system that plays an important role in the cotranslational folding of proteins  in addition to assisting folding of unfolded or partially unfolded mature polypeptides. It could be rationalized that HspR inactivation also facilitates expression of rRNA and tRNAs following heat-shock, or other stresses, as part of the transient adaptive response to environmental stresses. From the gene expression analysis (Figure 3; Additional data files 14 and 15) there is a detectable enhancement (albeit small) of rrnD and tRNAGln/Glu transcript levels in heat-shocked cultures and in an hspR disruption mutant; a high over-representation would not be expected because these particular stable RNA genes are highly expressed under normal growth conditions (data not shown) and a transient (approximately 15 minute) induction of one of the rRNA operons would not have a major impact on the large pre-existing pool of these stable species within the cytoplasm. From averaging of signals from the multiple probes in this region, the increase in the rrnD 16S rRNA transcript level was ≥ 10% following heat-shock and ≥ 20% in an hspR disruption mutant. We suggest that HspR-mediated control of rrnD transcription facilitates the maintenance of rRNA transcription following heat-shock. There are precedents for heat-stimulated transcription of rRNA operons in both Streptomyces and E. coli. León and Mellado demonstrated partial heat-shock stimulation of some rRNA promoters in the closely related species S. lividans . In E. coli the heat-shock sigma factor σ32 was shown to direct transcription of the rrnB P1 promoter and the authors suggest that σ32-directed transcription of rRNA promoters might play a role in ribosome synthesis at high temperatures . There are also reports of developmental regulation of rRNA and ribosomal protein synthesis in S. coelicolor [49, 50]. The upstream region of rrnD of S. coelicolor displays significant differences from that of other rRNA promoters in this genome (S. coelicolor contains six rrn operons). In this context it is of relevance that a recent study suggests that the p3 and p4 promoters of rrnD are differentially regulated by additional (as yet unidentified) factors . It is tempting to speculate that HspR-mediated induction of rrnD transcription may result in the production of a subset of ribosomes with a specific role in translation of stress-responsive proteins.
Transient stimulation of transcription of the tRNAGln/Glu cluster may lead to an enhancement in the cellular level of uncharged tRNAs - particularly since Gln-tRNAGln formation requires transamidation of Glu-tRNAGln . In this context it might be relevant that the tRNAGln/Glu cluster encodes the only two tRNAGln species in S. coelicolor and the transient accumulation of uncharged tRNAs is known to be a major trigger for the stringent response .
Most organisms contain only one Glu-tRNAGlu species . The tRNAGln/Glu cluster identified in this study encodes three Glu-tRNAGlu species (recognizing the GAG codon); one other Glu-tRNAGlu gene is encoded elsewhere in the S. coelicolor genome and recognizes the GAA codon, which is a very rarely used streptomycete codon. Glu-tRNAGlu has two major roles in the cell. In addition to its role in protein synthesis, Glu-tRNAGlu is a substrate in the first step of tetrapyrrole biosynthesis, to produce heme, for example [54, 55]. Inspection of the predicted protein products from the S. coelicolor genome indicates that this is the only available route for tetrapyrrole biosynthesis in this organism. It is possible, therefore, that there is an enhanced requirement for tetrapyrrole production following heat-shock (and concomitant oxidative stress), to provide heme, for example, for cytochrome biosynthesis and for catalase and superoxide dismutase production and this could be achieved by HspR-mediated regulation of Glu-tRNAGlu expression.
An additional possible explanation for enhanced expression of this sub-set of tRNAs could be that there is a higher transient demand for Gln and Glu in protein synthesis immediately following heat-shock. Transcript levels of two of the five tRNA species was enhanced approximately 10% in hspR disruptants (Figure 3; Additional data file 15). Indeed, the Gln/Glu frequency (the percentage of Gln/Glu codons in a codon set) in the heat-shock up-regulated gene set (Additional data file 4) is higher than that obtained for the entire genome (9.76% versus 8.32%). To estimate the significance of this finding, 10,000 random subsets of genes from the entire genome, of the same size as the up-regulated gene list, were created and their Gln/Glu frequency was calculated. It was found (through the use of the Z-score) that the heat-shock up-regulated gene set had an enhanced Gln/Glu codon frequency compared to any of the random sets, yielding a significance p-value of < 1.06 × 10-7. It is concluded that there is statistically significant enrichment of Gln/Glu codons in the heat-shock up-regulated genes and we speculate that HspR mediates transient stimulation of expression of the relevant tRNAs. Although the biological significance of this finding is not clear, it may be relevant that Glu (and Lys) tend to be over-represented in thermostable proteins . The difference in amino acid composition of the heat-shock genes relative to all genes, for all amino acids and amino acid pairs, is given in Additional data file 8.
High density IJISS DNA arrays have been developed for global analysis of Streptomyces gene expression and transcription factor binding. The HspR regulatory system of S. coelicolor was exploited to validate their sensitivity and specificity. New insights were gained into the possible role of HspR in regulation of cellular physiology - encompassing stable RNA synthesis in addition to molecular chaperone and protease production. It is envisaged that these arrays will find widespread use in systems level analysis of Streptomyces coelicolor biology.
Materials and methods
Streptomycesstrains and culture conditions
For the ChIP-on-chip studies the prototrophic S. coelicolor strain MT1110, a SCP1- SCP2- derivative of the wild-type strain, John Innes Stock Number 1147 , was cultivated in YEME liquid medium plus 10% sucrose at 30°C in a rotary shaking incubator. For the gene expression studies the previously reported two independent hspR disruption mutants, MT1151 and MT1153, were used together with the two independent, otherwise isogenic, hspR+ integrants, MT1152 and MT1154 . The heat-shock conditions were as reported previously .
