Target transcription binding sites differentiate two groups of MerR-monovalent metal ion sensors:...

Target transcription binding sites differentiate two groups ofMerR-monovalent metal ion sensorsmmi_7370 853..865

María E. Pérez Audero,1 Brenda M. Podoroska,1

María M. Ibáñez,1 Ana Cauerhff,2 Susana K. Checa1

and Fernando C. Soncini1*1Instituto de Biología Molecular y Celular de Rosario,Departamento de Microbiología, Facultad de CienciasBioquímicas y Farmacéuticas, Universidad Nacional deRosario, Consejo Nacional de InvestigacionesCientíficas y Técnicas, Suipacha 531,S2002LRK-Rosario, Argentina.2Laboratorio de Inmunología Estructural-FundaciónInstituto Leloir, Consejo Nacional de InvestigacionesCientíficas y Técnicas, Av. Patricias Argentinas 435,C1405BWE-Ciudad Autónoma de Buenos Aires,Argentina.

Summary

The evolution of bacterial regulatory circuits ofteninvolves duplication of genes encoding transcriptionfactors that may suffer both modifications in theirdetected signals, as well as, rewiring of their targetoperators. This, and subsequent horizontal genetransfer events contribute to generate a diverse arrayof regulatory pathways. In Salmonella, two homolo-gous transcription factors CueR and GolS are respon-sible for Cu and Au sensing and resistancerespectively. They share similarities not only in theirsequence but also in their target binding sites,although they cluster separately among MerR-monovalent metal sensors. Here, we demonstrate thatCueR and GolS can selectively distinguish their targetbinding sites by recognizing bases at positions 3� and3 of their cognate operators. Swap of these basesresults in switching regulator dependency. The differ-ences in promoter architecture plus the environmen-tally controlled regulator’s cytoplasmic availabilitywarrant intra-regulon regulator–operator selectivity,and the proper response to metal injury. Furthermore,the presence of the distinctive operators’ bases iswidely extended among the two groups of MerR-monovalent metal sensors, providing evidence ofthe co-evolution of these factors and their target

operators. This approach allows the prediction ofregulator’s dependency and the identification of tran-scription modules among groups of homologoustranscription factors.

Introduction

Bacteria respond to environmental changes through avariety of sensory devices that relay the signals to thecognate DNA-binding domains to modulate transcription(Alon, 2007; Perez and Groisman, 2009; van Hijum et al.,2009). In general, and with the exception of two-component systems, the sensor and the DNA-bindingdomains are present in one single protein, the transcrip-tional regulator or transcription factor. Upon contact with adiffusible environmental signal, a physicochemical inputor an endogenous metabolite affected by the new condi-tion, the sensory domain transduces the information to theDNA-binding element (van Hijum et al., 2009) thatassumes an active conformation that may either positivelyor negatively regulate transcription of its target genes.

It is widely accepted that duplication of genes coding forboth transcription factors and their target genes is one ofthe driving forces for creating new signal transductionpathways (Teichmann and Babu, 2004; Martínez-Núñezet al., 2010). After duplication, the new transcription factormay initially recognize the same signal and promoterbinding site as its ancestor. If the duplicated genes wereadjacent on a chromosome, divergence of both the tran-scriptional regulator and the target DNA-binding site onthe controlled promoters could occur, such that the newparalogue transcription factor would regulate a new set oftarget genes and respond to a new signal, while the oldtranscription factor remains regulating its original targetgenes in response to the original signal. Horizontal genetransfer events would also contribute to disseminate newtranscription modules, therefore providing a highly diversearray of regulatory pathways (Teichmann and Babu, 2004;van Hijum et al., 2009; Martínez-Núñez et al., 2010). Theduplication of both the transcription factor and its targetgene(s), followed by both operator and signal segregationhas been recently illustrated with the two MerR regulatorsfrom Escherichia coli, the Zn(II) sensor ZntR and the Cu(I)sensor CueR (Martínez-Núñez et al., 2010). These regu-lators control the transcription of the paralogous genes

Accepted 23 August, 2010. *For correspondence. E-mail [email protected]; Tel. (+54) 341 4356369; Fax (+54) 341 4390465.

Molecular Microbiology (2010) 78(4), 853–865 � doi:10.1111/j.1365-2958.2010.07370.xFirst published online 24 September 2010

© 2010 Blackwell Publishing Ltd

zntA and copA, coding for a Zn(II) and Cu(I) transportersrespectively (Brocklehurst et al., 1999; Outten et al.,2000; Stoyanov et al., 2001; Brown et al., 2003; Hobmanet al., 2005; Summers, 2009).

The MerR family of transcriptional regulators comprisesa group of proteins with similar N-terminal DNA-bindinghelix–turn–helix and highly diverse C-terminal regionsdepending on the signal they recognize, including heavymetal ions, organic compounds or oxidative stress (Brownet al., 2003). The N-terminal DNA-binding region interactswith a pseudopalindromic operator sequence present inatypical s70-targeted promoters, which have a long 19 or20 bp spacer between the -35 and -10 elements (Ansariet al., 1995; Heldwein and Brennan, 2001; Watanabeet al., 2008). An insight into the transcriptional activationmechanism of the MerR family was recently provided fromstructural studies performed in the drug-binding BmrRregulator and in the oxidative stress sensor SoxR (Held-wein and Brennan, 2001; Watanabe et al. 2008). Bindingof the regulator to the operator sequence untwist thepromoter DNA, involving a localized base pair breaking,base sliding and realignment of the -35 and -10 operatorelements to allow the formation of the open complex bythe RNA polymerase.

