Escherichia coli genome-wide promoter analysis: Identification of additional AtoC binding target elements

Ab initio motif detection and evaluation procedure

In order to predict putative AtoC binding sites, we implemented an ab initio motif detection procedure (Figure 1). This procedure involved an iterative pair-wise motif sampling process between the promoter of the atoDAEB operon, which was used as the initial qualified sequence, and a pool of E. coli promoters (initial promoter set). The promoter that showed the best homology to the atoDAEB promoter sequences was removed from the promoter pool and, together with the initial qualified promoter sequence, formed the qualified promoter set that was used for the next iteration of the motif-sampling process, against an input promoter set. In each iteration, the input promoter set is depleted by the selected promoter, plus those promoter sequences that scored below a predefined threshold (see Materials and Methods). The goal of this procedure was to yield motifs with maximized log-likelihood (ll) scores at the specified positions, thereby revealing conserved intergenic sites corresponding to putative AtoC binding sites. The ll-score represents an informative index of both the level of motif conservation (information content) and the number of its occurrences on a set of sequences. Promoters containing sequences, which yield motifs with high ll-scores, are pooled to form the final qualified set of promoters, which is used for the derivation of a best-scoring consensus motif.

Due to the fact that the MotifSampler program [17], which was used in this analysis, implements a heuristic algorithm, the sampling results are very sensitive to the input parameters. In particular, different motifs are obtained at the end of the procedure by slightly varying the parameters, e.g. the motif length, the motif maximizing positions or the initial promoter set (see Material and Methods). All these motifs represent different instances of sequence conservation between promoters. However, the sequence conservation among promoter regions is relatively small and this implies that other factors, e.g. the chromatin context or the formation of other transcription factor complexes, also contribute to the effective AtoC binding. Thus, among different sets of conserved sequences only a few would be biologically relevant. To cope with this issue and capture the highest possible number of biologically relevant motifs, the computational pipeline was applied many times with slight variations regarding the input parameters. Subsequently the biological relevance of the obtained motifs was assessed by implementing a Gene Ontology Term statistical analysis (see Materials and Methods).

Genome-wide motif searching using the motifs obtained by the iterative procedure coupled to the Hypergeometric Test for Gene Ontology Term overrepresentation correlate motifs with gene functions

Evaluation of lower-scoring motifs and retention of Motif 4

In addition to the highest-scoring motifs, we also investigated motifs with intermediary correspondence to the AtoC-binding site. These motifs were initially discarded from the automatic procedure due to the stringent parameterization to retain only the highest-scoring motifs for the specified position. In order to overcome this limitation and test the discovery strengths of the proposed analytic pipeline in this work, we applied this to motifs that score lower during the Motif Sampling phase (assuming that they reveal weak matching with the atoDAEB promoter binding site) and examined them separately regarding their evaluation in the GOT analysis. In general, the hits of intermediary motifs either overlapped those of the highest-scoring motifs or did not provide any overrepresented GOT and so they were discarded (data not shown). Motif 4, however, is an exception. This intermediary motif was retained (Table 2) because it matched an unexpectedly highly conserved repeat. This particular repeat corresponds to an uncommonly conserved intergenic site present in many intergenic regions and yielded a significant GOT overrepresentation, when submitted individually to the motif evaluation sub-procedure.

The enrichment of motif 4 is particularly high within its hits (at 0.7 prior probability motif 4 comprises 346 hits on promoter regions; data provided in Additional File 2) due to the very high sequence conservation, and despite its restrictive character, which yields a lower log-likelihood score (66.3). Therefore, motif 4 was strongly correlated to the GOT Transporter Activity function (Table 4).