In order to obtain Streptomyces chromatin of high quality, it was found that rapid, low temperature, physical disruption of the mycelium constituted a more reproducible method than the conventional lysozyme treatment methods. S. coelicolor MT1110 was cultivated at 30°C in 50 ml YEME liquid medium in 250 ml flasks with springs (supplemented with 10% sucrose, glycine and MgCl2 as specified in  up to early stationary phase (OD450 approximately 2.0). Cultures were divided into 20 ml aliquots and formaldehyde treated (final concentration, 1%) for 10 minutes at 30°C in order to in vivo crosslink proteins to DNA; glycine (final concentration of 0.5 M) was added to quench the formaldehyde and the culture was incubated for a further 5 minutes at 30°C. Mycelium was harvested by centrifugation, frozen in liquid nitrogen and then transferred to a 7 ml PTFE shaking flask with cap (which was also immersed in liquid nitrogen to cool it down). Mycelium was disrupted in a Mikrodismembrator U mechanical device (Sartorius Stedim Biotech, Epsom, Surrey, UK) for 2 × 1 minute at 2,000 rpm with one 10 mm diameter chromium steel grinding ball and contents of one tube of lysing matrix B (Q-BIOgene, Cambridge, UK). Chromatin processing and IP were based on previous methods [59, 60] with additional modifications. The pulverized mycelium was transferred to a tube containing 1 ml lysis buffer (10 mM Tris-HCl, pH 8, 20% sucrose, 50 mM NaCl, 10 mM EDTA, Protease Inhibitor Cocktail (Roche, Burgess Hill, West Sussex, UK); one tablet per 10 ml); 3 ml IP buffer (50 mM Tris-HCl, pH 8, 150 mM NaCl, 0.5% Triton X-100, plus Protease Inhibitor Cocktail) was added and the chromatin was sheared by sonication (Sonics VibraCell VCX130, CH-1217 Meyrin/Satigny, Switzerland) on ice. One 2 ml aliquot was sonicated 2 × 20 s power ON at 50% power, 40 s power OFF and the other 2 ml chromatin sample was sonicated 4 × 20 s power ON at 50% power, 40 s power OFF, to obtain the optimal DNA size range of 0.5-1.0 kb. Cell lysates were cleared by centrifugation at 12,000 rpm for 25 minutes. To assess chromatin quality, aliquots of the chromatin (70 μl) were treated with proteinase K (100 μg, Roche) for 2 h and the DNA-protein complexes were de-crosslinked at 65°C for 6 h; 5 μl aliquots were subjected to electrophoresis and the chromatin fraction(s) with optimal size range were subjected to IP either with specific antibody or with no antibody (mock IP) as control. The IgG fraction containing anti-HspR polyclonal antibodies  and the fraction from pre-immune serum from the same rabbit used for immunization were purified through Nab Protein A spin columns (Pierce, ThermoFisher Scientific, Cramlington, Northumberland, UK). Chromatin IP was carried out with 100 μl specific antibody added to 800 μl of chromatin overnight at 4°C on a rotating wheel at 12 rpm; 80 μl of either sepharose protein A (Sigma, Gillingham, Dorset, UK) or Ultralink immobilized Protein A/G beads (Pierce; previously washed twice in phosphate-buffered saline (PBS), once in PBS containing 5 μg/ml bovine serum albumin and resuspended in one half bead volume of PBS containing a protease inhibitor cocktail) were added to the immunoprecipitated chromatin and incubated for a further 3-4 h at 4°C on a rotating wheel at 12 rpm. The DNA-protein complexes bound to the beads were pelleted at 3,300 rpm for 1 minute and washed four times by resuspension in 1 ml ice cold IP buffer (wash 1), IP buffer plus salt (wash 2, as wash 1 but with 500 mM NaCl), wash buffer (wash 3, 10 mM Tris pH 8, 250 mM LiCl, 1 mM EDTA, 0.5% nonidet P-40 and 0.5% Na deoxycholate) and TE pH 7.6 (wash 4), with incubation at 4°C in a rotating wheel for 15 minutes and centrifugation at 3,300 rpm for 1 minute. After the first wash the protein A/G bound DNA protein complexes were transferred to a non-stick microfuge tube.
Immunoprecipitated complexes were eluted overnight at 55°C in Tris-EDTA, pH 7.6 (TE), 1% SDS, 100 μg Proteinase K (Roche) in 240 μl volume. A 170 μl aliquot of input chromatin (not subjected to IP) or mock-IP chromatin was incubated in parallel under the same conditions with 240 μl of elution buffer. Crosslinks were dissociated at 65°C for 30 minutes followed by centrifugation at 3,300 rpm for 1 minute. The protein A/G beads were washed in 50 μl TE and the supernatants were pooled. The immunoprecipitated and input chromatin/mock-IP samples were extracted twice with phenol/chloroform/isoamyl alcohol (25:24:1) pH 8, then once with chloroform and the DNA was ethanol precipitated in the presence of 20 μg glycogen as carrier, resuspended in 20 μl ultrapure water and quantified with a NanoDrop spectrophotometer.
Nucleic acid labeling and IJISS array hybridizations
Immunoprecipitated and input control DNA were labeled with Cy3-dCTP and Cy5-dCTP, respectively, using the BioPrime kit (Invitrogen, Paisley, UK). DNA (0.1-1 μg) was denatured at 94°C for 3 minutes in 40 μl including 20 μl 2.5× random primer mix and kept on ice. Nucleotide mix (5 μl; 2 mM dATP, 2 mM dGTP, 2 mM dTTP, 0.5 mM dCTP), 3.75 μl Cy3/Cy5-dCTP (Perkin Elmer, Beaconsfield, Bucks, UK) and 1.5 μl of Klenow fragment were added and the reaction was incubated at 37°C overnight. The labeled DNA was purified using the MinElute PCR purification kit (Qiagen, Crawley, West Sussex, UK) and the incorporated Cy3/Cy5-dCTP was quantified with the NanoDrop ND-1000 spectrophotometer. For gene expression analysis, cDNA synthesis and labeling were conducted as described previously .