Besides ZntR and CueR and their target genes, Salmo-nella enterica serovar Typhimurium also harbours a CueR-like regulator, GolS, acquired by horizontal gene transfer(McClelland et al., 2001; Checa et al., 2007; Espariz et al.2007). In spite of the similarities shared by GolS and CueRat the protein level (42% amino acid identity) and in theirtarget genes (i.e. GolS also controls the expression of acopA homologue gene, golT), we recently demonstratedthat in vivo GolS selectively responds to Au ions (Checaet al., 2007), differing from CueR that is able to induce theexpression of its target genes in response to Cu(I), Ag(I)and Au(I) with similar affinity (Changela et al., 2003; Stoy-anov and Brown, 2003). The difference in metal selectivityresides in the metal-binding loop of GolS, which is con-served among its homologues (Checa et al., 2007), sug-gesting a signal-dependent adaptation from a GolS/CueRancestral regulator.

Inspection of the CueR- and GolS-controlled operatorsreveals that both regulators recognize very similar targetsequences [(Outten et al., 2000; Stoyanov et al., 2001;Checa et al., 2007; Espariz et al., 2007; Pontel et al.,2007; Pontel and Soncini, 2009) and Fig. 2C], especiallyat the 5′ region of the operator site, which are identical inall CueR- and GolS-controlled promoters. This suggests apossible cross-recognition of the target genes by bothregulators. Indeed, during the initial characterization ofthe gol regulon, we reported, low level of Au-inducedexpression of the GolS-regulated genes in strains deletedof GolS (Checa et al., 2007; Pontel et al. 2007) that wasattributed to CueR.

Here, we show that in vivo GolS and CueR are selectivefor their innate operators and that only recognize theheterologous target binding sites in the absence of theother regulator. This selectivity is achieved by a combina-tion of both the presence of specific bases located atpositions 3′ and 3 of the operators, and a tight control ofthe regulator’s intracellular concentration. The phyloge-netic analysis of GolS and CueR homologues and theidentification of their predicted target binding sites allowus to distinguish and assign transcription modules amongthese groups of regulators.

Results

GolS and CueR can interact with the other regulon’starget genes in the absence of the innate regulator

During the characterization of the gol regulon, wenoticed low levels of Au-mediated induction of golB inthe DgolS mutant as well as in strains harbouring non-functional golS alleles (Checa et al., 2007), andassumed that this induction was driven by CueR. Tofurther examine this effect, we compared the metal-induced expression of GolS- and CueR-dependentgenes between the wild-type strain and mutants deletedin golS and/or cueR. Reporter strains harbouring chro-mosomal (Fig. 1) or plasmidic (Fig. S1) lacZ fusions toGolS- or CueR-controlled promoters were grown inLuria–Bertani medium (LB) in the absence or in thepresence of either 40 mM AuHCl4 or 1 mM CuSO4, con-centrations required to induce these regulons (Esparizet al., 2007; Pontel et al., 2007). Expression from theGolS-dependent promoters PgolB and PgolTS wasstrongly induced by Au both in the wild type and in theDcueR mutant strains (Figs 1A and S1A). Au-activatedexpression from these promoters was severely reducedbut still evident in the DgolS strain, and only completelyabolished in the DgolS DcueR double mutant strain. Onthe other hand, and as previously reported (Esparizet al., 2007), expression from the CueR-controlled pro-moters PcopA and PcuiD was induced either by Cu orAu in the wild-type strain (Figs 1B and S1B). Althoughthe response to Cu was abrogated in the DcueR strain,a significant level of Au-induced transcription of thesegenes was still observed in the DcueR strain (Figs 1Band S1B). This transcription was dependent on GolS, asit was eliminated in a mutant lacking both golS andcueR. Altogether these results establish that both GolSand CueR can cross-recognize and activate the paralo-gous promoters in the absence of the innate regulator.

Interestingly, the Au-mediated transcription of CueR-dependent genes increased (two to threefold) in theDgolS strain compared with the wild type (Figs 1B andS1B). Similarly, a small induction of the expression of

854 M. E. Pérez Audero et al. �

© 2010 Blackwell Publishing Ltd, Molecular Microbiology, 78, 853–865

golB and golTS in the presence of Cu was detected inthe DcueR mutant (Figs 1A and S1A). These effects canbe attributed to an augmented intracellular metal con-centration because of the lack of expression of theappropriate efflux mechanism mediated by the specificmetal sensor.