Motif 4 resides within a ~40-bp highly conserved repeat found in many E. coli promoters

Motif 4 weakly matched the palindromic AtoC binding site in the promoter of the atoDAEB operon [5] and at the same time it strongly matches an unexpectedly conserved ~40-bp repeat present in a significant number of promoters (motif 4 searching results, Additional File 2). An additional motif, motif 5, was constructed in order to represent this conserved region. The sequence of motif 5 at 0.7 prior probability is shown in Figure 2A. Motif 5 was used for genome-wide screening and matched 85 intergenic sites, whereas it did not match any site within ORFs (data provided in Additional File 2). The high degree of conservation of this 40 bp repeat and its wide representation in the genome (Figure 2B) suggests a more pivotal role in global E. coli regulatory mechanisms. The alignment of the first twenty hits shows unexpected sequence conservation, for an intergenic region (Figure 3) where, for example, there are 6 variants of the repeat with at least 38/40 identity in various intergenic positions. The probability of finding a 38-bp DNA sequence repeated by chance two times is 1/0.25³⁸ × 4 in the E. coli genome comprising 4.6 × 10⁶ nucleotides. This result is even more pronounced if we consider the absolute establishment of the repeat only within intergenic regions and not within ORFs.

Interestingly, this highly conserved sequence was also detected in many intergenic regions of other Enterobacteria such as Shigella flexneri, Salmonella enterica, Klebsiella pneumonia and Citrobacter coseri (data not shown).

Application of the computational procedure on the transcription factor LexA as a demonstration of its general effectiveness

In order to demonstrate that the computational procedure used here for the prediction of AtoC binding elements can be applied to other transcription factors, we analyzed its effectiveness using LexA, a well-characterized transcription factor. LexA, which regulates the transcription of several genes involved in the cellular response (SOS response) to DNA damage or inhibition of DNA [20], has been extensively studied and the multitude of experimentally identified DNA binding sites that predicted its consensus [21] were also used as a benchmark to assess the predictive capacity of our computational framework. By initiating the computational analysis with one LexA binding sequence and querying the whole promoter set of Escherichia coli K12 (motif length 20, same parameters as for AtoC), we identified a number of putative DNA binding sites, including thirteen known LexA binding sites. Moreover, the highest-scoring GOTs generated from this procedure were GO:0006974, "response to DNA damage stimulus" and GO:0009432, "SOS response", a finding that is in total agreement with the established biological function of LexA (the findings are presented in Additional File 3).

Experimental validation of the in silico predicted sequences by in vivo binding of recombinant AtoC

ChIP experiments are routinely used to define whether a DNA-binding protein recognizes a particular gene regulatory region in vivo. Regarding the system under study, the specificity of the results obtained with this method has already been demonstrated by the lack of AtoC binding to its defined site in the regulatory region of the atoDAEB operon in the E. coli strain BW28878, an atoSC deletion mutant [6]. In contrast, in the isogenic atoSC ⁺ strain BW25113 AtoC was found to bind this site in vivo and this binding was more pronounced in the presence of inducing acetoacetate [5]. Having confirmed that AtoC binds in vivo to its a priori target, ChIP experiments were used to define whether AtoC binds to any of the putative targets that emerged from the bioinformatics approach. Initially, we analyzed potential AtoC binding to four such sequences, three located within the dmsA, acr and fliA promoter regions, and one located within the fliT gene-encoding region. ChIP experiments indicated that AtoC binds to all four, as well as in the control atoDAEB promoter (data not shown). The specificity of the experimental approach used was confirmed by the lack of any visible signal, when the precipitations were performed either in the absence of AtoC-specific antibody or in the atoSC¯ strain BW28878.

