Limited functional conservation of a global regulator among related bacterial genera: Lrp in Escherichia, Proteus and Vibrio
© Lintner et al; licensee BioMed Central Ltd. 2008
Received: 16 November 2007
Accepted: 11 April 2008
Published: 11 April 2008
Bacterial genome sequences are being determined rapidly, but few species are physiologically well characterized. Predicting regulation from genome sequences usually involves extrapolation from better-studied bacteria, using the hypothesis that a conserved regulator, conserved target gene, and predicted regulator-binding site in the target promoter imply conserved regulation between the two species. However many compared organisms are ecologically and physiologically diverse, and the limits of extrapolation have not been well tested. In E. coli K-12 the leucine-responsive regulatory protein (Lrp) affects expression of ~400 genes. Proteus mirabilis and Vibrio cholerae have highly-conserved lrp orthologs (98% and 92% identity to E. coli lrp). The functional equivalence of Lrp from these related species was assessed.
Heterologous Lrp regulated gltB, livK and lrp transcriptional fusions in an E. coli background in the same general way as the native Lrp, though with significant differences in extent. Microarray analysis of these strains revealed that the heterologous Lrp proteins significantly influence only about half of the genes affected by native Lrp. In P. mirabilis, heterologous Lrp restored swarming, though with some pattern differences. P. mirabilis produced substantially more Lrp than E. coli or V. cholerae under some conditions. Lrp regulation of target gene orthologs differed among the three native hosts. Strikingly, while Lrp negatively regulates its own gene in E. coli, and was shown to do so even more strongly in P. mirabilis, Lrp appears to activate its own gene in V. cholerae.
The overall similarity of regulatory effects of the Lrp orthologs supports the use of extrapolation between related strains for general purposes. However this study also revealed intrinsic differences even between orthologous regulators sharing >90% overall identity, and 100% identity for the DNA-binding helix-turn-helix motif, as well as differences in the amounts of those regulators. These results suggest that predicting regulation of specific target genes based on genome sequence comparisons alone should be done on a conservative basis.
Microbial genome sequences are being determined with increasing frequency and speed. Nearly 500 bacterial genomes have been fully sequenced, and nearly 2000 more such projects are underway , with several current and planned large-scale metagenomic projects adding to the gathering avalanche of data [2–10]. A major motivation for this sequencing avalanche is the possibility of learning about a bacterium's physiology or pathogenesis, without resorting to either labor-intensive classical analyses or the still-expensive tools of systems biology. Increasingly effective methods are available to generate a "parts list" of genes and pathways from genome sequences of poorly-characterized bacteria [11–14]. However understanding the physiology of an organism, in terms of gene regulatory mechanisms and network connections, is currently much more difficult to achieve from sequence analysis alone.
Considerable research is focused on inferring gene regulatory networks from microarray analyses, following genetic or environmental disturbances [15–19]. However some microbes, for which the genome sequence is available from metagenomic studies, cannot even be grown in the laboratory. Particularly for poorly understood bacteria, it is unclear which experimental disturbances would be most physiologically relevant and would meaningfully probe the regulatory architecture .
Predicting gene regulatory networks from genome sequences alone is difficult, but can yield useful hypotheses about the conservation and evolution of regulatory networks [21–24]. Such prediction is typically accomplished by extrapolating from a well-characterized reference organism such as E. coli, if three criteria can be satisfied. The first two criteria are whether valid orthologs for a target (regulated) gene and its regulator (in the reference organism) are present in both organisms. Though it is unclear exactly how similar orthologous sequences must be for functional and regulatory predictions to have a sufficiently high probability of being accurate [25–29], these determinations are relatively straightforward from a computational perspective.
The third criterion, identifying a putative binding site for the regulator upstream of the orthologous target gene, is more complex [30–37]. Among other problems, many regulators have degenerate binding motifs and commonly-used approaches have limited sensitivity and specificity [38, 39]. In addition, the relative strength of a binding site can be as important to the resultant regulatory pattern as the site's existence , but binding strength is difficult to predict from sequence alone [41, 42], especially where the structure of the regulator is unknown [43, 44]. These difficulties have led some bioinformatic analyses to focus on the regulator-target gene connection alone, without attempting to predict the sign or strength of the interaction, and such approaches can provide useful information, if limited from the perspective of predicting or modeling cell physiology.
An even more basic issue is how similar two bacterial species have to be for the underlying hypothesis, which for brevity we refer to as the "regulatory extrapolation hypothesis," to be usefully applied. Even if orthologous genes and binding sites could be identified unambiguously, to what extent do a matching regulator, target gene and binding site correctly predict regulation? There are several potential concerns that, while not ruling out extrapolation, could place limits on its applicability. First, regarding the orthologous regulators, extrapolation implicitly assumes that they are similar in both DNA binding and in response to coregulators (if any). Second, it is implicitly assumed that the amounts of both regulator and coregulator vary in similar ways in the two organisms. Third, regarding the promoter regions for the orthologous target genes, regulatory patterns are flexible [45–47] and can be profoundly changed by limited mutation. For example, a single nucleotide change in the soxS promoter results in repression by SoxR, which normally activates, regardless of the redox signal ; and just 1–2 amino acid replacements in a regulatory protein can alter the range of coactivators or change the effect of an inducer to that of a corepressor [49, 50]. Fourth, some regulatory extrapolations involve species that are, ecologically at least, quite different from one another, and (not surprisingly) the environment to which an organism is adapted affects its regulatory architecture . Developing more robust computational approaches requires a fuller experimentally-based understanding of the extent to which regulatory architecture is conserved among related bacteria adapted to different environments.
The aim of this study is to assess conservation of regulatory architecture by studying a model regulator, and the network of target genes it controls (its "regulon"), in three related bacteria with fully-sequenced genomes. E. coli K-12 is the well-studied reference organism [52, 53]. Proteus mirabilis is, like E. coli, among the Enterobacteriaceae, but is a relatively distant member of that family . Vibrio cholerae is a member of a different family within the γ-Proteobacteria – the Vibrionaceae . These organisms share some basic properties. All three grow on mucosal epithelia, and all three are capable of differentiating into elongated, hyperflagellated swarmer cells that spread across solid surfaces [56–60]. E. coli, however, is adapted to growth in the mammalian or avian intestine, while P. mirabilis is a urinary tract pathogen, and V. cholerae is primarily a marine microbe that is an opportunistic pathogen of the human ileum .
The Lrp regulon is a good model for comparison among species for at least three reasons. First, the regulon is large and includes genes having a range of functions (including biosynthesis, catabolism, transport and virulence). Second, Lrp can generate diverse regulatory patterns that include both activation and repression, and differing sensitivities to the coregulators L-leucine and L-alanine [73, 74]. Third, while Lrp is abundant (2,500 molecules/cell) compared to many other transcription factors, it is present at much lower concentrations than the major nucleoid-structuring proteins Fis (60,000 molecules/cell), HU (30,000–55,000) and H-NS (20,000; all during exponential growth) . Despite the ability of Lrp to bind DNA semispecifically , expression of the great majority of genes is unaffected by deletion of lrp (see Fig. 1A in  and Fig. 3 in ), so its generalized effects as a histonelike protein are limited.
To compare the architecture of the Lrp regulons of E. coli, P. mirabilis, and V. cholerae, we began by addressing four general questions. First, are the effects of lrp disruption on complex phenotypes such as growth and swarming comparable in the three species? Second, are the lrp genes functionally interchangeable in complementation assays? Third, do the lrp genes themselves have the same expression pattern in the different species? Fourth, are the orthologs of what, in E. coli K-12, are Lrp-controlled genes regulated by Lrp in the same manner in the two other species? The goal of this study was not to determine the molecular bases for observed differences, but rather to assess the frequency of such differences.
Lrp is highly conserved among Enterobacteriaceae and Vibrionaceae
It would be extremely useful if transcriptional regulatory architecture of bacteria could be predicted from the sequence of their DNA. Attempts to do so generally involve extrapolation from well-studied species such as E. coli, in cases where the regulators and target genes are orthologous and a binding site is conserved in the target promoter. However, such extrapolation relies on several implicit assumptions (see Introduction) that have not been well tested experimentally. We used the Lrp regulon as a model to carry out tests of these assumptions.
The pronounced conservation among Lrp orthologs in enteric bacteria was first noted over a decade ago , and the large number of subsequently-determined genome sequences has not altered that pattern. The Lrp ortholog in P. mirabilis differs from that in E. coli by only 4/164 amino acids (98% identity), while Lrp from V. cholerae shows 92% identity to E. coli Lrp. Importantly, none of the changes observed in P. mirabilis and V. cholerae occur in the helix-turn-helix motif responsible for DNA sequence recognition (Fig. 1, cartoon representation and boxed region), defined via mutation of the E. coli lrp gene and x-ray crystallography of an archaeal ortholog [78, 79] and recently of the E. coli protein itself . Similarly, the Lrp orthologs of these bacteria are completely conserved for amino acids implicated in coregulator recognition (boxed amino acids in lower panel of Fig. 1).
Differences in growth phenotypes of lrpnull strains
The first test of functional conservation is that if Lrp is having similar broad effects on gene expression in E. coli, P. mirabilis, and V. cholerae, then one would expect to see similar effects of a lrp null mutation on their growth. Fig. 2A shows the results of growth experiments for wild-type (WT) and lrp strains of these three species grown in MOPS glucose minimally supplemented medium ("MOPS glucose") or MOPS glucose defined rich medium ("MOPS rich") [both media including nicotinate as required by the P. mirabilis strains, and pantothenate and thiamine as required by the P. mirabilis Δlrp strain (see Methods)]. The plot shows the WT specific growth rate on the x-axis, and the rate for the lrp strain on the y-axis; thus where lrp mutation has no effect on growth rate the points fall on the diagonal line. In MOPS rich medium (closed symbols), lrp mutation had little effect on growth of any of the three species. However in MOPS glucose medium, P. mirabilis stands out as having a substantial growth rate decrease when lrp is mutated (193 min vs. 66 min doubling time for the WT strain). This might represent a lrp-dependent partial auxotrophy, in addition to the lrp-dependent requirements for pantothenate and thiamine that were satisfied by the medium. However that may be, it is clear that the lrp mutation has differential effects in these three species.
We cloned the lrp genes from P. mirabilis and V. cholerae downstream of PlacUV5 in the low-copy vector pCC1 (Epicentre); a consensus E. coli Shine-Dalgarno sequence  was introduced as well. We also cloned the lrp gene from E. coli O157:H7 (which is identical to that of E. coli K-12 at the amino acid level). Thus the three lrp alleles had identical expression sequences. The effect of the lrp allele on P. mirabilis growth in minimal medium was fully complemented by supplying the cloned P. mirabilis lrp gene on a plasmid, and was mostly but incompletely complemented by the lrp genes from E. coli and V. cholerae (Fig. 2B).
Effects of orthologous lrpalleles on swarming behavior in Proteus
Regulation of E. coli target genes by Lrp proteins from P. mirabilis and V. cholerae
To begin testing the functional equivalency of the different Lrp orthologs, we co-transformed strain BE10.2 (ΔlacZ, lrp-Tn10) with a vector containing an E. coli Plrp-lacZ fusion and the various lrp-bearing plasmids (or vector control). Lrp directly represses its own promoter in E. coli, and this occurs whether or not leucine is present . Strains were grown in MOPS glucose, and the specific activity of β-galactosidase was determined and plotted against culture density to more quantitatively assess its level and to assure that the cultures were in balanced growth. Compared to the vector control, V. cholerae Lrp repressed Plrp-lacZ to the same extent as E. coli Lrp (~2 fold), while P. mirabilis Lrp (98% identical to E. coli Lrp) repressed about twice that much (Fig 4B).