GolS and CueR recognize paralogous operatorsalthough with lower affinity

To experimentally define the GolS operators, and tocompare them with the CueR promoter binding sites fromSalmonella and E. coli (Outten et al., 2000; Stoyanovet al., 2001; Yamamoto and Ishihama, 2005; Checa et al.,2007; Espariz et al., 2007; Pontel et al., 2007; Pontel andSoncini, 2009), we performed DNase I footprinting assayson both the coding and non-coding strands of the golBand golTS promoter fragments using purified GolS(Fig. 2A and B). The regulator protected from nt -9 to nt-34, and from nt -11 to nt -36, relative to the transcriptionstart site of the PgolB promoter in the coding and non-coding strands, and from nt -11 to nt -36, and from nt -12to nt -37 of the PgolTS promoter in the coding andnon-coding strands, respectively, with bases showinghypersensitivity to DNase I cleavage, as was observed

previously for this type of regulators (O’Halloran et al.,1989; Ansari et al., 1995; Outten et al., 2000; Esparizet al., 2007; Pontel et al., 2007). Alignment of the GolS-and CueR-protected regions in Salmonella and E. colirevealed a high degree of similarity (Fig. 2C), particularlyat 5′ region of the regulator binding site, highlighting thepresence of a conserved inverted dyad among all GolS-and CueR-controlled operators.

By using resonant mirror biosensor technology (IAsysaffinity sensor analysis) (Biswas et al., 2004) we esti-mated the affinities of GolS and CueR for biotinylatedDNA fragments containing the promoters of golBand copA. A dissociation equilibrium constant (KD) of8.7 ¥ 10-9 M was estimated for the GolS–PgolB interac-tion and 8.5 ¥ 10-8 M, for the CueR–PcopA interaction.By contrast, the KD values for GolS–PcopA and theCueR–PgolB crossed interactions were estimated to be5.7 ¥ 10-7 M and 7.2 ¥ 10-7 M respectively. Similar resultswere obtained by electrophoresis mobility shift (EMSA)analysis (Fig. 3A). Although no calculation of DNA-bindingconstant could be done from these semi-quantitativeassays, the interaction of GolS with the golB promoterwas 2–3 orders of magnitude stronger than with PcopA,and the interaction of CueR with PcopA was an orderof magnitude stronger than with PgolB. These results

Fig. 1. GolS and CueR cross-activatepromoters in the absence of the innateregulator. b-Galactosidase activities fromGolS- (A) and CueR- (B) regulatedtranscriptional fusions expressed on wild-type(wt), DgolS, DcueR or DgolS DcueR cellsgrown overnight in LB broth without (-) orwith the addition of 40 mM AuHCl4 (Au) or1 mM CuSO4 (Cu) (Espariz et al., 2007;Pontel et al., 2007). The data correspond tomean values of four independent experimentsperformed in duplicate. Error bars correspondto the standard deviations (SD).

Operator discrimination between paralogue regulators 855


Fig. 2. GolS and CueR recognized similar operator sequences.A and B. DNA footprinting analysis of the promoter region of golB (A) and golTS (B) performed on both end-labelled coding and non-codingstrands. The DNA fragments were incubated with purified GolS at a final concentration of 4 mM. Solid lines and arrows indicate theGolS-protected region and hypersensitive sites respectively.C. Promoter sequence alignment of genes controlled by GolS or CueR from Salmonella (CueRSTM) and E. coli (CueREc). Lines above or beloweach sequence indicate DNase I protected regions. Triangles indicate hypersensitive bands. The transcription start sites are indicated with anarrow and in boldface, the predicted -10 and -35 elements are boxed. The nucleotide bases at 3′ and 3 positions are highlighted. Data wereextracted from (A) and (B), as well as from References. (Outten et al., 2000; Stoyanov et al., 2001; Yamamoto and Ishihama, 2005; Esparizet al., 2007; Pontel et al., 2007; Pontel and Soncini, 2009).



indicate that, although both regulators can recognize theother regulon’s promoters, there are specific traits respon-sible for the preferential recognition between the regulatorand its target operators.

Binding of GolS and CueR to both golB and copA pro-moters was also compared by DNase I footprintingassays (Fig. 3C). The protein concentration used in thisexperiment was adjusted according to the in vitro affinity

determined by IAsys. The pattern of protected regions andthe bands showing hypersensitivity obtained for a givenpromoter were identical irrespective of the transcriptionfactor used (Fig. 3C), indicating that both golB and copAoperators can be recognized by either CueR or GolS in asimilar manner. As expected higher regulator’s concentra-tions were required for the heterologous interaction toattain the same level of protection than for the innate

Fig. 3. Purified GolS and CueR cross-recognize operators.A and B. Electrophoretic gel mobility shift assay analysis using 6 fmol of 32P 3′-end-labelled PCR fragment from the wild-type (A) or mutant (B)versions of the golB and copA promoter regions and purified GolS or CueR as indicated. GolS was used at 0, 0.005, 0.01, 0.02, 0.05, 0.100,0.2, 0.4, 0.8 and 1.5 mM final concentrations, and CueR was used at 0, 0.25, 0.5, 0.75, 1, 1.25, 1.5 and 3 mM, in all cases.C. DNA footprinting analysis of the golB and copA promoter regions. The DNA fragments from PgolB and PcopA end-labelled at thenon-coding and coding strands, respectively, were incubated with purified GolS or CueR or without protein (-) as indicated. GolS was used at1.25, 2.5 and 4 mM final concentration for PgolB and 3, 6 and 12 mM for PcopA. CueR was used at 1.25, 3 and 6 mM final concentration forPcopA and 12.5, 25 and 50 mM for PgolB. Solid lines and arrows indicate the regulator-protected region and hypersensitive sites respectively.



promoter fragment, which suggest that in vivo cross-recognition depends on the cytoplasmic concentrations ofeach transcription factors in a given condition.