To further increase the probability that AtoC would bind in vivo to even relatively low affinity targets predicted by the bioinformatic analysis, the experiments were performed in E. coli BL21[DE3] transformed with plasmid pHis₁₀-AtoC [22] and overexpressing a recombinant His-tagged form of AtoC, recognised by an anti-His probe. Initially, we analyzed potential AtoC binding to the hits of the qualified promoter sets and subsequently to additional hits of the motif derived from each execution. Ten of the qualified promoters of Table 1 were tested and found positive for in vivo AtoC binding, together with the already verified atoD: narZ [23], puuP [24], dmsA [25], crr [26], rtcB-rtcR [27], borD [28] and acrD [29]. ChIP analysis was also extended to accommodate targets derived from the Motif-Searching procedure while their cognate gene products suggested biological relevance (presented in Table 5). Sequences within six intergenic regions, i.e. metR-metE [30], trmA-btuB [31], ykgA-ykgQ [32], aegA-narQ [33, 34], ymiA [35], narZ [23], and cpxR-cpxP [36], which were observed as hits by motif 1 searching (Table 2) were selected for testing based both on their scores and their putative functional relevance to the ato genes. The results of the ChIP analysis for all intergenic regions ascribed to motif 1 are included in Figure 4, which clearly demonstrates the in vivo binding of recombinant AtoC. Six additional promoters, i.e. borD [28], putA-putP [37], acrD [29], adhP [38], rhaT-sodA [39, 40] and nirB [41], that are likely transcribed by sigma-54 polymerase and which matched motif 2, were similarly tested for in vivo AtoC binding. Finally, both the borD and acrD gene promoters, to which AtoC was found to bind in vivo, share patterns with motif 3. Regarding the top hits of motif 4 (Additional File 2), sseB and yjcH were also among the positive targets. This result is potentially significant since yjcH is cotranscribed with acs and actP, which encode proteins involved in acetate activation and transport, as part of the acs-yjcHG operon [42–44]. All predicted targets matching motifs 2, 3 and 4 that gave a positive signal in genomic DNA (Input Chromatins, see paragraph regarding specificity of AtoC binding) were also analysed by ChIP and the results are presented in Figure 5A. As becomes evident in this figure, not all targets are recognised by AtoC with the same intensity, even though the input chromatin signals share, as stated above, the same strong level of intensity. This allows a certain prioritization of the putative targets according to the strength of the AtoC binding.

Result	Site	Gene	Motif	Strand	Site distance from TSS (Refseq)		Gene product
+	GCTATGCAGAAATTTGCACA	atoD	1,2,3,4	(+)	-184	-165	acetyl-CoA: acetoacetyl-CoA transferase, subunit
+	GCTATGCAGAAAATTGCGCA	atoD	1,2,4	(-)	-163	-144	acetyl-CoA: acetoacetyl-CoA transferase, subunit
+	GCTATAGAAATAATTACACA	dmsA	1,2	(-)	-69	-50	dimethyl sulfoxide reductase, anaerobic, subunit A [25]
+	GCCATGCGGGGATTTAATCA	puuP	1	(+)	-148	-129	putrescine importer [24]
+	GCCGTCCAGATGTTTACACA*	metR-metE	1	(-)	-87, -170	-68, -151	transcriptional activator [30]
+	GCGATTGTAGGGATTGCTCA*	trmA-btuB	1,(2)	(+)	-49, -340	-30, -321	tRNA (uracil-5-)-methyltransferase/ outer membrane transporter [31]
+	ACTATACGGAAAATTCCACT*	ykgA-ykgQ	1,2	(-)	-235, -55	-216, -36	Putative transcriptional activator [32]
+	GTGATGGGATTATTTGATCT*	aegA-narQ	1	(-)	-147, -79	-128, -60	predicted fused oxidoreductase: FeS binding subunit and sensory HK in TCS with NarP [33, 34]
+	GCCTCGGAGGTATTTAAACA*	cpxR-cpxP	1	(-)	-21, -149	-2, -130	response regulator in TCS with CpxA/ periplasmic protein; combats stress [36]
+	GCGAGGCGGGTAATTAGACA	ymiA	1	(-)	-199	-180	small predicted membrane protein [35]
+	GGCCCAATTTACTGCTTAGG	crr	1,2	(+)	-28	-9	glucose-specific PTS component [26]
+	GCGGTGCAGGAGATTGCACA	fliT	1	(+)	127	146	predicted chaperone (flagellum) [46]
+	CCGGTCCAGGAATTTACTCA	narG	1	(-)	2	21	nitrate reductase 1, alpha subunit [23]
+	GCGAAACGATTAATTACACA	ycjM	1	(+)	94	113	glucosyltransferase predicted [64]
+	ACTGTACAGAAAGTTGCTCA	yeaM	1	(-)	668	687	transcription regulator (predicted) [65]
+	GCGGGTCAGGTAATTGCACA	gadA	1	(-)	959	978	glutamate decarboxylase A, PLP-dependent [66]
+	GACATGCAGTTGATTACACA	eutR	1	(+)	783	802	predicted transcription regulator [67]
+	GCGATCCAAAAGTTTACTCA	narZ	1	(+)	0	+ 19	nitrate reductase 2 alpha subunit [23]
+	CGGAGATTTCCCGCAAAGCC*	rtcB-rtcR	2	(+)	-145, -64	-126, -45	sigma-54-dependent transcriptional regulator of rtcBA [27]
+	CGGCAAGTTTCGACATTGCC*	putA-putP	2	(+)	-398, -45	-379, -26	fused transcriptional regulator/proline dehydrogenase/ carboxproline:sodium symporter [37]
-	GGGCATTTTCCTGCAAAACC*	rhaT-sodA	3	(+)	-167, -138	-148, -119	L-rhamnose:proton symporter/superoxide dismutase [39, 40]
-	GGATGATGTTCTGCATAGCA	adhP	3	(+)	-35	-16	ethanol-active dehydrogenase/acetaldehyde-active reductase [38]
-	TTTATAGAAATAGATGCACG	nirB	4	(-)	-205	-186	nitrite reductase, large subunit, NAD(P)H-binding [41]
+	TATGTGTAGAAAATTAAACA	borD	4,3	(+)	-59	-40	DLP12 prophage; predicted lipoprotein [28]
+	GTTCAGTATAAAAGGGCATG	acrD	2	(+)	-128	-109	aminoglycoside/multidrug efflux system [29]
-	GAAATAGAAATAGTTGAAAG	ykgE	4	(-)	-472	-453	predicted oxidoreductase [32]
+	CCGGATAAGGCGTTTACGCC	yjcH	4	(+),(-)	-114, -122	-95, -103	conserved inner membrane protein/ acetate transport [44]
-	CCGGATAAGACGTTTACGCC	yafJ	4	(+)	-193	-174	Unknown function, similarity to type II amidotransfease
+	CCGGATAAGGCGTTTACGCC	sseB	4	(+)	-125	-106	rhodanese-like enzyme [68]