Transcriptional activation is generally a more demanding process than repression, in the sense that the activator has not only to bind the DNA correctly but also (in most cases) to make productive contacts with RNA polymerase [90, 91]. Strain BE3780 contains a chromosomal operon fusion to the gene for glutamate synthase (gltB-lacZ) in the E. coli BE10 background (ΔlacZ, lrp-Tn10) . Lrp directly and strongly activates gltBD transcription in E. coli [74, 92], in a process that also requires the global regulator IHF  and is antagonized by Crp and ArgR . We found that, relative to the vector control, activation of gltB transcription by P. mirabilis or V. cholerae Lrp was essentially indistinguishable from that of the E. coli lrp positive control (Fig. 4C).
Leucine responsiveness of heterologous Lrp proteins
Conserved function among regulators depends not only on DNA-binding (and RNA poymerase-contacting) properties, but in some cases also on responses to small molecule coregulators. This provides the basis for a third test of assumptions in regulatory extrapolation. Lrp transduces metabolic signals in the form of amino acid pool levels, in particular the amino acids L-leucine and L-alanine . The livKHGMF operon is one of two high-affinity branched chain amino acid transport systems in E. coli . The livK gene is repressed by Lrp when exogenous leucine is present [70, 96], and activated when leucine is not in the medium . Thus livK is a particularly sensitive indicator of the responses of Lrp orthologs to leucine. The amino acid residues previously demonstrated to be involved in leucine binding in E. coli Lrp [64, 97] are completely conserved in the P. mirabilis and V. cholerae orthologs (boxes in lower half of Fig. 1).
Bacterial strains and plasmids used.
Escherichia coli W3110
E. coli BE10.2
W3110 Δlac-169 lrp35::Tn10
E. coli BE3780
lrp::Tn10, Δlac-169, gltB(psiQ35)::lacZ
E. coli EPI300
F- mcrA Δ (mrr-hsdRMS-mcrBC) Φ80d lacZΔM15 ΔlacX74 recA1 endA1 araD139 Δ (ara, leu)7697 galU galK λ- rpsL nupG trfA dhfr
F. C. Neidhardt
F-lambda-ilvG-rfb-50 rph-1 Δlrp::kan
Proteus mirabilis HI4320
P. mirabilis U6450
P. mirabilis U6450Δlrp
Vibrio cholerae El tor A1552
V. cholerae El tor A1552Δlrp
lrp-cat derivative of strain A1552
Low copy blunt cloning vector
pCC1 backbone with lrp gene from E. coli O157:H7 inserted at BamHI site
pCC1 backbone with lrp gene from P. mirabilis HI4320 inserted at BamHI site
pCC1 backbone with lrp gene from V. cholerae El tor A1552 inserted at BamHI
pCC1 backbone with Kan cassette cloned into BamHI site; used as vector control.
pACYC backbone with livK-lacZ transcriptional fusion. CAT gene was inactivated by digesting with NcoI, in-fill with Klenow fragment and religated to create a frame shift
pECLRP with CAT gene deleted and KAN cassette inserted at BsmI
pPMLRP with CAT gene deleted and KAN cassette inserted at BsmI
pVCLRP with CAT gene deleted and KAN cassette inserted at BsmI
pVEC with CAT gene deleted and KAN cassette inserted at BsmI
pBH403 backbone with E. coli O157 gltB promoter cloned at BamHI to SalI
pBH403 backbone with Proteus mirabilis gltB promoter cloned at BamHI to SalI
pBH403 backbone with Vibrio cholerae Vc2376gltB-2 promoter cloned at BamHI to SalI
pBH403 backbone with Vibrio cholerae Vc2373gltB-1 promoter cloned at BamHI to SalI
pBH403 backbone with E. coli W3110 lrp promoter region cloned at BamHI to SalI
pBH403 backbone with Proteus mirabilis lrp promoter cloned at BamHI to SalI
pBH403 with Vibrio cholerae lrp promoter cloned at BamHI to SalI
pPM3001 CAT gene was inactivated by digesting with with NcoI, in-fill with Klenow and religated to create a frame shift
In general the Lrp proteins from P. mirabilis and V. cholerae yielded livK regulatory patterns similar to that of the E. coli control, showing activation in the absence of leucine and repression in its presence (Fig 4D and 4G). However both P. mirabilis and V. cholerae Lrp gave threefold greater repression than E. coli Lrp (Fig 4G). We also looked at the effects of adding ILV on the regulation of E. coli Plrp and PgltB. Addition of ILV interfered to a moderate extent with the repression of Plrp by all three Lrp orthologs (Fig 4E), but the differences observed in the MOPS glucose cultures were maintained, with substantially greater repression by P. mirabilis Lrp. Also as expected from previous studies [74, 92], activation of PgltB by E. coli Lrp was moderately reduced in the presence of ILV; the two heterologous Lrp orthologs gave patterns essentially indistinguishable from that of E. coli Lrp (Fig 4F).
Microarray analysis of gene regulation by heterologous Lrp proteins – effects on regulon membership
The functional conservation of Lrp orthologs was more broadly assessed by using microarrays to analyze gene regulation, when lrp alleles from E. coli, P. mirabilis, or V. cholerae (all fused to the same expression sequences) were used to complement the lrp null mutation in E. coli K-12. In these experiments, the only variable is the Lrp itself – the target genes and promoter binding sites are identical. The data for all significantly-affected genes are available [see Additional file 2].
A potential concern with these experiments is that some apparent differences between the gene sets responsive to the three Lrp orthologs might be artifactual. For example, if the typical significantly-affected gene varies twofold, but this change is only counted as being significant in half of the genes that truly change twofold, then 1/4 of genes would appear to be unaffected by a Lrp ortholog, even if there were no real functional differences between the Lrp proteins. We addressed this concern in two ways.
First, the NULL hypothesis for observed overlaps under our experimental design is that two different Lrp proteins should produce identical (100% overlapping) sets of differentially-expressed genes. We are not aware of any distribution test statistic that can be used to evaluate this hypothesis (if the NULL hypothesis had been that two sets are random, then we could have used either hypergeometric distribution or Fisher's exact test). Instead we performed a bootstrap procedure to enumerate all possible outcomes of overlaps, given the data. If our actual NULL hypothesis were true, two complementation comparisons (e.g. E. coli Lrp vs. V. cholerae Lrp) would be indistinguishable. To test this, we generated a NULL distribution of overlaps by randomly exchanging columns of ratio values between two sets.
Statistical analysis of permuted microarray data
Size of overlap
# of overlaps with smaller size
Eco and Pmi
Eco and Vch
Pmi and Vch
Second, we used intra-set comparisons to qualitatively evaluate the observed overlaps. We compared the fractions of genes, differentially expressed by both Lrps in a pairwise comparison, at different ratio cut-offs. We also compared lrp vs. WT (chromosomal lrp+), and lrp(ParaBAD-LrpK12) vs. lrp. This allowed us to compare the overlaps between LrpK12 on two plasmids and between a plasmid and the WT chromosomal gene. The overlap fraction was calculated as the number of regulated genes in both of two sets, divided by the total number of unique genes in both sets. Fig. 6B illustrates that, as expected, the overlaps are consistently greater between identical complementation sets than between heterologous sets. Thus the microarray results reveal statistically significant functional differences between the Lrp orthologs.
Microarray analysis of gene regulation by heterologous Lrp proteins – effects on magnitude of regulation
We used the slope of a least-squares fit between two ratios as a measure of overall regulatory concordance (Fig. 7). One ratio is the expression level of genes in the E. coli Δlrp mutant over the level in that strain complemented by a plasmid carrying the E. coli lrp gene (on the x-axis in all panels). The second ratio (y-axis) is the expression in E. coli Δlrp over that in the same strain complemented by a test plasmid (vector control, or lrp from P. mirabilis or V. cholerae). Full complementation relative to that by pEcoLrp would yield a slope of 1.0 for the linear fit. For each column, the top row indicates the effects of "complementing" the Δlrp allele with the vector alone (pCC1). Not surprisingly, the negative control of "complementing" the lrp mutation with the vector gives slopes of <0.2 for all three gene sets. As a positive control, we carried out a similar analysis comparing on the one hand the E. coli lrp mutant to the mutant complemented with plasmid-borne E. coli lrp [i.e., lrp/lrp(pLrpK12)], and on the other hand the mutant to the WT lrp+ strain (lrp/WT). The resulting slope between these datasets was within error of 1.0 (0.94 ± 0.12; not shown), indicating that native Lrp – even with the heterologous promoter and translation initiator – fully complements the effect of the chromosomal lrp mutation under the growth conditions used.
Supplying the lrp allele from P. mirabilis (pPmiLrp, bottom row) gave substantial, though not full, complementation of the gene expression pattern, with slopes ranging from 0.81 to 0.93. The lrp allele from V. cholerae (pVchLrp), which is more divergent from that of E. coli than is that of P. mirabilis (Fig. 1), yielded lower overall regulatory complementation – the slopes range from 0.49 to 0.69. Thus small changes in the Lrp sequence, outside of the fully-conserved helix-turn-helix motif, have substantial effects on the magnitude of Lrp-dependent effects on gene expression.
We find that, if anything, this analysis underestimates the differences between effects of the different Lrp orthologs. Since the power of inference cannot be adequately estimated in microarray experiments, we fixed the false discovery rate (FDR) at 5% without setting a minimum limit on fold change. However when we added a fold-change limit to the fixed FDR the differences between regression slopes were increased (not shown). In these experiments, genes were assigned to columns A or B (Fig. 7) based on having significant responses (positive or negative) to E. coli Lrp. The measurements on which this selection was based include noise, so even without a real difference in Lrp function the data might regress back towards no change in the complementation experiments. However, since the correlations used only genes that were significantly affected in the same direction in both sets in each pair, no systematic degradation of the signal is expected.
We evaluated the significance of differences between the slopes in Fig. 7 as follows. To avoid making assumptions about the nature of the distribution under the NULL hypothesis, we bootstrapped the slopes and estimated 95% confidence bands for each slope. The coordinates of each point in the correlations are estimates of corresponding ratios of transcript abundances, obtained as means of ratios observed in independent biological experiments. Therefore, we estimated the width of slopes of the regression lines by permuting the series, choosing N points (corresponding to the number of genes used in the correlation) at random from one of the three ratios. For example, for gene 1 we can take the ratio from biological replicate 1, for gene 2 from replicate 3, etc. N-member series were permuted in this way 1000 times, and the spread of 95% confidence intervals around the mean was calculated relative to the regression coefficients from Fig. 7. We demonstrated that 95% confidence intervals do not overlap between regression lines of interest. This supports the conclusion that differences between Lrp orthologs can explain the different amounts of variance observed in the various complemented strains.
Levels of Lrp protein in E. coli, P. mirabilis, and V. choleraein different media and growth phases
If orthologous regulatory proteins are to generate the same expression patterns for orthologous target genes, having the same intrinsic properties (DNA binding specificity and equivalent interactions with small molecules and with other proteins) would be neccesary but not sufficient. The orthologs would also have to share extrinsic properties, including accumulation to similar levels under various growth conditions. This provides the basis for a fourth test of implicit assumptions underlying regulatory extrapolation. Potential differences in Lrp levels were minimized, in the complementation experiments described above, by providing each lrp ortholog with a common promoter and translation initiation region. However E. coli growing exponentially in a minimal glucose medium accumulates three- to four-fold more Lrp than in rich medium [85, 86]. To determine whether this pattern of Lrp accumulation is conserved, we used western blot analysis to measure the levels of Lrp throughout a batch growth cycle in E. coli, P. mirabilis and V. cholerae grown in MOPS glucose and MOPS defined rich media (supplemented as described in Methods).
These differences between E. coli and P. mirabilis, in Lrp protein levels, could well have substantial regulatory significance but needed confirmation. Samples were taken from parallel cultures of E. coli and P. mirabilis during early logarithmic, mid logarithmic, late logarithmic and stationary phases in defined rich medium. Equal amounts of total protein were resolved side-by-side via SDS PAGE. The results of the subsequent western blot (Fig. 8M) confirm that P. mirabilis produces substantially more Lrp protein throughout the growth phases, with the greatest difference (roughly twofold) seen in stationary phase. Thus Lrp provides an example of related bacteria with nearly-identical regulator proteins producing significantly different amounts of those regulators.