For hybridization on Sco-Chip2-v1 arrays, 40 pmol of Cy3-labeled immunoprecipitated DNA was co-hybridized with the same amount of Cy5-labeled total input chromatin DNA in 500 μl Agilent hybridization buffer (1 M NaCl, 50 mM MES, pH 7, 20% formamide, 1% Triton X-100 buffer), in an Agilent Technologies hybridization chamber, rotated at 55°C for 60 h in an Agilent Technologies hybridization oven. For hybridization on Sco-Chip2-v2 arrays, 10-40 pmol of Cy3-labeled immunoprecipitated DNA were co-hybridized with the same amount of Cy5-labeled control mock IP DNA in 120 μl Agilent hybridization buffer as above. To control for Cy-dye bias, the hybridization was repeated with the same IP DNA samples labeled in the opposite dye orientation. Two biological replicates were hybridized on both array formats.
The arrays were washed once in 50 ml of 6 × SSPE, 0.005% N-lauryl sarcosine and once in 0.06 × SSPE, 0.18% polyethylene glycol 200, both for 5 minutes at room temperature. The arrays were briefly immersed in Agilent Technologies stabilization and drying solution prior to processing in an Agilent Technologies scanner. The probe signals were quantified using Agilent's Feature Extraction software (version 184.108.40.206).
Two different types of dual hybridizations were conducted on the arrays. With the Sco-Chip2-v1 arrays, HspR-IP chromatin was co-hybridized with Cy5-labeled total input chromatin as reference and the mock 'no-antibody' IP chromatin was also co-hybridized with total input chromatin on a separate array; the enrichment ratios for each probe were calculated as the signal from the former divided by that from the latter array. With Sco-Chip2-v2, the HspR-IP chromatin was co-hybridized directly with the mock 'no antibody' IP chromatin - the sample processed in the same way as the HspR-IP, but without the specific antibody. To compensate for any dye bias in the latter experiments, replicate hybridizations were conducted with both Cy3/Cy5 dye orientations on different arrays. It should be noted that the experimental design in terms of hybridizations are different for Sco-Chip2-v1 (IP or mock IP versus total chromatin) and Sco-Chip2-v2 (IP versus mock IP), being comparable, respectively, to 'traditional' microarray (expression) experimental designs of indirect and direct hybridizations. However, both designs are valid since ultimately the same output ratio of interest (IP/mock IP signal) is calculated. The direct hybridization approach, introduced for Sco-Chip2-v2, is preferred because there is likely to be a reduction in variance (as fewer arrays are used to derive the same ratios).
RNA isolation analysis, cDNA synthesis and labeling
The RNA preparations used during this study were the same as those reported in . The RNA was isolated from 36 h S. coelicolor MT1110 cultures grown on SMMS agar, by the method reported previously ; RNA from YEME plus 10% sucrose was isolated from 40 h batch cultures by the RNeasy method described in . RNA quality and integrity was re-assessed using the Agilent Bioanalyzer 2100 system. The Cy3/Cy5-dCTP labeled cDNA was synthesized from 10 μg RNA samples following the methods described in .
Microarray data pre-processing
All (ChIP-on-chip and expression) hybridized arrays were scanned using an Agilent Technologies microarray scanner and resultant intensities calculated using Agilent Technologies Image Analysis and Feature Extraction software (version 220.127.116.11) with local background correction. Log2 signal/reference ratios were calculated for all arrays, the reference channel either representing total input chromatin (Sco-Chip2-v1 array), mock 'no-antibody' IP (Sco-Chip2-v2 array) or cDNA (Sco-Chip2-v2 array).
ChIP-on-chip array data were not normalized as the typical microarray normalization assumptions do not hold . All expression arrays were normalized by the Loess method using the LIMMA package [64, 65] in R (version 2.5.0 [66, 67]). For the heat-shock experiment, across array normalization was applied such that the median absolute deviations (MADs) were similar (scale function in LIMMA); no further normalization was applied to the data from the hspR mutant/wild-type comparison.
Within the output of the Feature Extraction software (Agilent Technologies) there are four binary (1 for bad, 0 for good) variables (gIsFeatNonUnifOL/rIsFeatNonUnifOL, gIsBGNonUnifOL/rIsBGNonUnifOL, gIsFeatPopnOL/rIsFeatPopnOL and gIsBGPopnOL/rIsBGPopnOL) that describe outliers in each channel on an array. Spots on the Sco-Chip2-v1 array were flagged as poor quality if at least one of the four Feature Extraction variables for the total input chromatin reference channel had the value 1 (bad). Spots on each of the Sco-Chip2-v2 arrays were flagged if at least one of the two channels was classed as an outlier by Feature Extraction.
All data were filtered such that only those probes were retained for analysis that had good quality data (not flagged) in each replicate array (to control dye bias) within each independent experiment: 20,586 probes for Sco-Chip2-v1 array; 43,056 probes for Sco-Chip2 (ChIP-on-chip); and 43,263 probes for the Sco-Chip2-v2 gene expression analysis.
Microarray expression data analysis
The filtered data sets for the gene expression experiments were analyzed using rank products analysis  via the RankProd package in R (version 2.5.0) . This method has been shown to be robust in the identification of true differentially expressed genes in data sets where there are few replicates and/or large variance [69, 70]. Differentially expressed genes were identified as having a pfp value  less than or equal to 0.15, equal to a false discovery rate of approximately 15%, a threshold value lower than that applied in the literature with this technique . The microarray-derived expression data have been deposited in ArrayExpress (accession numbers [E-MAXD-44], [E-MAXD-46] and [E-MAXD-49]).