In vivo analysis of intra-regulon regulator–operatorspecificity

To assess the cytoplasmic concentration of GolS andCueR in the presence of AuHCl4 or CuSO4, we generatedFLAG-tagged alleles of GolS and CueR by introducinga C-terminus FLAG-tag coding sequences at the3′-terminus of golS or cueR in the chromosome. TheFLAG-tagged alleles of the regulators displayed wild-typemetal responses when testing the expression of theappropriated lacZ reporter fusions (Fig. S2). Cellsexpressing GolS–FLAG or CueR–FLAG were grownovernight in the presence or absence of metals, and theintracellular concentrations of both proteins were deter-mined by semi-quantitative Western blot analysis usingpurified GolS–FLAG and CueR–FLAG fusion proteins asstandards (Fig. 4A). In the absence of metals, the intra-cellular concentration of CueR was estimated to be75 � 32 nM (Fig. 4A), while GolS concentration was esti-mated c. 18 � 9 nM, meaning less than four dimers percell. According to the in vitro affinity constants determinedby IAsys, the concentration of each regulator is highenough to interact with its innate operators, and wellbelow the concentration required for the recognition of theheterologous operators.

In cells grown in the presence of AuHCl4, and as theresult of its autoregulation (Checa et al., 2007) GolSreached an intracellular concentration of 7700 � 970 nM,an increment of more than 400-fold over its basal levels,while CueR concentration increased to 256 � 109 nM(Fig. 4A). In this condition, and giving the high amount ofGolS present in the cytoplasm, one can envisionthat GolS can effectively out-compete CueR for thecopA operator. Therefore, part of the Au inductiondetected in CueR-regulated genes in the wild-type strainunder long-term gold exposure may be promoted by GolS(Fig. 1B).

In the presence of CuSO4, CueR concentration raisedto 349 � 97 nM (Fig. 4A), fourfold higher than the esti-mated CueR–PcopA KD, but still below the CueR–PgolBKD, ensuring intra-regulon specificity. As cueR is notautoregulated, the increase in CueR concentration in thepresence of Cu may be the result of either transcrip-tional induction by an uncharacterized factor, or proteinstabilization (Egler et al., 2005). In this condition, theconcentration of GolS increased less than twofold(28 � 13 nM).

To test the above predictions we investigated the in vivoGolS or CueR promoter occupancy by chromatin immu-noprecipitation (ChIP) analysis (Fig. 4B) in cells grown in

the presence or absence of metals. Amplification of golTSand golB promoter fragments was evident with GolS-immunoprecipitated DNA samples obtained from cellsgrown in the presence of AuHCl4, but not fromCu-containing cultures or cells cultured in the absenceof metals. CueR-controlled promoter fragments weredetected in CueR-immunoprecipitated DNA samples fromeither Au- or Cu-containing cultures, and with less inten-sity from cultures grown in the absence of any addedmetal (Fig. 4B). Amplification bands of CueR-controlledpromoters were also evident in GolS-immunoprecipitatedDNA samples obtained from cells grown in the presenceof Au, as expected from the in vitro affinity constants andthe intracellular concentration of GolS in inducing condi-tions (Figs 3 and 4A).

Fig. 4. In vivo GolS and CueR promoter occupancy.A. Intracellular concentration of the transcription factors (TF) GolS(black bars) or CueR (gray bars) in wild-type cells carrying thechromosomal golS–FLAG and cueR–FLAG alleles. Bacteria weregrown as indicated in Fig. 1 and the level of each transcriptionalfactor was determined by semi-quantitative Western blot asdescribed in Experimental procedures. The plot is the mean valueof five independent experiments.B. Chromatin immunoprecipitation (ChIP) of the GolS-regulatedpromoters PgolTS and PgolB, and the CueR-regulated promotersPcopA, PcuiD and PcueP in wild-type cells. Total chromatin wasused as positive control (T) before incubation with 5 (+) or 50 (++)ml of polyclonal anti-GolS, or 2 (+) or 10 (++) ml of anti-CueR.Incubation with pre-immune sera was used as negative control(–ab). The PhoP-regulated promoter PmgtA was included in theassays as a negative control. The gels shown are a representativeresult from at least three independent experiments.



Operator bases at positions 3� and 3 confer regulatorspecificity among the cue and gol regulons

Both in vitro affinity studies and in vivo occupancyexperiments suggest that besides intracellular regulatorabundance, particular traits in the encoded operatorsequences may ensure binding of the cognate regulatorand disfavouring non-cognate operator/regulatorinteractions. Two independent MEME searches were per-formed with the three Salmonella gol operators and withthe five cue operators present in Salmonella and in E. coli.The program was set up to search for palindromic motifs,which allows the internal comparison of each hemi-site(Fig. S3). This analysis revealed the prevalence of an Aconserved at the 3′ position and a T at the 3 positionrelative to the centre of the operator in the gol promoters(Figs 2C and S3). By contrast, cue operators from bothSalmonella and E. coli have either a C or a G at equivalentpositions. Thus, we generated lacZ reporter plasmids car-rying the single and double mutant versions at the 3′ and3 operator positions of PgolB or PcopA, and tested theirregulator dependences comparing with wild-type versionsof the promoters (Figs 5 and S4).