Levels of lrp mRNA in E. coli, P. mirabilis, and V. choleraein different media and growth phases
We also measured the levels of lrp mRNA for all three organisms during log-phase growth, using a more highly-quantitative method, and the results are consistent with the protein data (Fig. 9B–D). We used a sensitive dilution-response approach that makes use of the fact that our three species-specific pairs of QRT-PCR primers for lrp amplify with the same efficiency but are completely specific for their respective template DNAs (data not shown). A standard amount of reference E. coli RNA (from a mid-log phase culture in MOPS glucose plus nicotinate) was mixed with varying amounts of test RNA (from cultures grown in either the MOPS glucose or MOPS rich medium). The various mixes were reverse transcribed and the resultant cDNA was used as template for simultaneous amplification with the three primer pairs, with real-time fluorescence monitoring (see Methods). Where the proportion of total mRNA as lrp mRNA is equal to that in the reference sample, the slope should be 1.0 (dotted lines in Fig. 9B–D). For E. coli-derived cDNA, the resulting slope is about 0.75 (MOPS glucose culture; Fig. 9B) or 0.4 (MOPS rich culture), indicating lower lrp mRNA levels in rich than in minimal medium and consistent with the medium-dependent effect on Lrp protein levels shown in Fig. 8. V. cholerae (Fig. 9D) gave a pattern similar to that of E. coli, though with less of a medium-dependent effect. However – also consistent with the protein data – P. mirabilis had substantially more lrp cDNA as a proportion of total cDNA than did E. coli, with slopes of about 2 (Fig. 9C).
Regulation of orthologous target genes in their native backgrounds
Wild-type and lrp strain pairs of E. coli, P. mirabilis, and V. cholerae were grown in MOPS glucose defined rich medium. Samples were taken in early logarithmic phase (OD600 nm = 0.3), and early stationary phase (1 h after the culture OD vs. time semilogarithmic plot diverged from linearity). Real-time RT-PCR analysis was used to determine the relative levels of adhE, gltB, and (as a control) recA mRNAs. The experiment was performed in triplicate and the relative levels of mRNA were determined using the standard curve method  and by normalizing to recA. There is no effect of Lrp on recA expression, at least in E. coli and V. cholerae under our conditions  and N. Dolganov, pers. commun.).
Regulation of adhE
AdhE is a fused acetaldehyde-CoA dehydrogenase, iron-dependent alcohol dehydrogenase and pyruvate-formate lyase deactivase [104–106]. In E. coli, adhE is preferentially expressed in stationary phase [107–109], and repressed by Lrp in a leucine-independent manner during exponential growth in minimal glucose medium  and ABK, unpublished data). Fig. 10D is a log-scale correlogram showing the regulatory pattern of adhE in all three organisms. If Lrp had no effect on adhE mRNA levels, then the points would fall on the diagonal line. The fact that all points are above the diagonal line is consistent with Lrp-dependent repression in all three species. However a more detailed analysis of this data reveals that the regulatory patterns from P. mirabilis and V. cholerae are different from those in E. coli and from one another. In E. coli (circles), there was a modest Lrp-dependent decrease in adhE mRNA in log phase. P. mirabilis, in contrast (triangles), showed a strong repressive effect of Lrp, though no real growth-phase dependent change in expression. V. cholerae (squares) exhibited slight Lrp-associated reduction in log phase expression, but in stationary phase the adhE mRNA levels were about 50-fold higher in the lrp mutant strain.
Regulation of gltB
The other target gene, gltB, was described earlier. V. cholerae appears to have two tandem gltB isozyme genes, with 73% and 43% amino acid identity to E. coli GltB. The presence of all conserved domains and key residues strongly suggests that both of these genes actually specify GltB [110, 111] and M.A. Vanoni, pers. commun.). We failed to detect expression of the Vibrio gltB with higher identity to E. coli during growth in minimal glucose and defined rich medium (Vc2376, not shown), however the lower-identity isozyme (Vc2373) was expressed. In E. coli gltB is activated 30–40 fold by Lrp when grown in MOPS glucose [67, 69, 74, 92], with the activation codependent on another global regulator, IHF [93, 94]. We have already shown that the Lrp orthologs from P. mirabilis and V. cholerae effectively replace E. coli Lrp, in an E. coli background, for activation of E. coli PgltB (Fig. 4, panels C and F). Here we determine whether the Proteus and Vibrio Lrp orthologs each activate their native gltB promoters in the native background. The P. mirabilis lrp strain did not grow well in the MOPS glucose medium used in this study, so all experiments were carried out in MOPS rich medium. In another rich medium (LB), activation of gltB by Lrp is reduced relative to minimal glucose, but is still about triple the level in a lrp disruptant .
We found that in E. coli gltB is activated ~25 fold by Lrp during mid-log, and about half as much in early stationary phase (Fig. 10E, circles). In P. mirabilis there was several-fold more log-phase gltB expression in the lrp+ than in the lrp strain, with little if any growth-phase-dependent change. V. cholerae gave the most divergent expression pattern: gltB mRNA levels were halved by Lrp in log phase, but increased about fivefold by Lrp in early stationary phase. Bearing in mind that this is the ortholog showing only 43% identity to E. coli gltB, it is nevertheless the case that while Lrp activates gltB in log-phase E. coli and P. mirabilis, under the same conditions it slightly represses Vc2373 in V. cholerae.
Lrp regulatory interactions with two promoter regions
Finally we tested whether promoter regions from orthologous genes, where the E. coli gene is Lrp-controlled, are regulated by Lrp in heterologous hosts. This was done by preparing lacZ operon fusions to a set of ortholog promoters cloned from E. coli, P. mirabilis, and V. cholerae, and then introducing each of these fusions into both the WT and lrp strains of all three species. Relative LacZ activity was measured using the approach shown in Fig. 4 (determining the slope of a LacZ activity vs. culture density plot). These experiments are reciprocal to those shown in Fig. 4, where heterologous lrp alleles had been moved into an E. coli background to test for control of E. coli target genes. However the two sets of experiments share the feature that the target promoters/binding sites being assessed are identical (the set of E. coli promoters in the case of Fig. 4, and the promoter for one of the three lrp orthologs in this experiment).
Regulation of Plrp
Aside from this, there is considerable variation in the level of repression. E. coli Plrp is much more strongly repressed by Lrp in the heterologous hosts than in the native host. Plrp of P. mirabilis, unlike that from the other two organisms, shows no evidence of autogenous repression by Lrp – P. mirabilis Plrp is only weakly repressed (1.3–1.4-fold) in all three hosts. This may explain the higher levels of Lrp we found in this organism (Figs. 7 and 8), and raises interesting questions about how Lrp levels are controlled in P. mirabilis. In summary, both the source of the promoter and the host background strongly affected the regulatory pattern.
Regulation of PgltB
The second promoter analyzed in this way was PgltB, including the promoters from both V. cholerae putative isozymes. In E. coli, PgltB is activated by Lrp [74, 92–94, 112], but the results in Fig. 11 are more varied than for Plrp. E. coli PgltB was the only one to be consistently expressed and activated by Lrp (10–30-fold) in all three hosts. PgltB from P. mirabilis was barely expressed in V. cholerae irrespective of Lrp, was expressed weakly in its native host (under the conditions used) and with statistically insignificant effects of Lrp, while in E. coli this promoter was expressed at a moderate level and was activated by Lrp (5-fold). The V. cholerae PgltB that is less similar to E. coli gltB (Vc2373) was strongly expressed in its native host, with no significant effect of Lrp, while showing lower expression levels and activation (3-fold) in E. coli, and yielding low expression but repression by Lrp (3-fold) in the P. mirabilis background. The other V. cholerae PgltB (Vc2376), more closely related to E. coli gltB of the two, had given undetectible expression in our RT-PCR assays. Vc2376 gave detectable expression in the form of a lacZ fusion, though as with the RT-PCR analysis it yielded much lower expression in the native host than did Vc2373. Lrp had insignificant effects on Vc2376 in the native host and in E. coli, while in P. mirabilis there was very low expression irrespective of Lrp. Thus, as with Plrp, the promoter behavior varied substantially between hosts with respect to Lrp effects, and given hosts expressed the promoters of orthologous target genes in varied manners.
Conserved regulation of Plrp and PgltB would have led to greater similarity of expression patterns of the three (or four) orthologs in the three host species. However, the differences may reflect more than just differences in Lrp. In E. coli, PgltB is controlled by Lrp, Crp, IHF, and ArgR [67, 74, 93, 94, 112]. Species or promoter binding-site differences affecting the other regulators (besides Lrp) could contribute to the different observed behaviors – in an extreme case, Lrp has no effect on PgltB in the absence of IHF binding . A similar caveat may apply to Plrp, which in addition to being autogenously repressed by Lrp  may be activated by GadE in E. coli .
Robust methods for predicting gene regulation from DNA sequence data would greatly increase the usefulness of the rapidly-expanding collection of bacterial genome sequences. However current methods rely on a hypothesis that has received limited testing – that a well-conserved regulator, and well-conserved target gene downstream of a putative binding site for the regulator, together imply a similar pattern of regulation (or at least some direct regulation). For brevity, we refer to this as the "regulatory extrapolation hypothesis," since it involves inference of a regulatory pattern based on conservation with respect to a well-studied reference organism. Some possibilities regarding this hypothesis are: that it is generally true among closely-related organisms (genetically, ecologically, or both), that it is generally true for only the most-highly conserved regulators and target genes, or that it is often incorrect even among highly-related genes and organisms. We have studied regulatory extrapolation by examining a well-conserved global regulator (Lrp), conserved genes that are Lrp regulatory targets in E. coli, and two species of increasing but limited genetic distance from E. coli: Proteus mirabilis and Vibrio cholerae.
Closely-related Lrp proteins have significant intrinsic differences
Regulatory extrapolation relies on a tacit assumption that regulatory proteins with high amino sequence identity are functionally equivalent. We took closely-related Lrp orthologs (all >92% identity) from three species, gave them identical expression sequences for transcription and translation, put them into the same very low-copy vector (pCC1), and introduced them into the same E. coli K-12 lrp and P. mirabilis lrp backgrounds. Of the 164 aa in all three Lrp orthologs, P. mirabilis Lrp differs from E. coli Lrp at only four positions, while the V. cholerae and E. coli orthologs differ at 12; none of these differences affects the known DNA-binding helix-turn-helix or the coregulator binding sites (Fig.1).
The Lrps exhibited similar overall behavior, supporting extrapolation in general, particularly where the only concern is whether a regulatory link exists at all irrespective of its sign or strength. However there were significant functional distinctions between the tested Lrp orthologs. In E. coli, the native Plrp (fused to lacZ) was repressed equivalently by E. coli and V. cholerae Lrp, but about twice as much by P. mirabilis Lrp (Fig. 4B). These differences were magnified in the presence of leucine, where P. mirabilis Lrp was unique in showing virtually no effect (Fig. 4E). In contrast, the three Lrp orthologs gave equivalent activation of PgltB (Figs. 4C, F). PlivK, which in E. coli is activated by Lrp in the absence of leucine and repressed in its presence, was regulated equally by all three Lrp orthologs with one exception: in the presence of leucine, the E. coli Lrp represses less than the others (Fig. 4D, G).
We used microarray analysis to more globally assess the ability of orthologous Lrp proteins to properly control the E. coli K-12 Lrp regulon. Our results confirmed that minor changes in the Lrp amino acid sequence had substantial effects on the targets (Fig. 5) and magnitude (Fig. 7) of Lrp effects. Of the genes whose expression was significantly changed by E. coli Lrp, over a third were not significantly affected by either of the other Lrp orthologs. Conversely, about half of the genes significantly affected by P. mirabilis Lrp, and about a third of those affected by V. cholerae Lrp, were also affected by E. coli Lrp in the E. coli background. Looking only at the subset of genes that were significantly affected by all three Lrps, we found that whether we examined genes significantly repressed by Lrp, activated by Lrp (in both cases including both direct and indirect effects), or directly controlled by Lrp in E. coli, the results were consistent. Specifically, the transcriptional effects of P. mirabilis Lrp were most similar to those of E. coli Lrp (correlation of 80–92%), followed by V. cholerae (correlation of 67–70%), with vector alone showing no statistically significant similarity (0–20%), and the chromosomal vs. plasmid-borne E. coli lrp comparison giving 94% concordance. It is important that in these experiments, the only variable is the Lrp ortholog present – the target genes and binding sites are identical between strains (as they are all based on the same E. coli host).