ChIP-on-chip data analysis
Genes were identified as having upstream regions enriched/bound by HspR by the following steps. Step 1, probes that had a significant (corrected p-value < 0.05, non-parametric t-test  (using Bioconductor package )) difference between log2 'antibody IP'/total chromatin and log2 'no-antibody IP'/total chromatin were identified. Step 2, the average (across biological replicates) log2 antibody IP/total chromatin - log2 no antibody IP/total chromatin distribution of significant probes was plotted. Step 3, the right tail, the region of the distribution that departs from the typical Gaussian curve of the distribution, was identified and used as the threshold (Additional data file 1). Step 4, enriched probes were identified as having a ratio (average log2 antibody IP/total chromatin - log2 no antibody IP/total chromatin) > threshold (Figure 2a). Step 5, genes that had at least two probes enriched in a promoter proximal region were scored as likely targets of HspR (Additional data file 3).
Genes/regions considered to be directly controlled by HspR were first identified using steps 1-4 (see Additional data file 2 and Figure 3) as described above with the following exceptions: significant probes were determined by the difference between log2 'HspR antibody IP'/'no-antibody IP' and log2 'HspR pre-immune serum antibody IP'/'no-antibody IP' (step 1); average (across biological replicates) log2 'HspR antibody IP'/'no-antibody IP' distribution of significant probes was plotted (step 2) to determine the threshold (>1.5 on log2 scale) of selection (step 3). Then the novel approach, for ChIP-on-chip analysis, of clustering enriched probes was undertaken as follows such that identified probe clusters corresponded to the most likely targets of HspR. Steps 1-4 were as above. Step 5, enriched probes were represented by their corresponding position in the genome. A distance matrix was constructed based on the Euclidean distance between each pair (within the enriched probes set) of probe positions, when represented on a log scale. Step 6, the distance matrix was normalized such that each distance was between 0 and 1 and converted into a similarity matrix by subtracting each normalized distance from 1. Thus, probes with a similarity score of 0 are distant from each other in the genome and probes with a similarity score close to 1 are near each other in the genome. Step 7, the similarity matrix was given as input to the clustering algorithm CAST (Clustering Affinity Search Technique) , which intrinsically calculates the optimal partitioning of a data set (via addition and removal of members to a cluster such that the affinity of a cluster remains 'tight') given a certain threshold. The threshold of 0.5 was used to cluster probes; this threshold is equivalent to grouping enriched probes that occur within 2.9 kb of each other in the genome. Step 8, clusters of size greater than 1 were annotated with gene names (SCO numbers) and designated as genes/regions bound by HspR.
The ChIP-on-chip data have been deposited in ArrayExpress (accession number [E-MAXD-48]).
Nucleotide sequence motif analysis
Identification of putative HspR target sites with HspR consensus sequence
For each gene identified experimentally as having a putative HspR binding site, the respective upstream sequence, 300 nucleotides upstream of the translational start codon and 200 nucleotides downstream of the translational start codon, in the correct transcriptional orientation, was extracted from the annotated genome file of S. coelicolor. The entire set of upstream sequences was then aligned independently to the previously published HspR consensus sequence (5'-TTGAGYN(7)ACTCAA)  using ClustalW .
Derivation of a new HspR consensus sequence
The set of upstream sequences representing putative HspR binding sites was submitted to the MEME (Multiple Em for Motif Elicitation) server [74, 75] using default settings except for: minimum width of the motif to search for was set to 5 nucleotides and maximum width to 30 nucleotides (to reflect the common transcription factor recognition site length in prokaryotes); and maximum number of sites to find was set to 10, to restrict the amount of data obtained.
Quantitative real time PCR analysis of selected differentially expressed genes
Specific primers and probes for SCO4410, SCO5639, SCO3202 and SCO4157 were designed using Primer 3 software and used for qPCR as described previously . The sequences for the forward and reverse primers and dual labeled probes were: SCO4410 forward primer 5'-GTGTCGGGCGAACTGG, reverse primer 5'-CCGGGACGCGATGA, and dual labeled probe 5'-TCTGCGATTCCAGCGGGGTC; SCO5639 forward primer 5'-ACCATGAAGACGAGAGAGAGG, reverse primer 5'-GTGCACGAACACGTCT, and dual labeled probe 5'-ATGCCGGGCGACGTGCTAAA; SCO3202 forward primer 5'-CTGATCCAGGAGGGCAAC, reverse primer 5'-GCGTACGTGGAGAACTTGAA, and dual labeled probe 5'-TCCGCGCGGTCGAGAAGTTC; SCO4157 forward primer 5'-GACGTACAAGGCGATCCAG, reverse primer 5'-ATGATGTTGCCGTTCATGTC, and dual labeled probe 5'-CCCTCAACCCGGGCAACTCC.
Electrophoretic mobility shift assays
Gel-shift assays were conducted using the Light Shift Chemiluminescence EMSA kit (Pierce), following the manufacturer's instructions. The 3'-biotinylated oligonucleotides comprising the HspR binding site, IR3  in the promoter region of the dnaK operon, and similar motifs in the promoter regions of SCO5639 and SCO4410 were annealed to their complementary strands and 200 fmol were used in the binding reaction together with either purified DnaK-refolded HspR (as described in ) or a JM109 E. coli cell extract over-expressing hspR . In competition experiments a 200-fold excess of the same, but non-biotinylated, DNA was included in the electrophoretic mobility shift assay reaction. The dnaK biotinylated probe used in gel shift assays was 5'-TGCACACTTGAGCCTGTTCCACTCAAGTCAGCTGGAG; the SCO5639 biotinylated probe was 5'-TCGGATTGGAATTACTAAGATTCAGGATGCAGCACGCATCGT and the SCO4410 biotinylated probe was 5'-CGTTTCGGGTGAATCCCGAAAATTCCAGACGTTCCGACGAGG.
