The PmrAB two-component regulatory system is part of a multicomponent feedback loop that acts as one of the key regulatory mechanisms of Salmonella typhimurium virulence [1–3]. The PmrAB regulatory system is itself responsive to Fe³⁺ and mild acid [4] and senses Mg²⁺ indirectly by communicating with the Mg²⁺-sensitive PhoPQ system [5–8] via PmrD [1, 9]. PmrD is hypothesized to transduce the signal from the PhoPQ system to the PmrAB system via a posttranslational modification. The gene pmrD is transcriptionally activated by the PhoPQ system but repressed by the PmrAB system [1, 9, 10]. The PmrAB system is required for resistance to the cationic antibiotic polymyxin B [11] and to Fe³⁺-mediated killing [4]. The Mg²⁺-dependent regulation of PmrAB was shown to be important for gene expression in an intracellular environment [12]. Fe³⁺-dependent PmrAB regulation, on the other hand, has been hypothesized to be essential for survival in extracellular environments [13]. A region in DNA to which the PmrA protein binds has been identified by DNA footprinting analysis [14, 15].

In contrast to pmrD, other known target genes of PmrAB in S. typhimurium are transcriptionally activated. One group of targets is involved in LPS modification. PmrAB-induced modifications include the addition of 4-amino-4-deoxy-L-arabinose (Ara4N) and phosphoethanolamine (pEtN) to lipid A [16]. Loci involved in the Ara4N modification of lipid A are ugd [6] and the pmrHFIJKLM loci, both of which are responsible for Ara4N biosynthesis [2, 16–18] and incorporation of Ara4N into lipid A [19, 20]. LPS modifications are hypothesized to allow bacterial survival within macrophages by lowering the affinity of the LPS for amphipathic cationic peptides with antimicrobial activity that are produced as a consequence of the innate immune response.

A second class of targets are directly dependent on PmrAB, but have as-yet-undefined functions. pmrC (co-transcribed with pmrAB [21]) and pmrG (located upstream of the pmrHFIJKLM operon) are both transcriptionally activated by PmrAB. Mutations in pmrG did not affect the resistance to polymyxin B [2]. Tamayo et al. recently identified two additional targets of PmrAB - yibD and dgoA. However, none of these was involved in resistance to polymyxin B or to high concentrations of Fe³⁺ [22]. These genes might therefore represent a group of as-yet-unidentified functions regulated by the PmrAB system [22]. Also, PmrAB-regulated genes involved in resistance to Fe³⁺ and pEtN addition to LPS remain to be identified [22]. Together with the recent indications of new PmrAB-dependent functions, this raises the possibility that not all PmrAB targets have yet been identified. Therefore, in this study we used an in silico approach to predict targets of the PmrAB regulatory system. Several methodologies exist for genome-wide screening using a motif model (or mathematical representation) of experimentally verified regulatory sites [23–27]. These assign to each possible motif position in the genome a score (the specifics of which depend on the methodology) that indicates how well the subsequence located at that position matches the motif model. Genome-wide screenings of this type have proved successful in detecting additional targets of the regulator being investigated. However, more reliable predictions for motifs in specific pathways have been obtained by incorporating cross-species comparisons (phylogenetic footprinting) [26, 28–34]. Because evolutionary forces tend to preferentially retain functional DNA sequences, motifs that are conserved in the intergenic regions of orthologs derived from several related species are more likely to be biologically relevant [35, 36].

In this study, we combine both approaches. Putative targets identified by a genome-wide screening were, whenever possible, analyzed by phylogenetic footprinting based on Gibbs sampling [33, 34, 37]. Four interesting targets were validated by wet lab experiments and the PmrA box of a representative target was subjected to site-directed mutagenesis.

Putative PmrAB targets were detected by genome-wide screening of S. typhimurium intergenic sequences using a PmrA motif model. If possible, the confidence in the predicted motifs was strengthened by a cross-species comparison: we tested whether the PmrA motif was conserved in the intergenic regions of close homologs in related species. To this end, we developed a two-step procedure for phylogenetic footprinting. In the first step, a motif-detection procedure based on Gibbs sampling was performed to generate a list of motifs. In the second step, these motifs were used as seeds to generate local multiple alignments. Eventually, the biological relevance of the obtained alignments was assessed.

We used the alignments rather than a listing of the high-scoring motifs obtained by Gibbs sampling for the following reasons. First, we observed, as also reported by McCue et al., a high overall similarity in intergenic regions of the selected species [34]. In general, the overall degree in conservation between the intergenic sequences of close homologs is about 93.56% for the sequenced representatives of Escherichia and Shigella species, 69.21% for Shigella and Salmonella and 53.31% for Salmonella and Yersinia. As a result of this property (high correlation in the data), not only the motif itself turns out to be conserved, but also its local neighborhood. Moreover, the degree of conservation between the aligned sequences in a biologically relevant alignment will reflect, in most cases, the phylogenetic relatedness of the species from which the sequences are derived (see Figure 2 for examples). By selecting the most promising alignment seeds (based on the appropriate heuristics for the scores) and constructing a local alignment with these seeds, we could also evaluate the local neighborhood of the seed. If this one seemed to be conserved as well, we could be more confident in the obtained alignment and in the motifs contained within the conserved parts. Therefore, the use of local alignments allows a better judgment on the reliability of the motifs.