Another assessment of Lrp functionality involved P. mirabilis swarming over a solid surface. Swarming is a complex phenomenon; for example, in Salmonella about a third of all genes showed swarming-associated changes in expression . For the purposes of the present study, swarming thus represents a sensitive indicator of Lrp action. In a P. mirabilis background, we found that all three Lrp orthologs restored swarming, but gave repeatable differences in the resulting swarming patterns (Fig. 3).
We are currently exploring the molecular bases for these intrinsic differences in the three Lrp orthologs. In theory, a combination of differences in DNA specificity (due to changes outside the helix-turn-helix motif), in cooperativity, in response to coregulatory amino acids, or to interactions with RNA polymerase or other regulatory proteins  could be involved. For the purposes of this report, however, the main point is simply that such differences exist even between proteins that are 98% identical (the P. mirabilis and E. coli Lrps).
Orthologous Lrp proteins can have different extrinsic properties
We also examined the native regulation of lrp in E. coli, P. mirabilis, and V. cholerae. Regulatory extrapolation relies on a second tacit assumption: that levels of the conserved regulator are similar in the organisms being compared, and change similarly in response to growth conditions. Target gene regulation is, not surprisingly, affected by the level of the regulatory protein; this is specifically true for Lrp [66, 92, 116].
Lrp protein levels in all three species were reduced in rich medium relative to glucose minimal medium (Fig. 8). For two of the three species, lrp mRNA levels are also lower in rich medium (Fig. 9). However we found that, during growth in defined rich medium (especially at higher cell densities), P. mirabilis levels of Lrp protein and lrp mRNA were about double those in E. coli or V. cholerae (Figs. 8, 9). For the purposes of this study, the important point is that Lrp levels differ significantly between the species, so that sequence analysis of the Lrp open reading frame and target gene promoter is not sufficient to predict expression patterns of the target gene. This regulatory variation is not an idiosyncracy of Lrp; for example, similar species-specific variation in regulation, among Enterobacteriaceae, has also been reported for the global regulator Fis , though as with Lrp the most basic patterns of expression are conserved .
Orthologous target genes are regulated differently by the same Lrp protein
A third tacit assumption underlying regulatory extrapolation is the reciprocal of the one described immediately above: that orthologous target genes moved into a common background will be regulated in the same way by orthologous regulators. We prepared lacZ fusions to both PgltB and Plrp promoters from E. coli, P. mirabilis, and V. cholerae, and introduced them into the lrp and lrp+ strain pairs for all three species in all combinations.
Once again the assumption is supported in general – most Plrp combinations are unaffected or repressed by Lrp (Fig. 11D), while all but one of the PgltB combinations are unaffected or activated by Lrp (Fig. 11E). [Note: repression and activation by Lrp have been demonstrated for these target genes in E. coli, but have not been proven to occur in the other backgrounds (where the effects might be indirect), and we use these terms for brevity.]
However the assumption is not supported by the specifics – e.g., the E. coli PgltB is well-expressed and Lrp-activated in all backgrounds, while the P. mirabilis PgltB ranges from nonexpression to Lrp-activated expression in the different backgrounds (Fig. 11). It is particularly interesting that, unique to the V. cholerae Plrp in the V. cholerae background, Lrp activated rather than repressing the promoter. There are some intriguing and distinctive sequence characteristics of the V. cholerae Plrp that may explain this behavior, and we are investigating these further. However V. cholerae Plrp gave low expression in the V. cholerae background even when Lrp was present, while this same promoter gave much higher (and Lrp-responsive) expression in the E. coli and P. mirabilis hosts. This, and the fact that the E. coli and P. mirabilis Plrp fusions were both well expressed in the Vibrio background, suggests that V. cholerae negatively regulates its Plrp via some Vibrio-specific factor, and that Lrp may in this case act as an anti-repressor.
Comparisons of independent microarray studies on distinct platforms are problematic [119, 120], and this report has the benefit of direct comparison using the same experimental and statistical methods. Nonetheless, our results are supported by earlier analyses of the somewhat differing effects of gene disruptions in the global regulators H-NS [121–124], IHF [125, 126], and Fis [127, 128] in the closely-related genera Escherichia and Salmonella.
One possible interpretation of these results is that global regulators are more likely to have greater recognition plasticity than local regulators, in which case a study of Lrp may represent something of a "worst case scenario" for regulatory extrapolation despite its remarkable conservation (Figs. 1, S1). It has been suggested that global regulators bind a large number of sites with a wide range of affinities, affecting chromosome superhelical density and providing a continuous "analog" regulation, in contrast to the more "digital" regulation by more specific regulators . It is also true, however, that more local and sequence-specific regulators (such as LexA) show considerable range in their in vivo DNA binding . There are certainly local regulators that bind unique sites with extreme specificity , though the value of predicting their regulatory roles across species is correspondingly limited.
Our results present a mixed picture. In general terms, we found that Lrp behaves in similar ways in the three tested species. However we also found significant intrinsic and extrinsic differences among the Lrp orthologs, and differences in the behavior of target gene promoters having predicted Lrp-binding sites, despite the fairly close genetic relatedness of the species we examined. These results suggest that regulatory extrapolation over limited genetic distances, with the goal of making fairly general predictions of regulon structures, can provide valid and useful insights. However our results also indicate that the strength and sign (positive or negative) of the regulation, even across limited genetic distances, is surprisingly variable.
Bacterial strains, media, and growth conditions
The bacterial strains used for this study are listed in Table 1. In all cases cells were grown in baffled flasks shaken at 37°C. Morpholinopropane sulfonic acid (MOPS) glucose minimal medium, and MOPS-based defined rich medium  were purchased from Teknova (Hollister, CA). In experiments comparing E. coli and V. cholerae with P. mirabilis, media for all strains were supplemented with 0.01 mM nicotinic acid, which is required for the growth of Proteus mirabilis  and of the lrp mutant of Vibrio cholerae (REL, unpublished observation). When lrp mutants were part of an experiment, minimal media also contained 0.01 mM each of pantothenate and thiamine, which we found to be additional requirements of the P. mirabilis lrp mutant, and in some cases 0.1 mM L-cysteine with 0.2 mM L-methionine which were not required but improved growth of this mutant (REL, unpublished observation). For PlivK-lacZ analyses, additional amino acids were used at the following final concentrations: 10 mM L-leucine, 0.4 mM L-isoleucine and 0.4 mM L-valine. Antibiotics were used, where indicated, as follows: 100 μg ampicillin/ml, 15 μg chloramphenicol/ml, 100 μg kanamycin/ml, and 10 μg tetracycline/ml.
The lrp alleles are as follows. For E. coli and V. cholerae, all but the first six and last six codons of the lrp ORF were replaced by the gene for chloramphenicol acetyltransferase (cat) (our unpublished result; N. Dolganov and G. Schoolnik, unpublished result), with confirmation by PCR amplification and sequencing. Some experiments made use of strains carrying an E. coli lrp::Tn10 allele (lrp-35, ). The P. mirabilis allele is a lrp::miniTn5 disruptant , provided by G. Fraser). The E. coli MG1655 lrp mutant has the entire lrp ORF replaced by the gene for kanamycin, and was constructed using λred recombinase gene replacement system . Other strain information is in Table 1.
Growth experiments and sample isolation
Overnight cultures in MOPS glucose or MOPS rich medium were inoculated from (respectively) M9 glucose or LB agar plates containing 0.01 mM nicotinic acid, and grown to early stationary phase. These cultures were then used to inoculate fresh media (1:32). OD600 nm was measured following sample dilution as needed to maintain OD within the range of 0.08–0.3.
Samples for real-time RT-PCR analysis were isolated at the indicated times by removing an equal number of cells (estimated from culture density) from the flask and immediately adding it to two volumes of RNA stabilization buffer (RNA Protect Bacteria Reagent, Qiagen, Valencia, CA). This prevents the rapid changes in mRNA content that otherwise occur when bacteria are harvested. Samples were mixed, left at room temperature for 10 min, and stored at 4°C for no more than 5 days.
Samples for microarray analysis were isolated at an OD600 of ~0.4, at which point 20 ml of culture was mixed with 2.5 ml of ice-cold 5% water-saturated phenol (pH < 7.0) in ethanol . After 10 min on ice, cells were pelleted, supernatant was removed, and pellets were frozen in liquid nitrogen and stored at -80°C, if necessary.
RNA Isolation and cDNA synthesis
For RT-PCR experiments total RNA was isolated using the RNeasy miniprep kit (Qiagen) using their protocol with an added sonication step. Briefly, cells in the stabilization buffer were harvested by centrifuging at 4°C for 15 min at 5,000 rpm. Supernatants were removed and the pellet was resuspended in 1× TE buffer containing lysozyme (400 μg/mL). Lysis buffer was added and the cells were sonicated 3× for 15 s in a cup horn attachment to enhance lysis. Following ethanol precipitation, RNA was bound to the column provided, washed and eluted. To eliminate DNA, the RNA was treated with RQ1 RNAse-free DNAse (Promega, Madison, WI) as directed. cDNA was synthesized using total RNA as template, random hexamers (Invitrogen, Carlsbad, CA), and ImPromII reverse transcriptase (Promega). The random primers were annealed at 25°C for 5 min, and the first strand was then extended at 42°C for 1 h. The reverse transcriptase was inactivated by heating to 70°C for 10 min. cDNA samples were stored at -20°C.
For microarray experiments total RNA was extracted by the hot phenol-chloroform method , and treated with DNase I in the presence of RNase inhibitor for subsequent labelling by reverse transcription with Cy3-dUTP and Cy5-dUTP fluorescent dyes (Amersham, Little Chalfont, United Kingdom). The RNeasy miniprep kit (see above) was also used in some cases.
Real time RT-PCR analysis
Primer sets (Integrated DNA Technologies, Coralville, IA) were designed for the adhE, gltB, lrp and recA genes for each strain (Table S1). Before each new experiment dilutions of cDNAs were tested to determine the concentration that gave maximally-efficient amplification, and to determine the efficiency for each primer set (23). Cycle threshold (CT) values were determined by Roche Lightcycler detection of SYBR green fluorescence. Melting curve (Roche Lightcycler software) and agarose gel analyses were used to confirm the formation of specific products, which ranged in size from 192–202 bp. The standard curve method was used to determine relative amounts of mRNA and levels were normalized to recA .
RNA was extracted from mid-log cells grown in MOPS glucose or MOPS rich media (plus nicotinate). RNA from the various samples was quantitated spectrophotometrically, diluted such that all samples had the same total RNA concentration, and then mixed 1:0, 1:1 and 1:7 with a standard level of RNA taken from E. coli grown in MOPS glucose (e.g., 1 μl E. coli MOPS glucose RNA mixed with 0, 1, or 7 μl P. mirabilis RNA). The RNA mixtures were then reverse transcribed (see above), and RT-PCR was performed using a 1:1:1 mixture of the lrp primer sets specific to each organism. Actual CT values were then plotted against the CT values expected if all original samples had the same proportion of lrp mRNA to total RNA. The resulting slopes indicate the fraction of lrp-specific cDNA relative to that in the reference E. coli sample.