Motif hits experimentally tested by ChIP analysis for in vivo AtoC binding. All annotations were provided by the Refseq database (Escherichia coli K12, Refseq id: NC_000913). Underlined: hits in gene-encoding regions. *These hits represent a unique site per pair, which could be attributed as cis-regulatory element to both flanking genes. As predicted by the GO Term analysis many of the targets are transcription factors, transporters and enzymes involved in oxidoreduction.

In vivo binding of AtoC to gene encoding regions

An interesting aspect of this bioinformatic analysis is the detection of several potential AtoC binding sites, corresponding to the pattern of motif 1, within gene-encoding regions. The hits of motif 1 screening within ORFs are shown in Additional File 2. Seven of them could have functional relevance to the ato system based on their GOT analysis. ChIP experiments were performed to identify whether AtoC binds to them in vivo and the results of this analysis were positive for six out of the seven gene-encoding regions tested, i.e. fliT, narG, ycjM, yeaM, gadA and eutR (Figure 6). Thus, it is most likely that AtoC also binds within the gene-encoding regions predicted by the bioinformatic analysis, although the functional relevance of this binding remains unclear.

Specificity of AtoC binding to its predicted targets: the effect of the acetoacetate inducer in vivo and in vitro DNA-binding competition experiments

In order to assess the specificity of AtoC binding to the predicted operators, we re-examined all ChIP analyses by reducing the PCR cycles to twenty five, while ChIP signals for both acetoacetate-induced and non-induced cultures were analysed comparatively for each target. AtoC was found to demonstrate increased affinity for most of the predicted "atypical" sites only in the case where acetoacetate, the inducer, was added to the cultures, while the abundant, over-expressed AtoC appears able to bind the atoDAEB promoter with the same affinity, regardless of the presence of acetoacetate (Figures 4, 5 and 6). In an attempt to ensure consistency of the results through the use of extra controls, chromatin input samples for all targets groups were also tested for PCR positive signals in the absence of the His-probe antibody precipitation. DNA signals of the chromatin input for both induced and non-induced cultures, exhibited the same signal intensity (Figure 5B), which proves that the signal intensity corresponding to each target can be solely attributed to the AtoC binding, whereas the absence of signal can be considered a sign of absence or weak binding, rather than a mere coincidence caused by primer malfunction. Moreover, low ChIP signals for the non-induced cultures provide negative controls, in the presence of both abundant AtoC and the antibody, directly relating the presence of abundant positive signals to acetoacetate induced AtoC binding of the predicted targets, thus supporting our case.