Additional data files
The following additional data are available with the online version of this paper. Additional data file 1 illustrates the distribution of significant probes from Sco-Chip2-v1 arrays. Additional data file 2 illustrates the distribution of significant probes from Sco-Chip2-v2 arrays. Additional data file 3 illustrates probe signals across significantly scoring HspR target regions using the Sco-Chip2-v1 array. Additional data file 4 tabulates genes significantly up-regulated following heat-shock at 42°C. Additional data file 5 lists the genes up-regulated in an hspR disruption mutant. Additional data file 6 illustrates the partial matches to the HspR-binding consensus sequence within the five tRNAGln/Glu cluster. Additional data file 7 details the motif identified from MEME analysis of the HspR targets identified with the Sco-Chip2-v2 array. Additional data file 8 tabulates amino acid composition of heat-shock-induced gene products relative to all annotated proteins. Additional data file 9 details qPCR expression data for selected heat induced genes. Additional data file 10 shows the qPCR expression data for SCO4410 and SCO5639. Additional data file 11 shows the comparison of heat shock expression data in SMMS and YEME cultures. Additional file 12 lists probes found to be significantly enriched for HspR on Sco-Chip2-v1 arrays. Additional file 13 lists probes found to be significantly enriched for HspR on Sco-Chip2-v2 arrays. Additional data file 14 shows an enlarged image of Figure 3d. Additional data file 15 shows an enlarged image of Figure 3e.
ink-jet in situ-synthesized
Maximum Em for Motif Elicitation
probability of false prediction
quantitative real time PCR.
We thank David Grainger and David Hodgson for helpful discussions and David Hodgson for contributions to method development. We thank the anonymous reviewers for constructive comments. This work was funded by the BBSRC (grants G18886 to CPS and BBD0115821 to A Kierzek, CPS, M Bushell and C Avignone-Rossa) and the European Commission (FP6 Programme, ActinoGEN IP005224 to CPS).
- Elliot MA, Buttner MJ, Nodwell JR: Multicellular development in Streptomyces. Myxobacteria: Multicellularity and Differentiation. Edited by: Whitworth DE. 2008, Washinton DC: ASM Press, 419-439.View ArticleGoogle Scholar
- Bibb MJ: Regulation of secondary metabolism in streptomycetes. Curr Opin Microbiol. 2005, 8: 208-215. 10.1016/j.mib.2005.02.016.PubMedView ArticleGoogle Scholar
- Challis GL, Hopwood DA: Synergy and contingency as driving forces for the evolution of multiple secondary metabolite production by Streptomyces species. Proc Natl Acad Sci USA. 100: 14555-14561. 10.1073/pnas.1934677100.
- Bentley SD, Chater KF, Cerdeño-Tárraga AM, Challis GL, Thomson NR, James KD, Harris DE, Quail MA, Kieser H, Harper D, Bateman A, Brown S, Chandra G, Chen CW, Collins M, Cronin A, Fraser A, Goble A, Hidalgo J, Hornsby T, Howarth S, Huang CH, Kieser T, Larke L, Murphy L, Oliver K, O'Neil S, Rabbinowitsch E, Rajandream MA, Rutherford K, et al: Complete genome sequence of the model actinomycete Streptomyces coelicolor A3(2). Nature. 2002, 417: 141-147. 10.1038/417141a.PubMedView ArticleGoogle Scholar
- Huang J, Lih CJ, Pan KH, Cohen SN: Global analysis of growth phase responsive gene expression and regulation of antibiotic biosynthetic pathways in Streptomyces coelicolor using DNA microarrays. Genes Dev. 2001, 15: 3183-3192. 10.1101/gad.943401.PubMedPubMed CentralView ArticleGoogle Scholar
- Bucca G, Brassington AM, Hotchkiss G, Mersinias V, Smith CP: Negative feedback regulation of dnaK, clpB and lon expression by the DnaK chaperone machine in Streptomyces coelicolor, identified by transcriptome and in vivo DnaK-depletion analysis. Mol Microbiol. 2003, 50: 153-166. 10.1046/j.1365-2958.2003.03696.x.PubMedView ArticleGoogle Scholar
- Hesketh A, Bucca G, Laing E, Flett F, Hotchkiss G, Smith CP, Chater KF: New pleiotropic effects of eliminating a rare tRNA from Streptomyces coelicolor, revealed by combined proteomic and transcriptomic analysis of liquid cultures. BMC Genomics. 2007, 8: 261-10.1186/1471-2164-8-261.PubMedPubMed CentralView ArticleGoogle Scholar
- Borodina I, Krabben P, Nielsen J: Genome-scale analysis of Streptomyces coelicolor A3(2) metabolism. Genome Res. 2005, 15: 820-829. 10.1101/gr.3364705.PubMedPubMed CentralView ArticleGoogle Scholar
- The Streptomyces coelicolor Microarray Resource. [http://www.surrey.ac.uk/SBMS/Fgenomics/Microarrays/]
- Wade JT, Struhl K, Busby SJ, Grainger DC: Genomic analysis of protein-DNA interactions in bacteria: insights into transcription and chromosome organization. Mol Microbiol. 2007, 65: 21-26. 10.1111/j.1365-2958.2007.05781.x.PubMedView ArticleGoogle Scholar