Western blot analysis
For each sample equal volumes of cells were centrifuged at 16,000 – g for two min. The supernatants were removed and the cell pellets were stored at -80°C until analysis. Pellets were resuspended in 1× SDS buffer (Novagen, Madison, WI). Cells were lysed by heating to 98°C for ten min, and total protein concentrations were determined using the RC/DC kit and protocol (BioRad, Hercules, CA). Equal amounts of protein were loaded on a 12% acrylamide SDS gel and electrophoresed at 110 V in 1× tris-glycine buffer. Proteins were then electroblotted to polyvinylidene difluoride (PVDF) membranes at 30 V for 1 h using the Xcell blot apparatus (Invitrogen). Proteins were detected by fluorescence using the ECL-plus Western Blotting Detection System (GE Health Sciences, Piscataway, NJ) per the manufacturer's protocol, with a 1:125 dilution of rabbit anti-Lrp polyclonal serum (gift of Dr. Joseph Calvo ), and a 1:25,000 dilution of horseradish peroxidase-conjugated goat anti-rabbit IgG (gift of Dr. Darren Sledjeski). Protein bands were visualized on a Storm 840I phosphorimager (Molecular Dynamics, now GE Healthcare). Densitometric analysis of Lrp bands was performed using the Molecular Dynamics software, and the amount of Lrp in each sample was determined by comparison to a standard curve from purified Lrp dilutions included on each gel.
Starting with freshly-streaked single colonies, cultures (supplemented with Ile, Leu, Val and thiamine as described above) were aerobically grown overnight at 37°C and then diluted 20-fold into 20 ml of fresh medium. Recombinant cultures were propagated in the presence of chloramphenicol, and growth was monitored via OD600 nm. Cultures were maintained in exponential growth for at least 10 generations by dilution.
Relative mRNA abundances between the lrp mutant and the same strain carrying a lrp gene on plasmid pCC1 (or carrying only the vector) were determined, using at least three biological replicates. Each replicate culture was grown on a different day, and inoculated with a mix of 2–3 average-sized colonies less than a day old. This analysis employed E. coli K-12 whole-genome DNA microarrays including 99% of all annotated open reading frames and the stable RNA genes. Slide preparation, reverse transcription with the Cy-dyes, hybridization, and image scanning were performed as previously described . The fluorescent probes were hybridized to an array at 65°C for 6 h. Intensities in both channels were smoothed using the Lowess method . Some biological replicate samples were split into technical replicates, on which dye-swap analyses were conducted. Known Lrp targets were taken from RegulonDB [71, 72], and are listed below.
aidB aroA b2659 dadA dadX fimA fimC fimD fimE
fimF fimG fimH fimI gabP gabT gcvH gcvP gcvT
gltB gltD gltF ilvA ilvD ilvE ilvG_1 ilvG_2 ilvH
ilvI ilvL ilvM kbl livF livG livH livJ livK
livM lrp lysU malT micF ompC ompF oppA oppB
oppC oppD oppF osmC osmY sdaA serA serC stpA
tdh yeiL ygaF
Statistical analysis of microarray data
Dye- and array-specific noise was removed using the analysis of variance (ANOVA) error model . In pair-wise comparisons, differentially expressed genes were identified at an estimated false discovery rate of less than 5% using the two-class T-test in the SAM package . The NULL hypothesis was that gene-specific intensities in two classes have indistinguishable means.
Strains were grown to exponential phase in glucose minimal MOPS medium (Teknova). Samples were taken at 20 and 30-min intervals throughout the growth period. Levels of β-galactosidase were determined by o-nitrophenyl-β-D-galactoside (ONPG) hydrolysis . β-galactosidase levels were plotted against culture absorbance, and points were fitted via linear regression. The resulting slope yields the β-galactosidase activity.
Cloning of lrp orthologs
The lrp genes (translational start to stop) from E. coli O157:H7, P. mirabilis HI4320 and V. cholerae El tor A1552 were PCR amplified from chromosomal DNA using Pfx DNA polymerase (Invitrogen). The upstream PCR primers (Table S1) contained a consensus E. coli ribosome binding site. Fragments were gel purified and cloned into the low-copy pCC1 blunt cloning vector (Epicentre), and transformed into E. coli EPI300 per the manufacturer's protocol. As a vector control, an irrelevant ~1360 bp DNA fragment (kanamycin resistance cassette provided by the manufacturer as a ligation control) was inserted into pCC1. Transformants were selected using chloramphenicol and sequence-confirmed. The recombinants pECLRP, pPMLRP and pVCLRP (Table 1) were isolated using Qiagen miniprep columns. The purified plasmids were then electroporated into E. coli BE3780 (Table 1) using a BioRad E. coli gene pulser and protocol. For experiments with P. mirabilis and V. cholerae Δlrp strains, which are already chloramphenicol resistant, these plasmids were digested with BsmI to remove the cat gene, and we inserted a kanamycin resistance gene PCR amplified from pACYC177.
Construction of lacZfusions
The promoter regions of the lrp and gltB geneswere PCR amplified from E. coli O157:H7, Proteus mirabilis HI4320 and Vibrio cholerae El tor type N16969 chromosomal DNA using gene specific primers (Table S1) and Pfx DNA polymerase (Invitrogen). The PCR products were digested with BamHI and SalI and ligated into pBH403, which is a derivative of pKK232-8 and contains a promoterless lacZ gene between two bidirectional transcription terminators. The recombinant plasmids (Table 1) were electroporated into E. coli BE10.2 and PS2209;Proteus mirabilis U6450 and U6450Δlrp; and Vibrio cholerae El tor strain A1552 and A1552Δlrp.
The authors thank Gary Schoolnik and Nadia Dolganov (Stanford Univ.) for the Vibrio cholerae lrp+/- pair, Gillian Fraser (Cambridge Univ.) for the Proteus mirabilis lrp+/- pair, Harry Mobley (Univ. of Michigan) for Proteus mirabilis HI4320, Victor DiRita (Univ. of Michigan) for V. cholerae DNA, Joseph Calvo (Cornell) for anti-Lrp antisera, Rick Keck (Univ. of Toledo) for help with plate photography, Sadik Khuder (Univ. of Toledo) for help and advice regarding non-microarray statistical anlyses, Maria Vanoni (Univ. degli Studi di Milano) for advice on GltB orthologs, and Mark Wooten and John Lazarus (Univ. of Toledo) for their advice regarding QRT-PCR. We also thank Drs. Schoolnik and Isabel Novella (Univ. of Toledo), and the anonymous reviewers for suggestions on the manuscript. This work was supported by funds from NIH grant R01 AI54716 to RMB, with subcontract to ABK. REL was also supported, in part, by a graduate fellowship from the University of Toledo Health Science Campus.
- Liolios K, Tavernarakis N, Hugenholtz P, Kyrpides NC: The Genomes On Line Database (GOLD) v.2: a monitor of genome projects worldwide. Nucleic Acids Res. 2006, 34 (Database issue): D332-4. 10.1093/nar/gkj145.PubMed CentralPubMedGoogle Scholar
- Handelsman J: Metagenomics: application of genomics to uncultured microorganisms. Microbiol Mol Biol Rev. 2004, 68 (4): 669-685. 10.1128/MMBR.68.4.669-685.2004.PubMed CentralPubMedGoogle Scholar
- Riesenfeld CS, Schloss PD, Handelsman J: Metagenomics: genomic analysis of microbial communities. Annu Rev Genet. 2004, 38: 525-552. 10.1146/annurev.genet.38.072902.091216.PubMedGoogle Scholar
- Rusch DB, Halpern AL, Sutton G, Heidelberg KB, Williamson S, Yooseph S, Wu D, Eisen JA, Hoffman JM, Remington K, Beeson K, Tran B, Smith H, Baden-Tillson H, Stewart C, Thorpe J, Freeman J, Andrews-Pfannkoch C, Venter JE, Li K, Kravitz S, Heidelberg JF, Utterback T, Rogers YH, Falcon LI, Souza V, Bonilla-Rosso G, Eguiarte LE, Karl DM, Sathyendranath S, Platt T, Bermingham E, Gallardo V, Tamayo-Castillo G, Ferrari MR, Strausberg RL, Nealson K, Friedman R, Frazier M, Venter JC: The Sorcerer II Global Ocean Sampling Expedition: Northwest Atlantic through Eastern Tropical Pacific. PLoS Biol. 2007, 5 (3): e77-10.1371/journal.pbio.0050077.PubMed CentralPubMedGoogle Scholar
- Steele HL, Streit WR: Metagenomics: advances in ecology and biotechnology. FEMS Microbiol Lett. 2005, 247 (2): 105-111. 10.1016/j.femsle.2005.05.011.PubMedGoogle Scholar
- Field D, Kyrpides N: The positive role of the ecological community in the genomic revolution. Microb Ecol. 2007, 53 (3): 507-511. 10.1007/s00248-007-9206-5.PubMedGoogle Scholar
- Gill SR, Pop M, Deboy RT, Eckburg PB, Turnbaugh PJ, Samuel BS, Gordon JI, Relman DA, Fraser-Liggett CM, Nelson KE: Metagenomic analysis of the human distal gut microbiome. Science. 2006, 312 (5778): 1355-1359. 10.1126/science.1124234.PubMed CentralPubMedGoogle Scholar
- Kennedy J, Marchesi JR, Dobson AD: Metagenomic approaches to exploit the biotechnological potential of the microbial consortia of marine sponges. Appl Microbiol Biotechnol. 2007, 75 (1): 11-20. 10.1007/s00253-007-0875-2.PubMedGoogle Scholar
- Edwards RA, Rodriguez-Brito B, Wegley L, Haynes M, Breitbart M, Peterson DM, Saar MO, Alexander S, Alexander EC, Rohwer F: Using pyrosequencing to shed light on deep mine microbial ecology. BMC Genomics. 2006, 7: 57-10.1186/1471-2164-7-57.PubMed CentralPubMedGoogle Scholar
- Duncan MJ: Genomics of oral bacteria. Crit Rev Oral Biol Med. 2003, 14: 175-187.PubMedGoogle Scholar
- Mao F, Su Z, Olman V, Dam P, Liu Z, Xu Y: Mapping of orthologous genes in the context of biological pathways: An application of integer programming. Proc Natl Acad Sci U S A. 2006, 103 (1): 129-134. 10.1073/pnas.0509737102.PubMed CentralPubMedGoogle Scholar
- Powell BC, Hutchison CA: Similarity-based gene detection: using COGs to find evolutionarily-conserved ORFs. BMC Bioinformatics. 2006, 7: 31-10.1186/1471-2105-7-31.PubMed CentralPubMedGoogle Scholar
- Edwards JS, Ibarra RU, Palsson BO: In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nat Biotechnol. 2001, 19 (2): 125-130. 10.1038/84379.PubMedGoogle Scholar
- Jothi R, Przytycka TM, Aravind L: Discovering functional linkages and uncharacterized cellular pathways using phylogenetic profile comparisons: a comprehensive assessment. BMC Bioinformatics. 2007, 8: 173-10.1186/1471-2105-8-173.PubMed CentralPubMedGoogle Scholar