To further address whether AtoC indeed binds to the predicted targets, the aforementioned representative of each of the three groups was also tested, together with atoD, in gel retardation assays combining His₁₀-AtoC with the PCR products that contain the predicted promoters or gene encoding regions. The specificity of the DNA-AtoC interactions has already been verified by competition experiments to evaluate the association of AtoC with biotinylated atoD probe in the presence of increasing concentrations of unlabeled specific (atoD) or nonspecific DNA competitors [4, 5]. As illustrated in Figure 7A, electrophoretic gel retardation assays showed that all DNA targets exhibit binding to AtoC, following incubation on ice for 25 min. However, this binding appears to be less pronounced when compared with the cognate, high-affinity AtoC binding site present in the atoD promoter. The specificity of the results was further verified by also performing DNA binding reactions on ice for 30 min in the presence and absence of DNA competitors as previously described [4, 5]. Under these conditions, His₁₀-AtoC binding to the biotin-labelled atoD probe was abolished either by the addition of excess amounts of unlabelled cognate (atoD) competitor or by unlabelled DNA competitors containing the novel, atypical binding sites, i.e. metR-metE, acrD, narG (Figure 7B). The degree of specificity of the AtoC binding to each of the predicted targets remains to be determined.

Computational Tools

All procedures were implemented on a Linux workstation using a combination of Perl scripts interacting with the specified INCLUSIVE programs for motif analysis (MotifSampler, MotifSearch, MotifRanking) [17]. All sequences, annotations and intermediary results were stored and queried in a Mysql database using the Perl-Mysql interface DBI library.

Selection of promoters

The promoters are defined here as the sequences from -250 to +30 relative to each transcription start site (TSS). The annotations of the TSS and the genomic sequence of Escherichia coli K-12 were extracted from the Refseq database [56] (refseq id:NC_000913) using Perl and Bioperl scripts. No overlap between two promoters was allowed and all intergenic sequences shorter than 500 base pairs and between two genes encoded respectively in -1 and +1 strands were entirely included and considered as promoters of both flanking genes in the GO Term analysis step.

Definition of promoter sets

The initial promoter set is defined as the promoter pool that is subjected to motif sampling. The different initial promoter sets used in the three executions of this analysis are presented in Table 1.

The promoters submitted to each reiteration of motif sampling represent the input promoter set. During the first iteration the input promoter set, which equals the initial promoter set, is subjected to motif sampling against atoDAEB promoter sequences representing the initial qualified promoter set. The number of promoters within the input promoter set decreases after each reiteration, since both a qualified promoter, as well as promoters scoring below a threshold value (see below), are eliminated from the pool. The qualified promoter set, ever increasing by one, is then used for the next iteration of the motif sampling process and the procedure advances until no more qualified promoters can be found.

Iterative procedure for de novo motif detection

The de novo motif detection was performed using a Gibbs sampling algorithm (implemented in INCLUSIVE MotifSampler program [17]) in a multi-step procedure. The first step of the procedure was a pair-wise motif sampling between the atoDAEB operon promoter and each of the other E. coli promoters of the initial promoter set (3789 in total in the case of the full set of E. coli promoters), using the following parameters: A hundred runs (detection of one motif per run permitted); motif length 20; both strands; background model of order 3 (downloaded from the INCLUSIVE [17] web site for E. coli K-12), with prior probability value 0.5. The program retained the highest scoring motif.

In each iteration:

– A log likelihood (ll) score is estimated for every motif derived from the pair-wise motif sampling between each remaining promoter in the input promoter set and the qualified promoter set derived from the previous sampling iterations.

– The promoter of the input promoter set yielding a motif with the maximum ll-score within the specified position of the atoDAEB promoter is retained and added to the qualified promoter dataset. Thus, the qualified promoter set is derived through iterative agglomeration.