- Eichenberger P, Fujita M, Jensen ST, Conlon EM, Rudner DZ, Wang ST, Ferguson C, Haga K, Sato T, Liu JS, Losick R: The program of gene transcription for a single differentiating cell type during sporulation in Bacillus subtilis. PLoS Biol. 2004, 2: e328-10.1371/journal.pbio.0020328.PubMedPubMed CentralView ArticleGoogle Scholar
- Grainger DC, Hurd D, Harrison M, Holdstock J, Busby SJ: Studies of the distribution of Escherichia coli cAMP-receptor protein and RNA polymerase along the E. coli chromosome. Proc Natl Acad Sci USA. 2005, 102: 17693-17698. 10.1073/pnas.0506687102.PubMedPubMed CentralView ArticleGoogle Scholar
- Grainger DC, Hurd D, Goldberg MD, Busby SJ: Association of nucleoid proteins with coding and non-coding segments of the Escherichia coli genome. Nucleic Acids Res. 2006, 34: 4642-4652. 10.1093/nar/gkl542.PubMedPubMed CentralView ArticleGoogle Scholar
- Grainger DC, Aiba H, Hurd D, Browning DF, Busby SJ: Transcription factor distribution in Escherichia coli: studies with FNR protein. Nucleic Acids Res. 2007, 35: 269-278. 10.1093/nar/gkl1023.PubMedPubMed CentralView ArticleGoogle Scholar
- Lucchini S, Rowley G, Goldberg MD, Hurd D, Harrison M, Hinton JC: H-NS mediates the silencing of laterally acquired genes in bacteria. PLoS Pathog. 2006, 2: e81-10.1371/journal.ppat.0020081.PubMedPubMed CentralView ArticleGoogle Scholar
- Kültz D: Molecular and evolutionary basis of the cellular stress response. Annu Rev Physiol. 2005, 67: 225-257. 10.1146/annurev.physiol.67.040403.103635.PubMedView ArticleGoogle Scholar
- Bukau B, Weissman J, Horwich A: Molecular chaperones and protein quality control. Cell. 2006, 125: 443-451. 10.1016/j.cell.2006.04.014.PubMedView ArticleGoogle Scholar
- Genevaux P, Georgopoulos C, Kelley WL: The Hsp70 chaperone machines of Escherichia coli: a paradigm for the repartition of chaperone functions. Mol Microbiol. 2007, 66: 840-857. 10.1111/j.1365-2958.2007.05961.x.PubMedView ArticleGoogle Scholar
- Narberhaus F: Negative regulation of bacterial heat shock genes. Mol Microbiol. 1999, 31: 1-8. 10.1046/j.1365-2958.1999.01166.x.PubMedView ArticleGoogle Scholar
- Yura T, Nagai H, Mori H: Regulation of the heat-shock response in bacteria. Annu Rev Microbiol. 1993, 47: 321-350. 10.1146/annurev.mi.47.100193.001541.PubMedView ArticleGoogle Scholar
- Alba BM, Gross CA: Regulation of the Escherichia coli sigma-dependent envelope stress response. Mol Microbiol. 2004, 52: 613-619. 10.1111/j.1365-2958.2003.03982.x.PubMedView ArticleGoogle Scholar
- Servant P, Mazodier P: Negative regulation of the heat shock response in Streptomyces. Arch Microbiol. 2001, 176: 237-242. 10.1007/s002030100321.PubMedView ArticleGoogle Scholar
- Servant P, Rapoport G, Mazodier P: RheA repressor of hsp18 in Streptomyces albus G. Microbiology. 1999, 145: 2385-2391.PubMedView ArticleGoogle Scholar
- Servant P, Grandvalet C, Mazodier P: The RheA repressor in the thermosensor of the HSP18 heat shock response in Streptomyces albus. Proc Natl Acad Sci U S A. 2000, 97: 3538-3543. 10.1073/pnas.070426197.PubMedPubMed CentralView ArticleGoogle Scholar
- Grandvalet C, Servant P, Mazodier P: Disruption of hspR, the repressor gene of the dnaK operon in Streptomyces albus G. Mol Microbiol. 1997, 23: 77-84. 10.1046/j.1365-2958.1997.1811563.x.PubMedView ArticleGoogle Scholar
- Bucca G, Ferina G, Puglia AM, Smith CP: The dnaK operon of Streptomyces coelicolor encodes a novel heat-shock protein which binds to the promoter region of the operon. Mol Microbiol. 1995, 17: 663-674. 10.1111/j.1365-2958.1995.mmi_17040663.x.PubMedView ArticleGoogle Scholar
- Bucca G, Hindle Z, Smith CP: Regulation of the dnaK operon of Streptomyces coelicolor A3(2) is governed by HspR, an autoregulatory repressor protein. J Bacteriol. 1997, 179: 5999-6004.PubMedPubMed CentralGoogle Scholar
- Stewart GR, Wernisch L, Stabler R, Mangan JA, Hinds J, Laing KG, Young DB, Butcher PD: Dissection of the heat-shock response in Mycobacterium tuberculosis using mutants and microarrays. Microbiology. 2002, 148: 3129-3138.PubMedView ArticleGoogle Scholar
- Engels S, Schweitzer JE, Ludwig C, Bott M, Schaffer S: clpC and clpP1P2 gene expression in Corynebacterium glutamicum is controlled by a regulatory network involving the transcriptional regulators ClgR and HspR as well as the ECF sigma factor sigmaH. Mol Microbiol. 2004, 52: 285-302. 10.1111/j.1365-2958.2003.03979.x.PubMedView ArticleGoogle Scholar
- Spohn G, Scarlato V: The autoregulatory HspR repressor protein governs chaperone gene transcription in Helicobacter pylori. Mol Microbiol. 1999, 34: 663-674. 10.1046/j.1365-2958.1999.01625.x.PubMedView ArticleGoogle Scholar
- Spohn G, Danielli A, Roncarati D, Delany I, Rappouli R, Scarlato V: Dual control of Helicobacter pylori heat shock gene transcription by HspR and HrcA. J Bacteriol. 2004, 186: 2956-2965. 10.1128/JB.186.10.2956-2965.2004.PubMedPubMed CentralView ArticleGoogle Scholar