The simultaneous replacement of the A and the T by Cat positions 3′ and 3 in the golB operator rendered apromoter that drove a similar reporter gene expressionpattern than the native copA promoter (Fig. 5B); i.e. (i)transcription from PgolBCC promoter was induced by Auand Cu in the wild-type strain, (ii) there was no inductionof the reporter gene in the presence of Cu in the DcueRstrain, while golS deletion did not significantly affect itsCu-mediated activation and (iii) the response to Au ionsincreased at least twofold in the DgolS strain, and slightlydecreased in the DcueR mutant. Similarly, the reportergene under the PcopAAT promoter resembled a nativegolB promoter. The response to Au decreased in theDgolS strain, but was not affected in the DcueR mutant,while Cu-mediated activation was only evident in thestrain deleted in cueR. Single mutant versions of thepromoters displayed a pattern of metal induction inbetween of the wild-type and the double mutant promot-ers (Fig. S4). However, the identity of the base at position3′ appeared to be more relevant for the regulator’s recog-nition than that located at position 3.

The relative in vitro binding affinities of GolS and CueRfor the mutant operators were compared with their wild-type forms by EMSA (Fig 3A and B). At least 300-foldhigher concentration of GolS was required to detect aninteraction with PgolBCC than with native PgolB. Besides,CueR displayed an increased affinity for the mutated golBpromoter. Conversely, GolS exhibited a substantialincreased affinity for PcopAAT compared with the nativecopA promoter, while CueR showed a decreased affinityfor PcopAAT.

Phylogenetic correlation between the 3� and 3operator’s position and its regulator dependency

We performed a phylogenetic analysis of 60 GolS- orCueR-like regulator sequences in 45 genomes from pre-viously analysed species (Checa et al., 2007) as well asfrom species recently examined for their copperresponses (Williamson et al. 2006; Jian et al., 2009;

Fig. 5. Two nucleotide bases determine regulator/operatorselectivity.A. Sequence of the native (PgolB and PcopA) or mutant versions(PgolBCC and PcopAAT) of the golB and copA promoters. Bases atthe 3′ and 3 positions relative to the centre (position 1) of theoperator and the -10 and -35 elements (boxes) are indicated.B. b-Galactosidase activity (Miller units) was determined onwild-type (wt), DgolS, DcueR or DgolS DcueR cells carrying thereporter plasmids pPgolB, pPgolBCC, pPcopA or pPcopAAT as inFig. 1. (**Overnight cultures of the DgolS strain harbouring thePcopA reporter plasmid in the presence of 40 mM AuHCl4 renderedlow OD600. This toxic phenotype could be attributed to the level ofb-galactosidase expression in this condition.) The data correspondto the mean value of three independent experiments done induplicate. Error bars correspond to the standard deviations (SD).



Table S1). As already demonstrated (Checa et al., 2007),GolS clustered in a separate branch from CueR and itshomologues (Fig. 6). Then, a consensus matrix for GolS/CueR-binding sites was generated by MEME (Bailey and

Gribskov, 1998) with the CueR/GolS operators present inSalmonella and in E. coli, and used to screen the selectedgenomes. All operators identified in the 33 genomes thatharbour only one CueR- or GolS-coding gene were



directly assigned as controlled by these regulators. In thecases in which there were more than one CueR/GolS-coding gene, we assigned regulator’s dependency basedprimarily on genomic localization, because usually theregulator’s gene and its predicted targets localized inclose proximity forming genetic clusters (Table S2).Orphan operators were not considered for further analysisin case of discrepancy about their regulator’s dependence(see Table S2). With this information, we paired eachregulator on the phylogenetic tree with its putatively rec-ognized operators. For simplicity, only the nucleotidebases at positions 3′ and 3 of each operator are shown inFig. 6. (The complete operator sequences recognized byeach analysed regulator are displayed in Table S2.)Remarkably, the majority of the putative operators pre-dicted to be controlled by GolS-like regulators conservedan A residue at position 3′ in the dyad, and a T at 3.Conversely, most of predicted CueR-like regulated opera-tors harboured G/C in these positions.

This analysis provides the demonstration of phyloge-netic differences between GolS xenologues and the CueRcluster, as initially proposed (Checa et al., 2007). In addi-tion it allows the identification of transcription modulesbelonging to these paralogous regulators in other Gram-negative bacterial species.

Discussion

Evolution of bacterial regulatory circuits often involves theduplication of a transcription factor coding gene (Teich-mann and Babu, 2004; van Hijum et al., 2009; Martínez-Núñez et al., 2010). The new factor may initially recognizethe same binding site and, hence, regulate the sametarget gene as the original regulator, but soon it canevolve to respond to a different signal and/or improves itsbinding to a subset of the original controlled operators.Alternatively, the new transcriptional regulator may beginto recognize new binding sites upstream of other targetgenes. Dissemination by horizontal gene transfer wouldalso favour the evolution and complexity of the transcrip-tional regulatory network. As single-input modules andbeing widely spread among microorganisms, high-specificity regulators that control a small number of geneswith similar functions are suitable models to analyse bothnew input signal detection by related transcription factorsand promoter rewiring in the evolution of bacterial