- Galbraith SJ, Tran LM, Liao JC: Transcriptome network component analysis with limited microarray data. Bioinformatics. 2006, 22 (15): 1886-1894. 10.1093/bioinformatics/btl279.PubMedGoogle Scholar
- Kim H, Hu W, Kluger Y: Unraveling condition specific gene transcriptional regulatory networks in Saccharomyces cerevisiae. BMC Bioinformatics. 2006, 7: 165-10.1186/1471-2105-7-165.PubMed CentralPubMedGoogle Scholar
- Pournara I, Wernisch L: Factor analysis for gene regulatory networks and transcription factor activity profiles. BMC Bioinformatics. 2007, 8: 61-10.1186/1471-2105-8-61.PubMed CentralPubMedGoogle Scholar
- Qian J, Lin J, Luscombe NM, Yu H, Gerstein M: Prediction of regulatory networks: genome-wide identification of transcription factor targets from gene expression data. Bioinformatics. 2003, 19 (15): 1917-1926. 10.1093/bioinformatics/btg347.PubMedGoogle Scholar
- Wang Y, Joshi T, Zhang XS, Xu D, Chen L: Inferring gene regulatory networks from multiple microarray datasets. Bioinformatics. 2006, 22 (19): 2413-2420. 10.1093/bioinformatics/btl396.PubMedGoogle Scholar
- VanBogelen RA, Greis KD, Blumenthal RM, Tani TH, Matthews RG: Mapping regulatory networks in microbial cells. Trends Microbiol. 1999, 7 (8): 320-328. 10.1016/S0966-842X(99)01540-1. 1999/08/04PubMedGoogle Scholar
- Balaji S, Aravind L: The two faces of short-range evolutionary dynamics of regulatory modes in bacterial transcriptional regulatory networks. Bioessays. 2007, 29 (7): 625-629. 10.1002/bies.20600.PubMedGoogle Scholar
- Espinosa V, Gonzalez AD, Vasconcelos AT, Huerta AM, Collado-Vides J: Comparative studies of transcriptional regulation mechanisms in a group of eight gamma-proteobacterial genomes. J Mol Biol. 2005, 354 (1): 184-199. 10.1016/j.jmb.2005.09.037.PubMedGoogle Scholar
- Ravcheev DA, Gerasimova AV, Mironov AA, Gelfand MS: Comparative genomic analysis of regulation of anaerobic respiration in ten genomes from three families of gamma-proteobacteria (Enterobacteriaceae, Pasteurellaceae, Vibrionaceae). BMC Genomics. 2007, 8: 54-10.1186/1471-2164-8-54.PubMed CentralPubMedGoogle Scholar
- Madan Babu M, Teichmann SA, Aravind L: Evolutionary dynamics of prokaryotic transcriptional regulatory networks. J Mol Biol. 2006, 358 (2): 614-633. 10.1016/j.jmb.2006.02.019.PubMedGoogle Scholar
- Devos D, Valencia A: Practical limits of function prediction. Proteins. 2000, 41 (1): 98-107. 10.1002/1097-0134(20001001)41:1<98::AID-PROT120>3.0.CO;2-S.PubMedGoogle Scholar
- Tian W, Skolnick J: How well is enzyme function conserved as a function of pairwise sequence identity?. J Mol Biol. 2003, 333 (4): 863-882. 10.1016/j.jmb.2003.08.057.PubMedGoogle Scholar
- Whisstock JC, Lesk AM: Prediction of protein function from protein sequence and structure. Q Rev Biophys. 2003, 36 (3): 307-340. 10.1017/S0033583503003901.PubMedGoogle Scholar
- Alexander PA, He Y, Chen Y, Orban J, Bryan PN: The design and characterization of two proteins with 88% sequence identity but different structure and function. Proc Natl Acad Sci U S A. 2007, 104: 11963-11968. 10.1073/pnas.0700922104.PubMed CentralPubMedGoogle Scholar
- Price MN, Dehal PS, Arkin AP: Orthologous Transcription Factors in Bacteria Have Different Functions and Regulate Different Genes. PLoS Comput Biol. 2007, 3 (9): 1739-50. 10.1371/journal.pcbi.0030175.PubMedGoogle Scholar
- Bulyk ML, McGuire AM, Masuda N, Church GM: A motif co-occurrence approach for genome-wide prediction of transcription-factor-binding sites in Escherichia coli. Genome Res. 2004, 14 (2): 201-208. 10.1101/gr.1448004.PubMed CentralPubMedGoogle Scholar
- Gelfand MS, Novichkov PS, Novichkova ES, Mironov AA: Comparative analysis of regulatory patterns in bacterial genomes. Brief Bioinform. 2000, 1 (4): 357-371. 10.1093/bib/1.4.357.PubMedGoogle Scholar
- Sandve GK, Drablos F: A survey of motif discovery methods in an integrated framework. Biol Direct. 2006, 1: 11-10.1186/1745-6150-1-11.PubMed CentralPubMedGoogle Scholar
- Sarai A, Kono H: Protein-DNA recognition patterns and predictions. Annu Rev Biophys Biomol Struct. 2005, 34: 379-398. 10.1146/annurev.biophys.34.040204.144537.PubMedGoogle Scholar
- Tan K, McCue LA, Stormo GD: Making connections between novel transcription factors and their DNA motifs. Genome Res. 2005, 15 (2): 312-320. 10.1101/gr.3069205.PubMed CentralPubMedGoogle Scholar
- Gelfand MS: Evolution of transcriptional regulatory networks in microbial genomes. Curr Opin Struct Biol. 2006, 16 (3): 420-429. 10.1016/j.sbi.2006.04.001.PubMedGoogle Scholar
- Robison K, McGuire AM, Church GM: A comprehensive library of DNA-binding site matrices for 55 proteins applied to the complete Escherichia coli K-12 genome. J Mol Biol. 1998, 284 (2): 241-254. 10.1006/jmbi.1998.2160.PubMedGoogle Scholar
- Thieffry D, Salgado H, Huerta AM, Collado-Vides J: Prediction of transcriptional regulatory sites in the complete genome sequence of Escherichia coli K-12. Bioinformatics. 1998, 14 (5): 391-400. 10.1093/bioinformatics/14.5.391.PubMedGoogle Scholar
- Reddy TE, DeLisi C, Shakhnovich BE: Binding site graphs: a new graph theoretical framework for prediction of transcription factor binding sites. PLoS Comput Biol. 2007, 3 (5): e90-10.1371/journal.pcbi.0030090.PubMed CentralPubMedGoogle Scholar
- Schones DE, Smith AD, Zhang MQ: Statistical significance of cis-regulatory modules. BMC Bioinformatics. 2007, 8: 19-10.1186/1471-2105-8-19.PubMed CentralPubMedGoogle Scholar
- Kalir S, Alon U: Using a quantitative blueprint to reprogram the dynamics of the flagella gene network. Cell. 2004, 117 (6): 713-720. 10.1016/j.cell.2004.05.010.PubMedGoogle Scholar
- Liu J, Stormo GD: Combining SELEX with quantitative assays to rapidly obtain accurate models of protein-DNA interactions. Nucleic Acids Res. 2005, 33 (17): e141-10.1093/nar/gni139.PubMed CentralPubMedGoogle Scholar
- Liu J, Stormo GD: Quantitative analysis of EGR proteins binding to DNA: assessing additivity in both the binding site and the protein. BMC Bioinformatics. 2005, 6: 176-10.1186/1471-2105-6-176.PubMed CentralPubMedGoogle Scholar
- Liu Z, Mao F, Guo JT, Yan B, Wang P, Qu Y, Xu Y: Quantitative evaluation of protein-DNA interactions using an optimized knowledge-based potential. Nucleic Acids Res. 2005, 33 (2): 546-558. 10.1093/nar/gki204.PubMed CentralPubMedGoogle Scholar
- Morozov AV, Havranek JJ, Baker D, Siggia ED: Protein-DNA binding specificity predictions with structural models. Nucleic Acids Res. 2005, 33 (18): 5781-5798. 10.1093/nar/gki875.PubMed CentralPubMedGoogle Scholar
- Lozada-Chavez I, Janga SC, Collado-Vides J: Bacterial regulatory networks are extremely flexible in evolution. Nucleic Acids Res. 2006, 34 (12): 3434-3445. 10.1093/nar/gkl423.PubMed CentralPubMedGoogle Scholar
- Tsong AE, Tuch BB, Li H, Johnson AD: Evolution of alternative transcriptional circuits with identical logic. Nature. 2006, 443 (7110): 415-420. 10.1038/nature05099.PubMedGoogle Scholar
- Mayo AE, Setty Y, Shavit S, Zaslaver A, Alon U: Plasticity of the cis-regulatory input function of a gene. PLoS Biol. 2006, 4: e45-10.1371/journal.pbio.0040045.PubMed CentralPubMedGoogle Scholar
- Hidalgo E, Demple B: Spacing of promoter elements regulates the basal expression of the soxS gene and converts SoxR from a transcriptional activator into a repressor. Embo J. 1997, 16 (5): 1056-1065. 10.1093/emboj/16.5.1056.PubMed CentralPubMedGoogle Scholar
- Kamionka A, Bogdanska-Urbaniak J, Scholz O, Hillen W: Two mutations in the tetracycline repressor change the inducer anhydrotetracycline to a corepressor. Nucleic Acids Res. 2004, 32 (2): 842-847. 10.1093/nar/gkh200.PubMed CentralPubMedGoogle Scholar
- Lin SH, Kovac L, Chin AJ, Chin CC, Lee JC: Ability of E. coli cyclic AMP receptor protein to differentiate cyclic nucelotides: effects of single site mutations. Biochemistry. 2002, 41 (9): 2946-2955. 10.1021/bi0119215.PubMedGoogle Scholar
- Suiter AM, Banziger O, Dean AM: Fitness consequences of a regulatory polymorphism in a seasonal environment. Proc Natl Acad Sci U S A. 2003, 100 (22): 12782-12786. 10.1073/pnas.2134994100.PubMed CentralPubMedGoogle Scholar
- ASM Press, EcoSal: Escherichia coli and Salmonella cellular and molecular biology. 2007, ASM PressGoogle Scholar
- Blattner FR, Plunkett G, Bloch CA, Perna NT, Burland V, Riley M, Collado-Vides J, Glasner JD, Rode CK, Mayhew GF, Gregor J, Davis NW, Kirkpatrick HA, Goeden MA, Rose DJ, Mau B, Shao Y: The complete genome sequence of Escherichia coli K-12. Science. 1997, 277: 1453-1474. 10.1126/science.277.5331.1453.PubMedGoogle Scholar
- Sanger_Institute: Proteus mirabilis genome sequencing project. [http://www.sanger.ac.uk/Projects/P_mirabilis/]
- Heidelberg JF, Eisen JA, Nelson WC, Clayton RA: DNA sequence of both chromosomes of the cholera pathogen Vibrio cholerae. Nature. 2000, 406: 477-483. 10.1038/35020000.PubMedGoogle Scholar
- Dick H, Murray RG, Walmsley S: Swarmer cell differentiation of Proteus mirabilis in fluid media. Can J Microbiol. 1985, 31 (11): 1041-1050.PubMedGoogle Scholar
- Harshey RM: Bees aren't the only ones: swarming in gram-negative bacteria. Mol Microbiol. 1994, 13 (3): 389-394. 10.1111/j.1365-2958.1994.tb00433.x.PubMedGoogle Scholar
- Gardel CL, Mekalanos JJ: Alterations in Vibrio cholerae motility phenotypes correlate with changes in virulence factor expression. Infect Immun. 1996, 64 (6): 2246-2255.PubMed CentralPubMedGoogle Scholar
- Rather PN: Swarmer cell differentiation in Proteus mirabilis. Environ Microbiol. 2005, 7 (8): 1065-1073. 10.1111/j.1462-2920.2005.00806.x.PubMedGoogle Scholar
- Nielsen AT, Dolganov NA, Otto G, Miller MC, Wu CY, Schoolnik GK: RpoS controls the Vibrio cholerae mucosal escape response. PLoS Pathog. 2006, 2 (10): e109-10.1371/journal.ppat.0020109.PubMed CentralPubMedGoogle Scholar