- Roncarati D, Danielli A, Spohn G, Delany I, Scarlato V: Transcriptional regulation of stress response and motility functions in Helicobacter pylori is mediated by HspR and HrcA. J Bacteriol. 2007, 189: 7234-7243. 10.1128/JB.00626-07.PubMedPubMed CentralView ArticleGoogle Scholar
- Schmid AK, Howell HA, Battista JR, Peterson SN, Lidstrom ME: HspR is a global negative regulator of heat shock gene expression in Deinococcus radiodurans. Mol Microbiol. 2005, 55: 1579-1590. 10.1111/j.1365-2958.2005.04494.x.PubMedView ArticleGoogle Scholar
- Ventura M, Kenny JG, Zhang Z, Fitzgerald GF, van Sinderen D: The clpB gene of Bifidobacterium breve UCC 2003: transcriptional analysis and first insights into stress induction. Microbiology. 2005, 151: 2861-2872. 10.1099/mic.0.28176-0.PubMedView ArticleGoogle Scholar
- Andersen MT, Brøndsted L, Pearson BM, Mulholland F, Parker M, Pin C, Wells JM, Ingmer H: Diverse roles for HspR in Campylobacter jejuni revealed by the proteome, transcriptome and phenotypic characterization of an hspR mutant. Microbiology. 2005, 151: 905-915. 10.1099/mic.0.27513-0.PubMedView ArticleGoogle Scholar
- Laing E, Mersinias V, Smith CP, Hubbard SJ: Analysis of gene expression in operons of Streptomyces coelicolor. Genome Biol. 2006, 7: R46-10.1186/gb-2006-7-6-r46.PubMedPubMed CentralView ArticleGoogle Scholar
- Breitling R, Armengaud P, Amtmann A, Herzyk P: Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Lett. 2004, 573: 83-92. 10.1016/j.febslet.2004.07.055.PubMedView ArticleGoogle Scholar
- Van Keulen G, Hopwood DA, Dijkhuizen L, Sawers RG: Gas vesicles in actinomycetes: old buoys in novel habitats?. Trends Microbiol. 2005, 13: 350-354. 10.1016/j.tim.2005.06.006.PubMedView ArticleGoogle Scholar
- Hong HJ, Paget MS, Buttner MJ: A signal transduction system in Streptomyces coelicolor that activates the expression of a putative cell wall glycan operon in response to vancomycin and other cell wall-specific antibiotics. Mol Microbiol. 2002, 44: 1199-1211. 10.1046/j.1365-2958.2002.02960.x.PubMedView ArticleGoogle Scholar
- Finn RD, Mistry J, Schuser-Böckler B, Griffiths-Jones S, Hollich V, Lassmann T, Moxon S, Marshall M, Khanna A, Durbin R, Eddy SR, Sonnhammer ELL, Bateman A: Pfam: clans, web tools and services. Nucleic Acids Res. 2006, 34: D247-D251. 10.1093/nar/gkj149.PubMedPubMed CentralView ArticleGoogle Scholar
- SIB Blast Network Service. [http://www.expasy.org/tools/blast/]
- Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997, 25: 3389-3402. 10.1093/nar/25.17.3389.PubMedPubMed CentralView ArticleGoogle Scholar
- Fox DS, Cox GM, Heitman J: Phospholipid-binding protein Cts1 controls septation and functions coordinately with calcineurin in Cryptococcus neoformans. Eukaryot Cell. 2003, 2: 1025-1035. 10.1128/EC.2.5.1025-1035.2003.PubMedPubMed CentralView ArticleGoogle Scholar
- Regulatory Sequence Analysis Tools. [http://rsat.ulb.ac.be/rsat/]
- van Helden J: Regulatory sequence analysis tools. Nucleic Acids Res. 2003, 31: 3593-3596. 10.1093/nar/gkg567.PubMedPubMed CentralView ArticleGoogle Scholar
- Hartl FU, Hayer-Hartl M: Molecular chaperones in the cytosol: from nascent chain to folded protein. Science. 2002, 295: 1852-1858. 10.1126/science.1068408.PubMedView ArticleGoogle Scholar
- de León P, Mellado R: Ribosomal RNA synthesis in Streptomyces lividans under heat shock conditions. Gene. 1997, 194: 125-132. 10.1016/S0378-1119(97)00183-2.PubMedView ArticleGoogle Scholar
- Newlands JT, Gaal T, Mecsas J, Gourse RL: Transcription of the Escherichia coli rrnB P1 promoter by the Heat shock RNA Polymerase (Eσ 32) in vitro. J Bacteriol. 1993, 175: 661-668.PubMedPubMed CentralGoogle Scholar
- Kim HL, Shin EK, Kim HM, Ryou SM, Kim S, Cha CJ, Bae J, Lee K: Heterogeneous rRNAs are differentially expressed during the morphological development of Streptomyces coelicolor. FEMS Microbiol Lett. 2007, 275: 146-152. 10.1111/j.1574-6968.2007.00872.x.PubMedView ArticleGoogle Scholar
- Blanco G, Rodicio MR, Puglia AM, Méndez C, Thompson CJ, Salas JA: Synthesis of ribosomal proteins during growth of Streptomyces coelicolor. Mol Microbiol. 1994, 12: 375-385. 10.1111/j.1365-2958.1994.tb01027.x.PubMedView ArticleGoogle Scholar
- Hahn MY, Roe J-H: Partial purification of factors for differential transcription of the rrnD promoters for ribosomal RNA synthesis in Streptomyces coelicolor. J Microbiol. 2007, 45: 534-540.PubMedGoogle Scholar
- Feng L, Sheppard K, Namgoong S, Ambrogelly A, Polycarpo C, Randau L, Tumbula-Hansen D, Söll D: Aminoacyl-tRNA synthesis by pre-translational amino acid modification. RNA Biol. 2004, 1: 16-20.PubMedView ArticleGoogle Scholar