regulons. In Salmonella, GolS and CueR can be distin-guished as paralogues that evolved from a commonancestor. These regulators diverged in signal recognition,as GolS prefers Au(I) ions while CueR promotes similarlevels of transcription of its target genes in response toeither Cu(I), Ag(I) or Au(I) (Changela et al., 2003; Stoy-anov and Brown, 2003; Checa et al., 2007; Espariz et al.,2007). And as it is shown in this work, they also evolvedto distinguish their target transcription binding sites.Although the CueR and GolS operators are remarkablysimilar (Fig. 2C), and residual regulon cross-activationhas been observed in strains lacking one of the regulators[Fig. 1; (Checa et al., 2007; Pontel et al., 2007)], eachtranscription factor displays higher affinity for its innateoperators than for the target DNA-binding site of the otherregulator (Fig. 3). In fact, in vivo wild-type cross-activationbetween gol and cue regulons is prevented by a combi-nation of both promoter architecture [differences in opera-tor sequences at positions 3′ and 3 (Figs 2C and 5)] andmodulation of transcriptional factor availability (Fig. 4A),which determines effective promoter occupancy (Fig. 4B).

Expression from PgesABC was an exception, becauseit was only induced by GolS. Unlike the other GolS-controlled genes, no CueR-mediated activation ofgesABC was observed in a DgolS strain (Fig. S1A). Thiswas not completely unexpected, because we have previ-ously reported that the operator at the gesABC promoteris less conserved than those present in the other twoGolS-regulated loci (Pontel et al., 2007). The lack of simi-larity is extended to the consensus CueR target bindingsite, making the PgesABC promoter not recognized byCueR even in the absence of GolS.

The identity of the operator bases at positions 3′ and 3and the predicted regulator’s dependency is not restrictedto the Salmonella gol and cue regulons. While the majorityof the GolS-like putative operators detected in our phylo-genetic analysis harbour an A and a T at positions 3′ and3, respectively, the CueR-like operators harbour G or C atthese positions (Fig. 6). Indeed, the genes under thecontrol of the recently characterized Cupriavidus metalli-durans CH34 gold sensor CupR (Jian et al., 2009), whichclustered together with GolS and separate from theenterobacterial copper sensors, harbour A and T bases atpositions 3′ and 3 respectively (Fig. 6).

It was demonstrated that CupR controls the expressionof the Rmet_3523/Rmet_3525 cup-locus (cupAR cupC),

Fig. 6. The identity of operator’s bases 3′ and 3 distinguishes bacterial GolS-like versus CueR-like regulated operators. Phylogenetic treeobtained by comparison of the full length GolS-like and CueR-like regulators (Table S1). The tree was constructed by Bayesian inference asdescribed in Experimental procedures. The numbers on the branches indicate the bipartition probabilities calculated by the 50% majority rule.The scale bar represents 0.1 amino acid substitutions per site. The nucleotide bases at position 3′ and 3 of the assigned operator/s for eachGolS or CueR homologue are listed on the right (see the text for details on the analysis and assignation of operators). The complete list of allputative GolS or CueR operator sequences identified, as well as the score, locus Tag, product names and other information are provided inTable S2.



which codes for a P-type ATPase similar to GolT, for theregulator itself, and for a metal chaperone (Jian et al.,2009; Reith et al., 2009). Transcriptomic analysis con-ducted in this bacterium revealed that the cup-locus wasamong the most strongly upregulated genes at all con-centration tested, with cupC being the most stronglyupregulated gene at the lowest AuHCl4 concentrationused (Jian et al., 2009). In vivo and in vitro CupR metalresponse also favoured the role of the cup cluster in theselective detection of gold in C. metallidurans (Jian et al.,2009). The development of resistance mechanismsagainst toxic gold ions would certainly be advantageousto improve this species survival on auriferous soils wheregold concentrations can exceed 100 ppb. Indeed, C. met-allidurans was isolated from biofilms grown on Au grainsretrieved from soils and sediments from temperate andtropical Australian sites (Reith et al., 2009). This bacte-rium is able to precipitate gold from solutions and it wasproposed to be involved in the formation of grains ofsecondary gold in soils. Therefore, it constitutes anexample of a species adapted to cope with Au toxicity.

Our in silico analysis predicts that CupR would alsocontrol the expression of pMOL30-encoded genes copOand copF (and probably copG and copJ) (Table S2). Thesegenes are part of the CopRS-controlled copper-resistancelocus detected in C. metallidurans (Monchy et al., 2006),suggesting a complex regulation of these genes. Interest-ingly, the putative CupR operator found upstream thesecopOF(GJ) operon is less conserved than the CupR-dependent cup operators (note the presence of a C atposition 3 of the predicted copO operator, Fig. 6 andTable S2). Therefore, we propose that CupR-dependentexpression of these genes will be relevant under long-termgold exposure, similarly to the GolS-dependent inductionof the Salmonella gesABC operon (Pontel et al., 2007).

Although our results clearly indicate that GolS and itsxenologues diverged from an ancestral monovalentmetal ion regulator to recognize distinctive target sites,the metal ion selectivity of a particular GolS xenologuewill depend on the simultaneous presence of othercoinage metal ion resistance/detoxification systems inthe bacterium. In this regard, we argue that in Salmo-nella, GolS selectively recognizes gold because of thepresence of the copper-resistance cue system (Kimet al., 2002; Espariz et al., 2007). The cue system main-tains the cytoplasmic levels of ‘available’ copper ionbellow the concentrations required to activate GolS.Indeed, we have previously shown that GolS can inducethe expression of GolT and GolB in the presence ofcopper, in a mutant deleted in the copper exporter CopA(Kim et al., 2002; Espariz et al., 2007). This can beexplained by the deregulation of the copper homeostasiscaused by the absence of the copper exporter. Theexpression of GolT in this condition will partially com-

pensate the deficiency in Cu efflux (Osman et al., 2010).Nevertheless, even in this artificial condition, the activa-tion of GolS by copper does not reach the required levelof the ‘activated’ sensor to induce the expression of theges operon, which is only induced by gold activatedGolS (Pontel et al., 2007).