- Matz C, McDougald D, Moreno AM, Yung PY, Yildiz FH, Kjelleberg S: Biofilm formation and phenotypic variation enhance predation-driven persistence of Vibrio cholerae. Proc Natl Acad Sci U S A. 2005, 102 (46): 16819-16824. 10.1073/pnas.0505350102.PubMed CentralPubMedGoogle Scholar
- Martinez-Antonio A, Collado-Vides J: Identifying global regulators in transcriptional regulatory networks in bacteria. Curr Opin Microbiol. 2003, 6 (5): 482-489. 10.1016/j.mib.2003.09.002.PubMedGoogle Scholar
- Brinkman AB, Ettema TJ, de Vos WM, van der Oost J: The Lrp family of transcriptional regulators. Mol Microbiol. 2003, 48 (2): 287-294. 10.1046/j.1365-2958.2003.03442.x.PubMedGoogle Scholar
- Calvo JM, Matthews RG: The leucine-responsive regulatory protein, a global regulator of metabolism in Escherichia coli. Microbiol Rev. 1994, 58 (3): 466-490.PubMed CentralPubMedGoogle Scholar
- Newman EB, Lin R: Leucine-responsive regulatory protein: a global regulator of gene expression in E. coli. Annu Rev Microbiol. 1995, 49: 747-775. 10.1146/annurev.mi.49.100195.003531.PubMedGoogle Scholar
- Bhagwat SP, Rice MR, Matthews RG, Blumenthal RM: Use of an inducible regulatory protein to identify members of a regulon: application to the regulon controlled by the leucine-responsive regulatory protein (Lrp) in Escherichia coli. J Bacteriol. 1997, 179 (20): 6254-6263.PubMed CentralPubMedGoogle Scholar
- Ernsting BR, Atkinson MR, Ninfa AJ, Matthews RG: Characterization of the regulon controlled by the leucine-responsive regulatory protein in Escherichia coli. J Bacteriol. 1992, 174 (4): 1109-1118.PubMed CentralPubMedGoogle Scholar
- Hung SP, Baldi P, Hatfield GW: Global gene expression profiling in Escherichia coli K12. The effects of leucine-responsive regulatory protein. J Biol Chem. 2002, 277 (43): 40309-40323. 10.1074/jbc.M204044200.PubMedGoogle Scholar
- Tani TH, Khodursky A, Blumenthal RM, Brown PO, Matthews RG: Adaptation to famine: a family of stationary-phase genes revealed by microarray analysis. Proc Natl Acad Sci U S A. 2002, 99 (21): 13471-13476. 10.1073/pnas.212510999.PubMed CentralPubMedGoogle Scholar
- Tchetina E, Newman EB: Identification of Lrp-regulated genes by inverse PCR and sequencing: regulation of two mal operons of Escherichia coli by leucine-responsive regulatory protein. J Bacteriol. 1995, 177 (10): 2679-2683.PubMed CentralPubMedGoogle Scholar
- Salgado H, Gama-Castro S, Peralta-Gil M, Diaz-Peredo E, Sanchez-Solano F, Santos-Zavaleta A, Martinez-Flores I, Jimenez-Jacinto V, Bonavides-Martinez C, Segura-Salazar J, Martinez-Antonio A, Collado-Vides J: RegulonDB (version 5.0): Escherichia coli K-12 transcriptional regulatory network, operon organization, and growth conditions. Nucleic Acids Res. 2006, 34 (Database issue): D394-7. 10.1093/nar/gkj156.PubMed CentralPubMedGoogle Scholar
- Salgado H, Santos-Zavaleta A, Gama-Castro S, Peralta-Gil M, Penaloza-Spinola MI, Martinez-Antonio A, Karp PD, Collado-Vides J: The comprehensive updated regulatory network of Escherichia coli K-12. BMC Bioinformatics. 2006, 7 (1): 5-10.1186/1471-2105-7-5.PubMed CentralPubMedGoogle Scholar
- Chen S, Calvo JM: Leucine-induced dissociation of Escherichia coli Lrp hexadecamers to octamers. J Mol Biol. 2002, 318 (4): 1031-1042. 10.1016/S0022-2836(02)00187-0.PubMedGoogle Scholar
- Ernsting BR, Denninger JW, Blumenthal RM, Matthews RG: Regulation of the gltBDF operon of Escherichia coli: how is a leucine-insensitive operon regulated by the leucine-responsive regulatory protein?. J Bacteriol. 1993, 175 (22): 7160-7169.PubMed CentralPubMedGoogle Scholar
- Azam TA, Ishihama A: Twelve species of the nucleoid-associated protein from Escherichia coli. Sequence recognition specificity and DNA binding affinity. J Biol Chem. 1999, 274: 33105-33113. 10.1074/jbc.274.46.33105.PubMedGoogle Scholar
- Peterson SN, Dahlquist FW, Reich NO: The role of high affinity non-specific DNA binding by Lrp in transcriptional regulation and DNA organization. J Mol Biol. 2007, 369: 1307-1317. 10.1016/j.jmb.2007.04.023.PubMedGoogle Scholar
- Friedberg D, Plakto JV, Tyler B, Calvo JM: The amino acid sequence of Lrp is highly conserved in four enteric microorganisms. J Bacteriol. 1995, 177: 1624-1626.PubMed CentralPubMedGoogle Scholar
- Leonard PM, Smits SH, Sedelnikova SE, Brinkman AB, de Vos WM, van der Oost J, Rice DW, Rafferty JB: Crystal structure of the Lrp-like transcriptional regulator from the archaeon Pyrococcus furiosus. Embo J. 2001, 20 (5): 990-997. 10.1093/emboj/20.5.990.PubMed CentralPubMedGoogle Scholar
- Platko JV, Calvo JM: Mutations affecting the ability of Escherichia coli Lrp to bind DNA, activate transcription, or respond to leucine. J Bacteriol. 1993, 175 (4): 1110-1117.PubMed CentralPubMedGoogle Scholar
- de los Rios S, Perona JJ: Structure of the Escherichia coli leucine-responsive regulatory protein Lrp reveals a novel octameric assembly. J Mol Biol. 2007, 366 (5): 1589-1602. 10.1016/j.jmb.2006.12.032.PubMed CentralPubMedGoogle Scholar
- Price MN, Dehal PS, Arkin AP: Horizontal gene transfer and the evolution of transcriptional regulation in Escherichia coli. Genome Biol. 2008, 9 (1): R4-10.1186/gb-2008-9-1-r4. 2008/01/09PubMed CentralPubMedGoogle Scholar
- Friedberg D, Midkiff M, Calvo JM: Global versus local regulatory roles for Lrp-related proteins: Haemophilus influenzae as a case study. J Bacteriol. 2001, 183 (13): 4004-4011. 10.1128/JB.183.13.4004-4011.2001.PubMed CentralPubMedGoogle Scholar
- Nakamoto T: A unified view of the initiation of protein synthesis. Biochem Biophys Res Commun. 2006, 341 (3): 675-678. 10.1016/j.bbrc.2006.01.019.PubMedGoogle Scholar
- Hay NA, Tipper DJ, Gygi D, Hughes C: A nonswarming mutant of Proteus mirabilis lacks the Lrp global transcriptional regulator. J Bacteriol. 1997, 179 (15): 4741-4746.PubMed CentralPubMedGoogle Scholar
- Chen CF, Lan J, Korovine M, Shao ZQ, Tao L, Zhang J, Newman EB: Metabolic regulation of lrp gene expression in Escherichia coli K-12. Microbiology. 1997, 143 (6): 2079-2084.PubMedGoogle Scholar
- Landgraf JR, Wu J, Calvo JM: Effects of nutrition and growth rate on Lrp levels in Escherichia coli. J Bacteriol. 1996, 178 (23): 6930-6936.PubMed CentralPubMedGoogle Scholar
- Willins DA, Ryan CW, Platko JV, Calvo JM: Characterization of Lrp, and Escherichia coli regulatory protein that mediates a global response to leucine. J Biol Chem. 1991, 266 (17): 10768-10774.PubMedGoogle Scholar
- Tung JS, Knight CA: Relative importance of some factors affecting the electrophoretic migration of proteins in sodium dodecyl sulfate-polyacrylamide gels. Anal Biochem. 1972, 48 (1): 153-163. 10.1016/0003-2697(72)90179-0. 1972/07/01PubMedGoogle Scholar
- Wang Q, Wu J, Friedberg D, Plakto J, Calvo JM: Regulation of the Escherichia coli lrp gene. J Bacteriol. 1994, 176 (7): 1831-1839.PubMed CentralPubMedGoogle Scholar
- Lloyd G, Landini P, Busby S: Activation and repression of transcription initiation in bacteria. Essays Biochem. 2001, 37: 17-31.PubMedGoogle Scholar
- Rhodius VA, Busby SJ: Positive activation of gene expression. Curr Opin Microbiol. 1998, 1 (2): 152-159. 10.1016/S1369-5274(98)80005-2.PubMedGoogle Scholar
- Borst DW, Blumenthal RM, Matthews RG: Use of an in vivo titration method to study a global regulator: effect of varying Lrp levels on expression of gltBDF in Escherichia coli. J Bacteriol. 1996, 178 (23): 6904-6912.PubMed CentralPubMedGoogle Scholar
- Paul L, Blumenthal RM, Matthews RG: Activation from a distance: roles of Lrp and integration host factor in transcriptional activation of gltBDF. J Bacteriol. 2001, 183 (13): 3910-3918. 10.1128/JB.183.13.3910-3918.2001.PubMed CentralPubMedGoogle Scholar
- Paul L, Mishra PK, Blumenthal RM, Matthews RG: Integration of regulatory signals through involvement of multiple global regulators: control of the Escherichia coli gltBDF operon by Lrp, IHF, Crp, and ArgR. BMC Microbiol. 2007, 7: 2-10.1186/1471-2180-7-2.PubMed CentralPubMedGoogle Scholar
- Landick R, Oxender DL: The complete nucleotide sequences of the Escherichia coli LIV-BP and LS-BP genes. Implications for the mechanism of high-affinity branched-chain amino acid transport. J Biol Chem. 1985, 260 (14): 8257-8261.PubMedGoogle Scholar
- Haney SA, Platko JV, Oxender DL, Calvo JM: Lrp, a leucine-responsive protein, regulates branched-chain amino acid transport genes in Escherichia coli. J Bacteriol. 1992, 174 (1): 108-115.PubMed CentralPubMedGoogle Scholar
- Thaw P, Sedelnikova SE, Muranova T, Wiese S, Ayora S, Alonso JC, Brinkman AB, Akerboom J, van der Oost J, Rafferty JB: Structural insight into gene transcriptional regulation and effector binding by the Lrp/AsnC family. Nucleic Acids Res. 2006, 34 (5): 1439-1449. 10.1093/nar/gkl009.PubMed CentralPubMedGoogle Scholar
- Quay SC, Dick TE, Oxender DL: Role of transport systems in amino acid metabolism: leucine toxicity and the branched-chain amino acid transport systems. J Bacteriol. 1977, 129 (3): 1257-1265.PubMed CentralPubMedGoogle Scholar
- Bouvier J, Gordia S, Kampmann G, Lange R, Hengge-Aronis R, Gutierrez C: Interplay between global regulators of Escherichia coli: effect of RpoS, Lrp and H-NS on transcription of the gene osmC. Mol Microbiol. 1998, 28 (5): 971-980. 10.1046/j.1365-2958.1998.00855.x.PubMedGoogle Scholar
- Lin R, Ernsting B, Hirshfield IN, Matthews RG, Neidhardt FC, Clark RL, Newman EB: The lrp gene product regulates expression of lysU in Escherichia coli K-12. J Bacteriol. 1992, 174 (9): 2779-2784.PubMed CentralPubMedGoogle Scholar
- Rhee KY, Parekh BS, Hatfield GW: Leucine-responsive regulatory protein-DNA interactions in the leader region of the ilvGMEDA operon of Escherichia coli. J Biol Chem. 1996, 271 (43): 26499-26507. 10.1074/jbc.271.43.26499.PubMedGoogle Scholar
- Munch R, Hiller K, Grote A, Scheer M, Klein J, Schobert M, Jahn D: Virtual Footprint and PRODORIC: an integrative framework for regulon prediction in prokaryotes. Bioinformatics. 2005, 21 (22): 4187-4189. 10.1093/bioinformatics/bti635.PubMedGoogle Scholar
- Wong ML, Medrano JF: Real-time PCR for mRNA quantitation. Biotechniques. 2005, 39 (1): 75-85.PubMedGoogle Scholar