Second, Gibbs sampling is a stochastic procedure. The algorithm has to be run repeatedly on the same dataset, each time generating potentially different motifs. As a consequence, the output of a motif-detection approach can be simultaneously redundant and non-exhaustive: some statistically strong motifs are detected repeatedly in different runs. On the other hand, some motifs might never be detected. Indeed, because Gibbs sampling was originally designed for unrelated sequences and because of the high correlation in the data, the number of possible equally scoring motifs (local optima) might be so high that many runs have to be performed before all motifs have been covered. All these local optima coincide with motifs that, when used as seeds, will result in a similar alignment. The same alignment can thus be obtained by several motifs, but there is no guarantee that all possible motifs that result in the same alignment will be detected by Gibbs sampling. Therefore, an alignment is a better summary of the degree of conservation between the intergenic regions than a listing of the highest-scoring motifs.

Moreover, regulatory systems such as PmrAB might have acquired some very species-specific targets. For such highly specialized regulatory systems, motifs are likely to be present in the intergenic sequences of a selected subset of orthologs only. Because such motifs occur in a restricted number of sequences of the dataset, they will not necessarily correspond to the highest-scoring motifs. Thus, they might be overlooked when selecting on high-scoring motifs by, for instance, setting a threshold on the score. Once a reliable local alignment of a set of intergenic sequences is obtained, one can judge the degree of confidence to put on the prediction of the motif of interest not only by checking in which subset of species the motif is conserved, but also by taking into consideration other factors, such as the functional annotation of the putative target. The motifs that we select on the basis of our heuristic will result in a biologically relevant alignment that includes the maximal number of species. As such, our heuristic tries to overcome the fact that Gibbs sampling is intrinsically unable to cope with correlated data. Note that the motif of interest (PmrA motif) does not necessarily have to correspond to the motif used to produce the alignment.

We showed that our in silico phylogenetic footprinting approach can be used to confirm targets detected by genome-wide screening. So far, it can only be used for species that show a high degree of conservation in their intergenic regions, similar to the conservation observed in this study. As more complete genomic data become available, the approach might be extended to other species.

As suggested previously [34], the high observed similarity in intergenic sequences might be due to the small phylogenetic distance between the species we analyzed. However, it cannot be excluded that because of the small size of the intergenic regions in bacteria and the very similar habitat and mechanism of regulation among the γ-proteobacterial species used in this study, a large part of the complete intergenic region is functional and therefore conserved. This hypothesis was also put forward by Rajewsky et al. [41]. The alignment of the intergenic region of the well-characterized ugd indeed points in that direction. Large parts of the conserved regions of the alignments correspond to experimentally verified motifs.

Remarkably, most potential PmrAB-regulated genes exhibited a footprint of the PmrA motif in E. coli only, and several target genes had no counterpart at all in organisms other than Salmonella species. This indicates a high degree of specialization of the PmrAB two-component system in Salmonella. Such specialization could also explain the considerable differences between PmrAB-dependent regulons in related species. For instance, in both Y. pestis and S. typhimurium the attenuated virulence of phoP mutants is ascribed to a defect in LPS modification, a process shown to be PmrAB-dependent [42]. So far, two S. typhimurium loci have been postulated to be involved in this LPS remodeling: pmrHFIJKLM and ugd. Only for pmrH did we detect an ortholog in Y. pestis and a conserved footprint of the PmrA motif in the promoter region of this ortholog. The Ugd protein does not even have a functional counterpart in Y. pestis. This low similarity in PmrAB regulon composition indicates that a different network of genes must be responsible for a similar phenotype in distinct species. This is not completely unexpected in view of the very different LPS composition of Salmonella and Y. pestis [42].

For most of the known experimentally verified targets, clear phylogenetic footprints of the PmrA motif could be detected in the intergenic regions of close homologs. In the intergenic region of pmrD we could recover the consensus sequence only partially (that is, one half-site) because the second half-site overlaps with the coding region (data not shown) and this was not included in the current analysis. Another PhoPQ-dependent gene that contributes to resistance to antimicrobial peptides is mig-14 [10]. However, we could not find the presence of a clear PmrA consensus in the promoter of mig-14. Neither could we detect a PmrA motif in dgoA, which was previously shown to be regulated by PmrAB [22]. This would indicate that both targets are only indirectly dependent on PmrAB. It can, however, not be excluded that they represent false negatives of our screening.