Accordingly, from our in silico analysis we predict that inC. metallidurans CupR selectively detects gold becauseof the presence of two cop copper-resistance clusters inthe CHR1 chromosome and in the pMOL30 plasmid(Monchy et al., 2006; Jian et al., 2009). Nevertheless,metal selectivity remains to be experimentally establishedfor the rest of GolS or CueR xenologues.

The differences at the operator target sequencesdetermining regulator/operator selectivity must correlatewith modifications in the DNA-binding domains of thetranscription factor. The structure of the MerR homo-logues SoxR and BmrR bound to their operators pre-dicts a conserved mechanism in the DNA recognition,being the a2-helix of the N-terminal helix–turn–helixmotif the region responsible for contacting the DNA andfor the specificity in this interaction (Heldwein andBrennan, 2001; Watanabe et al., 2008). According to theoxidized [2Fe-2S]2+ form of SoxR bound to DNA struc-ture, base-specific interactions by the serine residue atposition 26 of the a2-helix are predicted to play animportant role in the interaction with the operator of soxSpromoter, SoxR only known target gene (Watanabeet al., 2008). The DNA-binding domain of GolS andCueR are 43% identical, with only few differences at theamino acid residues encompassing the a2-helix. Fur-thermore, all GolS/CueR homologues conserve a Kresidue at this position (K15), which agrees with the simi-larity in the DNA target site recognized by both groups ofregulators (Fig. 2C). Interestingly, all GolS homologuesharbour a conserved methionine residue at position 16within this helix (Table S1), while a threonine, an alanineor a serine but not a methionine is present in CueRhomologues. A more detailed analysis of the function ofthis conserved residue in the operator’s selectivity isunder way in our laboratory.

In sum, here we describe an integrated approach todissect the evolution of bacterial paralogous regulatorycircuits based on the co-evolution of local regulators andtheir target operators. This analysis would also offer apowerful resource to better understand the evolution ofmore complex regulatory networks.

Experimental procedures

Bacterial strains and growth conditions

All S. enterica serovar Typhimurium strains are derived fromwild-type strain 14028s and were constructed by phage P22-mediated transductions as described (Davis et al., 1980).



Strains, plasmids and oligonucleotides used in this study arelisted in Tables S3 and S4. Cells were grown at 37°C in LBbroth or on LB agar plates. Ampicillin and tetracycline wereused at 100 and 15 mg ml-1 respectively. IPTG (0.5 mM) wasadded to promote the expression of native or FLAG-taggedGolS and CueR. Reagents, chemicals and oligonucleotideswere from Sigma, except the LB culture media that was fromDifco.

Bacterial genetic and molecular biology techniques

Strains carrying golS::1xFLAG (PB7647) or cueR::1xFLAG(PB7652) alleles were constructed essentially as described(Pontel and Soncini, 2009), with plasmid pSU312 as template(Uzzau et al., 2001).

Plasmids carrying lacZ gene fusions to the wild-typeversions of GolS- or CueR-controlled promoters wereconstructed as follows. Blunt-end fragments from each pro-moter were PCR-amplified from Salmonella 14028sgenomic DNA using the appropriate primers (listed inTable S4) and Pfx-50 DNA polymerase (Invitrogen). Theamplified products [from nt 398263 to nt 398595 for PgolTSand PgesABC; from nt 401137 to nt 401440 for PgolB, fromnt 560411 to nt 560591 for PcopA; and from nt 196268 tont 196570 for PcuiD, as defined by the S. enterica serovarTyphimurium LT2 genome sequence (McClelland et al.,2001)] were treated with XmaI and cloned into plasmidpMC1871 digested with the same restriction enzyme. Notethat pPgolTS (PB1223) and pPges (pPB1224) were gener-ated with the same amplification product (Checa et al.,2007; Pontel et al., 2007). The completed list of plasmids isprovided in Table S3.

Plasmids harbouring the mutant versions of golB andcopA promoters (Table S3) were generated by PCR-mediated overlap extension site-directed mutagenesis(Aiyar et al., 1996). For mutant golB promoters, a 110 bpfragment was amplified using wild-type pPgolB as template,primer PgolB–Rv and each of the primers carrying the mis-matched base/s listed in Table S4, as required. The frag-ments were purified and used as primers along withPgolB–Fw to amplify a 320 bp product from the same DNAtemplate. The mutated fragments were independentlycloned into pMC1871 vector as described above. To obtainmutant pPcopA, a 140 bp fragment was amplified usingwild-type pPcopA as template, primer PcopA–Rv, and eachof the primers carrying the mismatched base/s (Table S4),as required. The fragments were purified and used asprimer along with pMC–Fw to amplify a 635 bp product fromthe same DNA template. Each mutated fragment wastreated with XmaI, and the 195 bp released fragment wascloned into digested pMC1871 vector.