– The motif sampling performs independent rounds (same parameters as above) and datasets composed of n+1 sequences, where "n" the number of sequences in the dataset of the previous step (started by 2).

– When the ll score of a particular motif derived is lower than the 30th percentile of all scores, then this promoter is immediately eliminated from the input promoter set.

– Additionally when the position-specific maximum ll-score of a motif within a promoter is either equal or lower than the score derived in the previous step, then this promoter is also removed from the input promoter set.

In order to reduce the number of motif sampling rounds, the elimination of promoters that are less probable to maximize the ll-scores of the detected motifs is predicated, otherwise the screening procedure becomes time-exhaustive and possibly non converging to a final qualified promoter set, with an increasing number of sequences embedded in the qualified promoter dataset.

The procedure was successfully performed three times using all the same parameters except the initial promoter set. The first execution comprised the full set of all E. coli promoters. For the other two executions the initial promoter set was either the set of sigma-54-dependent promoters retrieved from the RegulonDB database [18] (argT, astC, chaC, dcuD, ddpX, fdhF, glmY, glnA, glnH, glnK, gltI, hycA, hydA, hyfA, hypA, ibpB, kch, nac, norV, potF, prpB, pspA, pspG, puuP, rpoH, rtcB, rtcR, rutA, yaiS, ybhK, yeaG, yfhK, ygjG, yhdW, zraP, zraS) or the set of promoters that comprise binding sites for other response regulators of two-component systems and are annotated in the E. coli K-12 database EcoCyc [19] as targets of response regulators (dmsA, emrK, gadE, yca, spy, cydA, sucA, cpx, rdoA, ppiA, yqjA, acrD, ompC, mdtA, ftnB, yebE, ydeH, ycfS, efeU, yccA, aroG, ppiD, degP, ybaJ, cusR, dctA, frdA, dcuB, astC, kdpF, nrfA, nirB, hcp, napF, tppB, ompF, csgD, psiE, pstS, asr, phoH, phoB, phoA, phoE, mgtA, yrbL, mgrB, rstA, nagA, borD, hemL, ftsA, osmC, tnaA, torC, uhpT, zraS). For all three executions, the positions from -184 to -165 upstream the atoDAEB operon were used as input parameter.

Motif Ranking

The motifs generated by Motif Sampling were ranked and separated by Kullback-Leiber distance (INCLUSIVE MotifRanking program [17]). The threshold was set to 0.9 which means that if the Kullback-Leiber distance between two motifs is smaller than this threshold these motifs are considered as similar.

Motif Evaluation and correlation with biological function

The evaluation of the motifs was performed by coupling Motif Searching with Gene Ontology Term Analysis. The motif searching hits were attributed to genes and these genes were submitted to the Hypergeometric Test for GO Test over-representation, described below.

Construction of motif 5

The promoters which contained hits of motif 4 were submitted to MotifSampler with the same parameters as the initial motif samplings, except from the motif length, which was set to 40. For the visualization of the genomic positions of the hits of motif 5 we used the CGview Server [61].

Bacterial strains and growth conditions

Cells were grown to their mid-exponential phase at 37°C in 10 ml of modified M9 mineral medium supplemented with 0.1 mM CaCl₂, 1 mM MgSO₄, 0.4% (w/v) glucose, 1 μg of thiamine/ml and 80 μg of proline/ml [62], in the absence or presence of 10 mM acetoacetate. E. coli BL21[DE3] cells carrying pHis₁₀-AtoC plasmid [22] were grown at 37°C in minimal medium containing 100 μg/ml ampicillin. Induction of recombinant AtoC expression was achieved by addition of IPTG (0.25 mM) to the cultures when the OD₆₀₀ reached 0.25 and it was allowed to take place for 4 hours. Acetoacetate, the known inducer of the AtoSC TCS, was also added during the induction phase of the culture at a concentration of 10 mM.