- Cashel M, Gentry V, Hernandez VJ, Vinella D: The stringent response. Escherichia coli and Salmonella Cellular and Molecular Biology. Edited by: Neidhardt FC, Curtiss R. 1996, Washington DC: ASM Press, 1: 1458-1496. 2Google Scholar
- Levicán G, Katz A, Valenzuela P, Söll D, Orellana O: A tRNAGlu that uncouples protein and tetrapyrrole biosynthesis. FEBS Lett. 2005, 579: 6383-6387. 10.1016/j.febslet.2005.09.100.PubMedView ArticleGoogle Scholar
- Jahn D, Verkamp E, Söll D: Glutamyl-transfer RNA; a precursor of heme and chlorophyll biosynthesis. Trends Biochem Sci. 1992, 17: 215-218. 10.1016/0968-0004(92)90380-R.PubMedView ArticleGoogle Scholar
- Farias ST, Bonato MC: Preferred amino acids and thermostability. Genet Mol Res. 2003, 2: 383-393.PubMedGoogle Scholar
- Hindle Z, Smith CP: Substrate induction and catabolite repression of the Streptomyces coelicolor glycerol operon are mediated through the GylR protein. Mol Microbiol. 1994, 12: 737-745. 10.1111/j.1365-2958.1994.tb01061.x.PubMedView ArticleGoogle Scholar
- Kieser T, Bibb MJ, Buttner MJ, Chater KF, Hopwood DA: Practical Streptomyces Genetics. 2000, Norwich: John Innes CentreGoogle Scholar
- Grainger DC, Overton TW, Reppas N, Wade JT, Tamai E, Hobman JL, Constantinidou C, Struhl K, Church G, Busby SJ: Genomic studies with Escherichia coli MelR protein: applications of chromatin immunoprecipitation and microarrays. J Bacteriol. 2004, 186: 6938-6943. 10.1128/JB.186.20.6938-6943.2004.PubMedPubMed CentralView ArticleGoogle Scholar
- McKenzie NL, Nodwell JR: Phosphorylated AbsA2 negatively regulates antibiotic production in Streptomyces coelicolor through interactions with pathway-specific regulatory gene promoters. J Bacteriol. 2007, 189: 5284-5292. 10.1128/JB.00305-07.PubMedPubMed CentralView ArticleGoogle Scholar
- Bucca G, Brassington AM, Schönfeld HJ, Smith CP: The HspR regulon of Streptomyces coelicolor: a role for the DnaK chaperone as a transcriptional corepressor. Mol Microbiol. 2000, 38: 1093-1103. 10.1046/j.1365-2958.2000.02194.x.PubMedView ArticleGoogle Scholar
- University of Surrey Streptomyces Microarray Hybridisation Protocol. [http://www.surrey.ac.uk/SBMS/Fgenomics/Microarrays/docs/Strep_hyb_protocol_1005.pdf]
- Buck MJ, Lieb JD: ChIP-chip: considerations for the design, analysis, and application of genome-wide chromatin immunoprecipitation experiments. Genomics. 2004, 83: 349-360. 10.1016/j.ygeno.2003.11.004.PubMedView ArticleGoogle Scholar
- Smyth GK, Speed TP: Normalization of cDNA microarray data. Methods. 2003, 31: 265-273. 10.1016/S1046-2023(03)00155-5.PubMedView ArticleGoogle Scholar
- Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JY, Zhang J: Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004, 5: R80-10.1186/gb-2004-5-10-r80.PubMedPubMed CentralView ArticleGoogle Scholar
- R. [http://www.R-project.org]
- R Development Core Team: R: a Language and Environment for Statistical Computing. 2005, Austria: R Foundation for Statistical Computing: ViennaGoogle Scholar
- Hong F, Breitling R, McEntee CW, Wittner BS, Nemhauser JL, Chory J: RankProd: a Bioconductor package for detecting differentially expressed genes in meta-analysis. Bioinformatics. 2006, 22: 2825-2827. 10.1093/bioinformatics/btl476.PubMedView ArticleGoogle Scholar
- Jeffery IB, Desmond GH, Culhane AC: Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data. BMC Bioinformatics. 2006, 7: 359-10.1186/1471-2105-7-359.PubMedPubMed CentralView ArticleGoogle Scholar
- Hong F, Breitling R: A comparison of meta-analysis methods for detecting differentially expressed genes in microarray experiments. Bioinformatics. 2008, 24: 374-382. 10.1093/bioinformatics/btm620.PubMedView ArticleGoogle Scholar
- Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc. 1999, 57: 289-300.Google Scholar
- Ben-Dor A, Shamir R, Yakhini Z: Clustering gene expression patterns. J Comput Biol. 1999, 6: 281-297. 10.1089/106652799318274.PubMedView ArticleGoogle Scholar
- Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H, Valentin F, Wallace IM, Wilm A, Lopez R, Thompson JD, Gibson TJ, Higgins DG: Clustal W and Clustal X version 2.0. Bioinformatics. 2007, 23: 2947-2948. 10.1093/bioinformatics/btm404.PubMedView ArticleGoogle Scholar
- MEME. [http://meme.sdsc.edu/meme]
- Bailey TL, Elkan C: Fitting a mixture model by expectation maximization to discover motifs in biopolymers. Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology: August 14-17, 1994; Stanford, California. Edited by: Altman R, Brutlag D, Karp P, Lathrop R, Searls D. 1994, Menlo Park, CA: AAAI Press, 28-36.Google Scholar
- WebLogo. [http://weblogo.berkeley.edu/]
- Crooks GE, Hon G, Chandonia JM, Brenner SE: WebLogo: a sequence logo generator. Genome Res. 2004, 14: 1188-1190. 10.1101/gr.849004.PubMedPubMed CentralView ArticleGoogle 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.