Plasmids pGOLS–FLAG (pPB 1277) and pCUER–FLAG(pPB 1278) (Table S3) were generated as follows. BothgolS::1xFLAG and cueR::1xFLAG loci were PCR-amplifiedfrom PB7647 or PB7652 genomic DNA, respectively, usingeither primers golS–ORF–Fw or cueR–ORF–Fw, and FLAG–HindIII–Rv, as required (Table S4). The amplified fragmentswere treated with BamHI and HindIII and cloned into digestedpUH21–2laqIq vector. These expression plasmids were usedto purify GolS–FLAG and CueR–FLAG respectively. All con-structs were verified by DNA sequencing.

Metal induction and quantification assays

Metal induction assays of GolS- and CueR-controlled genesby 40 mM AuHCl3 or 1 mM CuSO4 were done on overnightcultures essentially as described (Checa et al., 2007).

Quantification of GolS and CueR were done on whole-cellextracts (30 mg of total protein) from PB7647 (golS::1xFLAG)or PB7652 (cueR::1xFLAG) strains (Table S3) grown over-night in the presence or absence of Au or Cu salts. Proteinswere analysed by SDS-PAGE (15% polyacrylamide gels) andWestern blot, using monoclonal anti-FLAG antibodies(Sigma-Aldrich) and SuperSignal West Femto Trial Kit(Pierce). Calibration curves were done with purified GolS–1xFLAG and CueR–1xFLAG. Calculations were based on theassumption of a bacterial cellular volume of 1.9 fl (Lejonaet al., 2004) and a molecular mass of 18.3 kDa for GolS–1xFLAG and 16.6 kDa for CueR–1xFLAG. Native and FLAG-tagged forms of the transcriptional regulators were purifiedfrom E. coli DH5a strain carrying the corresponding expres-sion plasmids as described (Checa et al., 2007).

Protein–DNA interaction analysis

Electrophoretic gel mobility shift and DNase I protectionassays were performed using purified GolS or CueR asdescribed (Pontel et al., 2007). (Primers used to amplify thewild-type or mutated versions of the promoters are listed inTable S4.) Chromatin immunoprecipitation (ChIP) assayswere performed as described (Checa et al., 2007). Cross-linked extract obtained from the wild-type strain grown over-night in the absence or presence of Au or Cu salts wereincubated with anti-GolS or anti-CueR rabbit polyclonal anti-sera as indicated. A 300 bp PhoP-dependent mgtA promoterfragment (Spinelli et al., 2008) was used as a negativecontrol in these experiments.

IAsys affinity sensor analysis experiments were conductedon the IAsys Plus apparatus (Affinity Sensors, Saxon Hill,Cambridge, UK) essentially as described (Castelli et al.,2003). Biotin-tagged DNA ligate harbouring golB and copApromoters were generated by incubating pairs of singlestrand oligonucleotides (Table S4) at 85°C for 10 min andthen allowing to cold at room temperatures. Tagged DNA (at1 mM final concentration in buffer 20 mM Tris-HCl pH = 8.0,150 mM NaCl) was bound to individual streptavidin-coupledcarboxymethyl dextran sensor chips (Affinity Sensors) at20°C for 15 min, using the protocol recommended by themanufacturer. Purified GolS or CueR was added to reachmultiple protein concentrations (ranging 0.05–5 mM) to indi-vidual cuvettes and protein binding to immobilized DNA wasallowed to occur for 5 min to obtain a saturating equilibriumresponse (Req) and the data were collected at interval of0.3 s. All binding reactions were carried out in binding buffer20 mM Tris-HCl pH = 8.0, 150 mM NaCl, 5 mM DTT, at 25°Cwith constant stirring. Dissociation was measured afteradding binding buffer. Bovine serum albumin (1.0 mM) inter-action was tested as a non-related control protein, renderingbasal level sensorgrams.

Kinetic analysis was performed using the FASTfit software(Affinity Sensors), which provided the values of kon and kdissat each protein concentration. Second-order association rateconstants (kass) were obtained from a linear regression plot



of kon versus protein concentration. Dissociation equilibriumconstants (KD) were then derived for each reaction from theequation: KD = kdiss/kass.

Bioinformatics

Complete protein sequences of GolS or CueR homologues(see Table S1 and S2 for sequence, accession numbers andspecies names) were analysed as described (Checa et al.,2007). The consensus motif for GolS/CueR-binding site wasgenerated using multiple expectation maximization for motifelicitation tool, MEME, with operators recognized by theseregulators in Salmonella and E. coli (Fig. 2C). The position-specific scoring matrix obtained was then used to search formatching motifs by MAST in the 45 genome sequences ofspecies harbouring GolS- or CueR-like regulators (Table S2)as described (Pontel et al., 2007; Pontel and Soncini, 2009).

Acknowledgements

We thank E. García Véscovi, L. Pontel and E. Serra for criticalreading and comments on the manuscript and E. Serra for hishelpful advice in the phylogenetic analysis of GolS/CueRhomologues. This work was supported by grants fromAgencia Nacional de Promoción Científica y Tecnológica andfrom the National Research Council CONICET to S.K.C. andF.C.S. M.E.P.A. and M.M.I. are fellow of the CONICET. A.C.,S.K.C. and F.C.S. are career investigators of CONICET.F.C.S. is also a career investigator of the Rosario NationalUniversity Research Council (CIUNR).

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