ChIP assays

ChIP assays were performed using chromatin from E. coli BL21 [DE3] over-expressing His-tagged AtoC, grown in the absence or presence of acetoacetate. The assays were performed using Magna ChIP™ A Millipore kit following the manufacturer's instructions. Briefly, in vivo cross-linking of the nucleoproteins took place by the addition of formaldehyde (final concentration 1%), directly to the E. coli (10 ml cultures) grown in modified M9 mineral medium, when the OD₆₀₀ of the culture reached 0.6. The cross-linking reactions were quenched 20 min later by the addition of glycine and the cells were harvested by centrifugation and washed twice with ice-cold PBS, supplemented with a protease inhibitor cocktail. Cell lyses were performed as described in the kit manual and the cellular DNA was sheared by sonication to an average size of 500 to 1,000 bps. Cell debris was removed by centrifugation and the supernatant was used as the chromatin input for the immunoprecipitation reactions, after an initial stage of pre-incubation with protein A magnetic beads, in the absence of antibodies, to remove the nonspecific binding proteins. The ChIP reactions were then performed by adding the His-probe™ Santa Cruz rabbit polyclonal) and fresh protein A magnetic beads, to the resulting supernatant and allowing the reactions to take place overnight, with rotation at 4°C. DNA was then obtained from the immunoprecipitated protein/DNA complexes and the presence of the target promoter sequences in the chromatin immunoprecipitates, was detected by PCR amplification. Parallel mock precipitations performed in the absence of added antibody, were used as negative controls for each reaction.

Primer design and PCR reactions

The primers used to monitor the chromatin immunoprecipitations (Additional file 4) were designed, using the Oligo Explorer software, to generate a product of approximately 300 base pairs. All oligonucleotides were synthesized by VBC Genomics, Austria. The number of PCR amplification cycles varied from 25 to 35 to achieve the maximum sensitivity and specificity. The PCR products were analyzed by electrophoresis on 2% w/v agarose gels and stained using GelRed (Biotium). DNA polymerase DyNAzyme™ EXT and reaction buffers used in the PCR process were purchased from Finnzymes. Msp I digest DNA ladder was purchased by New England Biolabs.

Electrophoretic Mobility Shift Assays

We used the electrophoretic mobility shift assay methods of Orchard and May [63], slightly modified, to determine the formation of protein-DNA complexes. The His₁₀-AtoC protein was bound to double-stranded oligonucleotides spanning the representative target sites of the three promoters (atoD, metR-metE and acrD) and the narG gene. Respective quantities of each of the four indicated particles were combined with elevated His₁₀-AtoC quantities (1, 1.25, 5 and 10 μg) in 25 μl of binding buffer, 10 mM Tris [pH 7.6], 10 mM MgCl₂, 50 mM NaCl, 5% (v/v) glycerol, and incubated on ice for 25 min, following a 5 min pre-incubation at room temperature, before the addition of protein. Non-protein containing samples were quickly mixed with 10× Gel loading buffer (Takara) and all samples were applied to a 100 V prerun polyacrylamide gel containing 0.5× TBE buffer prepared with 6.0% (w/v) polyacrylamide (ratio of acrylamide to bisacrylamide, 29:1. Following electrophoresis (120 V, 120 min), the gels were stained first with Gel-Red dye (Biotium) and then with Coomassie blue.

For the DNA-binding experiments 10 ng (3 nM) of biotinylated atoD probe were used in binding buffer consisting of 10 mM Tris-HCl pH 7.4, 50 mM NaCl, 1 mM EDTA, 4 mM DTT and 5% (v/v) glycerol, as previously described [5]. Competitive non-biotinylated atoD was added in the same concentration (up to 80 nM each) with metR-metE, acrD and narG. Each reaction took place in a final volume of 20 μl consisted of 0.2 mg/ml BSA and 2 μg sonicated calf thymus competitive DNA. All the ingredients were added and pre-incubated for 5 min at room temperature, before the addition of 0.35 μM (2 μg) of the protein and transfer to ice for 30 min. At the end of binding reactions, the samples were loaded to 4% (w/v) acrylamide gel in 0.25× TBE buffer and ran at 120 V, with a 30 min prerun at 100 V. DNA was transferred from the gels to Biodyne B membranes (Pall) and detected by Phototope-Star detection kit (New England Biolabs), as previously described [4, 5].