As well as the known targets, several putative new predictions could be made. Some of these predictions are consistent with previously published observations. Indeed, the PmrAB system is part of a complex regulatory cascade acting downstream of the pleiotropic PhoPQ system. The PhoPQ regulon is responsible for intracellular survival of bacteria and genes dependent on PhoPQ are induced in bacteria inside macrophages. Part of the PhoPQ regulon has been discovered to be dependent only indirectly on PhoPQ, via PmrAB. This PmrAB-dependent subset is known to confer resistance to cationic peptides by encoding genes involved in LPS modification and genes contributing to resistance to raised Fe³⁺ concentration. Genes encoding proteins involved in modification of membrane components and outer-membrane proteins are therefore sensible additional putative PmrAB targets. Another target worth mentioning in view of the Fe³⁺-sensitivity of the PmrAB system is yncD, which encodes a putative outer-membrane receptor for iron transport.

Phage remnants, such as mig-3, have been described as macrophage inducible, PhoPQ-dependent genes [40], and thus can be PmrAB-dependent. This might explain the PmrA motif in the intergenic region between STM1868A and mig-3. Detweiler et al. showed that two genes, virK and somA, both coexpressed with the SPI-2 system, confer resistance to cationic peptides and their expression is PhoPQ-dependent. Also, four fimbrial operons had genes that were coexpressed with SPI-2 [43]. Predictions of the PmrAB-dependence of sseJ, which encodes a secreted effector of the SPI-2 system, or of genes that are involved in fimbrial synthesis (such as in lpfA, encoding the S. typhimurium long polar fimbria A precursor), could therefore be in agreement with these findings. Recently, the study of Kim et al. related PmrAB-dependent regulation to swarming motility functions in S. typhimurium [44]. This could explain why we detected a putative PmrA motif in the intergenic region of flhC, which encodes a master transcriptional activator of flagellar genes.

To confirm the predictive power of our methodology further, four putative PmrAB targets were validated biologically. Expression analysis clearly demonstrated PmrAB-dependence of yibD, which confirms the recent observations of Tamayo et al. [22]. The observed PmrA-dependence of mig-13 accords with its being upregulated in macrophages in in vivo conditions [40]. The clear PmrAB-dependence of aroQ, which encodes a periplasmic chorismate mutase, is striking. In general, chorismate mutases are involved in the synthesis of tyrosine and phenylalanine and are key to the synthesis of a plethora of secondary metabolites [45]. The function of periplasmic chorismate mutases, which differ from the cytoplasmic chorismate mutases in their long carboxy-terminal extension [46], is still unclear. Periplasmic AroQ proteins have also been detected in Y. pestis, Pseudomonas aeruginosa, Mycobacterium species, Erwinia herbicola and in the phytoparasitic nematode Meloidogyne javanica [46]. Strikingly, all these organisms containing AroQ interact with a eukaryotic host. This observation, together with the fact that AroQ is dependent on the key virulence regulator PmrAB in S. typhimurium, suggests that the as-yet-unknown function of AroQ might be involved in bacteria-host interactions.

Despite its low expression level in our in vitro conditions, the sseJ fusion showed a clear PmrAB-dependent induction by Fe³⁺ excess. SseJ is a secreted effector protein that is translocated across the membrane of the Salmonella-containing vacuole (SCV) by SPI-2. From recent evidence it was speculated that the putative acyltransferase activity of SseJ would be involved in modifying the lipid composition of the SCV [47, 48], thereby interfering with the trafficking and maturation properties of the SCV in infected cells. The PmrA-dependence of sseJ would therefore link expression of genes involved in bacterial LPS modification with those involved in regulating the lipid composition of the SCV membrane.

Further experimental analysis will shed light on how these previously undescribed PmrAB-dependent proteins, with unknown functions, relate to the known part of the PmrAB-dependent regulon.

The extent to which each of the tested strains reacted to the signals Fe³⁺ or/and Mg²⁺ varied considerably. This is not surprising in view of the complex regulatory system that integrates both these signals. Indeed, both signals are transduced via the PhoPQ, PmrD, PmrAB multicomponent system, which includes a posttranslational signal transduction and a transcriptional feedback loop [1]. Depending on the affinities between the interacting components of such dynamic systems, small changes in the initial concentrations of the components might result in large differences in the observed expression levels [49]. A more detailed study of the dynamics of this system might reveal how such systems can integrate signals so differently.

Site-directed mutagenesis of the PmrA box in the yibD promoter indicated a crucial role for the T at position 3 and the A at position 5 of the first half-site of the motif. As can be deduced from the consensus site in Figure 4, no degeneracy is allowed at positions 3 and 5. This observation allows us to extrapolate to a certain extent the sequence requirements of the PmrA box in yibD to other PmrAB targets. Some positions seem essential, whereas it appears that the specific choice of nucleotide at the other positions affects the level of induction. By altering the nucleotides, the binding affinities of the regulatory protein to the box can be modified, allowing specific fine-tuning of gene expression in a cell.

We conclusively demonstrate that our in silico approach reliably identifies additional PmrAB-dependent targets. Although false positives will still be present among these predicted targets, the method offers an interesting approach for further elucidation of genetic networks involved in the expression of S. typhimurium virulence genes. We predicted the PmrAB-dependent regulation of four additional targets: yibD, aroQ, mig-13 and sseJ. Our approach might become extendable to other species when more genome sequences become available.