- Kessler D, Herth W, Knappe J: Ultrastructure and pyruvate formate-lyase radical quenching property of the multienzymic AdhE protein of Escherichia coli. J Biol Chem. 1992, 267 (25): 18073-18079.PubMedGoogle Scholar
- Kessler D, Leibrecht I, Knappe J: Pyruvate-formate-lyase-deactivase and acetyl-CoA reductase activities of Escherichia coli reside on a polymeric protein particle encoded by adhE. FEBS Lett. 1991, 281 (1-2): 59-63. 10.1016/0014-5793(91)80358-A.PubMedGoogle Scholar
- Knappe J, Sawers G: A radical-chemical route to acetyl-CoA: the anaerobically induced pyruvate formate-lyase system of Escherichia coli. FEMS Microbiol Rev. 1990, 6 (4): 383-398. 10.1016/S0168-6445(05)80006-3.PubMedGoogle Scholar
- Jung IL, Phyo KH, Kim IG: RpoS-mediated growth-dependent expression of the Escherichia coli tkt genes encoding transketolases isoenzymes. Curr Microbiol. 2005, 50 (6): 314-318. 10.1007/s00284-005-4501-1.PubMedGoogle Scholar
- Lange R, Hengge-Aronis R: Growth phase-regulated expression of bolA and morphology of stationary-phase Escherichia coli cells are controlled by the novel sigma factor sigma S. J Bacteriol. 1991, 173 (14): 4474-4481.PubMed CentralPubMedGoogle Scholar
- Membrillo-Hernandez J, Lin EC: Regulation of expression of the adhE gene, encoding ethanol oxidoreductase in Escherichia coli: transcription from a downstream promoter and regulation by fnr and RpoS. J Bacteriol. 1999, 181 (24): 7571-7579.PubMed CentralPubMedGoogle Scholar
- Vanoni MA, Curti B: Glutamate synthase: a complex iron-sulfur flavoprotein. Cell Mol Life Sci. 1999, 55 (4): 617-638. 10.1007/s000180050319.PubMedGoogle Scholar
- Vanoni MA, Dossena L, van den Heuvel RH, Curti B: Structure-function studies on the complex iron-sulfur flavoprotein glutamate synthase: the key enzyme of ammonia assimilation. Photosynth Res. 2005, 83 (2): 219-238. 10.1007/s11120-004-2438-z.PubMedGoogle Scholar
- Wiese DE, Ernsting BR, Blumenthal RM, Matthews RG: A nucleoprotein activation complex between the leucine-responsive regulatory protein and DNA upstream of the gltBDF operon in Escherichia coli. J Mol Biol. 1997, 270 (2): 152-168. 10.1006/jmbi.1997.1057.PubMedGoogle Scholar
- Hommais F, Krin E, Coppee JY, Lacroix C, Yeramian E, Danchin A, Bertin P: GadE (YhiE): a novel activator involved in the response to acid environment in Escherichia coli. Microbiology. 2004, 150 (Pt 1): 61-72. 10.1099/mic.0.26659-0. 2004/01/02PubMedGoogle Scholar
- Wang Q, Frye JG, McClelland M, Harshey RM: Gene expression patterns during swarming in Salmonella typhimurium: genes specific to surface growth and putative new motility and pathogenicity genes. Mol Microbiol. 2004, 52 (1): 169-187. 10.1111/j.1365-2958.2003.03977.x.PubMedGoogle Scholar
- Pul U, Wurm R, Wagner R: The role of LRP and H-NS in transcription regulation: involvement of synergism, allostery and macromolecular crowding. J Mol Biol. 2007, 366: 900-915. 10.1016/j.jmb.2006.11.067.PubMedGoogle Scholar
- Chen C, Newman EB: Comparison of the sensitivities of two Escherichia coli genes to in vivo variation of Lrp concentration. J Bacteriol. 1998, 180 (3): 655-659.PubMed CentralPubMedGoogle Scholar
- Beach MB, Osuna R: Identification and characterization of the fis operon in enteric bacteria. J Bacteriol. 1998, 180: 5932-5946.PubMed CentralPubMedGoogle Scholar
- Mallik P, Pratt TS, Beach MB, Bradley MD, Undamatla J, Osuna R: Growth phase-dependent regulation and stringent control of fis are conserved processes in enteric bacteria and involve a single promoter (fis P) in Escherichia coli. J Bacteriol. 2004, 186: 122-135. 10.1128/JB.186.1.122-135.2004.PubMed CentralPubMedGoogle Scholar
- Chen J, Hsueh HM, Delongchamp R, Lin CJ, Tsai CA: Reproducibility of microarray data: a further analysis of microarray quality control (MAQC)data. BMC Bioinformatics. 2007, 8 (1): 412-10.1186/1471-2105-8-412.PubMed CentralPubMedGoogle Scholar
- Shi L, Reid LH, Jones WD, Shippy R, Warrington JA, Baker SC, Collins PJ, de Longueville F, Kawasaki ES, Lee KY, Luo Y, Sun YA, Willey JC, Setterquist RA, Fischer GM, Tong W, Dragan YP, Dix DJ, Frueh FW, Goodsaid FM, Herman D, Jensen RV, Johnson CD, Lobenhofer EK, Puri RK, Schrf U, Thierry-Mieg J, Wang C, Wilson M, Wolber PK, Zhang L, Amur S, Bao W, Barbacioru CC, Lucas AB, Bertholet V, Boysen C, Bromley B, Brown D, Brunner A, Canales R, Cao XM, Cebula TA, Chen JJ, Cheng J, Chu TM, Chudin E, Corson J, Corton JC, Croner LJ, Davies C, Davison TS, Delenstarr G, Deng X, Dorris D, Eklund AC, Fan XH, Fang H, Fulmer-Smentek S, Fuscoe JC, Gallagher K, Ge W, Guo L, Guo X, Hager J, Haje PK, Han J, Han T, Harbottle HC, Harris SC, Hatchwell E, Hauser CA, Hester S, Hong H, Hurban P, Jackson SA, Ji H, Knight CR, Kuo WP, LeClerc JE, Levy S, Li QZ, Liu C, Liu Y, Lombardi MJ, Ma Y, Magnuson SR, Maqsodi B, McDaniel T, Mei N, Myklebost O, Ning B, Novoradovskaya N, Orr MS, Osborn TW, Papallo A, Patterson TA, Perkins RG, Peters EH, Peterson R, Philips KL, Pine PS, Pusztai L, Qian F, Ren H, Rosen M, Rosenzweig BA, Samaha RR, Schena M, Schroth GP, Shchegrova S, Smith DD, Staedtler F, Su Z, Sun H, Szallasi Z, Tezak Z, Thierry-Mieg D, Thompson KL, Tikhonova I, Turpaz Y, Vallanat B, Van C, Walker SJ, Wang SJ, Wang Y, Wolfinger R, Wong A, Wu J, Xiao C, Xie Q, Xu J, Yang W, Zhang L, Zhong S, Zong Y, Slikker W: The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat Biotechnol. 2006, 24 (9): 1151-1161. 10.1038/nbt1239.PubMedGoogle Scholar
- Grainger DC, Hurd D, Goldberg MD, Busby SJ: Association of nucleoid proteins with coding and non-coding segments of the Escherichia coli genome. Nucleic Acids Res. 2006, 34 (16): 4642-4652. 10.1093/nar/gkl542.PubMed CentralPubMedGoogle Scholar
- Lucchini S, Rowley G, Goldberg MD, Hurd D, Harrison M, Hinton JC: H-NS mediates the silencing of laterally acquired genes in bacteria. PLoS Pathog. 2006, 2 (8): e81-10.1371/journal.ppat.0020081.PubMed CentralPubMedGoogle Scholar
- Navarre WW, Porwollik S, Wang Y, McClelland M, Rosen H, Libby SJ, Fang FC: Selective silencing of foreign DNA with low GC content by the H-NS protein in Salmonella. Science. 2006, 313 (5784): 236-238. 10.1126/science.1128794.PubMedGoogle Scholar
- Oshima T, Ishikawa S, Kurokawa K, Aiba H, Ogasawara N: Escherichia coli histone-like protein H-NS preferentially binds to horizontally acquired DNA in association with RNA polymerase. DNA Res. 2006, 13 (4): 141-153. 10.1093/dnares/dsl009.PubMedGoogle Scholar
- Arfin SM, Long AD, Ito ET, Tolleri L, Riehle MM, Paegle ES, Hatfield GW: Global gene expression profiling in Escherichia coli K12. The effects of integration host factor. J Biol Chem. 2000, 275 (38): 29672-29684. 10.1074/jbc.M002247200.PubMedGoogle Scholar
- Mangan MW, Lucchini S, Danino V, Croinin TO, Hinton JC, Dorman CJ: The integration host factor (IHF) integrates stationary-phase and virulence gene expression in Salmonella enterica serovar Typhimurium. Mol Microbiol. 2006, 59 (6): 1831-1847. 10.1111/j.1365-2958.2006.05062.x.PubMedGoogle Scholar
- Bradley MD, Beach MB, de Koning AP, Pratt TS, Osuna R: Effects of Fis on Escherichia coli gene expression during different growth stages. Microbiology. 2007, 153 (Pt 9): 2922-2940. 10.1099/mic.0.2007/008565-0.PubMedGoogle Scholar
- Kelly A, Goldberg MD, Carroll RK, Danino V, Hinton JC, Dorman CJ: A global role for Fis in the transcriptional control of metabolism and type III secretion in Salmonella enterica serovar Typhimurium. Microbiology. 2004, 150 (Pt 7): 2037-2053. 10.1099/mic.0.27209-0.PubMedGoogle Scholar
- Marr C, Geertz M, Huett MT, Muskhelishvili G: Dissecting the logical types of network control in gene expression profiles. BMC Syst Biol. 2008, 2 (1): 18-10.1186/1752-0509-2-18. 2008/02/21PubMed CentralPubMedGoogle Scholar
- Wade JT, Reppas NB, Church GM, Struhl K: Genomic analysis of LexA binding reveals the permissive nature of the Escherichia coli genome and identifies unconventional target sites. Genes Dev. 2005, 19 (21): 2619-2630. 10.1101/gad.1355605. 2005/11/03PubMed CentralPubMedGoogle Scholar
- Grainger DC, Overton TW, Reppas N, Wade JT, Tamai E, Hobman JL, Constantinidou C, Struhl K, Church G, Busby SJ: Genomic studies with Escherichia coli MelR protein: applications of chromatin immunoprecipitation and microarrays. J Bacteriol. 2004, 186 (20): 6938-6943. 10.1128/JB.186.20.6938-6943.2004. 2004/10/07PubMed CentralPubMedGoogle Scholar
- Neidhardt FC, Bloch PL, Smith DF: Culture medium for enterobacteria. J Bacteriol. 1974, 119 (3): 736-747.PubMed CentralPubMedGoogle Scholar
- Serra-Moreno R, Acosta S, Hernalsteens JP, Jofre J, Muniesa M: Use of the lambda Red recombinase system to produce recombinant prophages carrying antibiotic resistance genes. BMC Mol Biol. 2006, 7: 31-10.1186/1471-2199-7-31.PubMed CentralPubMedGoogle Scholar
- Lin-Chao S, Cohen SN: The rate of processing and degradation of antisense RNAI regulates the replication of ColE1-type plasmids in vivo. Cell. 1991, 65 (7): 1233-1242. 10.1016/0092-8674(91)90018-T.PubMedGoogle Scholar
- Khodursky AB, Bernstein JA, Peter BJ, Rhodius V, Wendisch VF, Zimmer DP: Escherichia coli spotted double-strand DNA microarrays: RNA extraction, labeling, hybridization, quality control, and data management. Methods Mol Biol. 2003, 224: 61-78.PubMedGoogle Scholar
- Berger JA, Hautaniemi S, Jarvinen AK, Edgren H, Mitra SK, Astola J: Optimized LOWESS normalization parameter selection for DNA microarray data. BMC Bioinformatics. 2004, 5: 194-10.1186/1471-2105-5-194.PubMed CentralPubMedGoogle Scholar
- Cui X, Kerr MK, Churchill GA: Transformations for cDNA microarray data. Stat Appl Genet Mol Biol. 2003, 2: Article4-PubMedGoogle Scholar
- Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A. 2001, 98 (9): 5116-5121. 10.1073/pnas.091062498.PubMed CentralPubMedGoogle Scholar
- Platko JV, Willins DA, Calvo JM: The ilvIH operon of Escherichia coli is positively regulated. J Bacteriol. 1990, 172 (8): 4563-4570.PubMed CentralPubMedGoogle Scholar
- NCBI: BLink list of Lrp orthologs/paralogs. [http://0-www.ncbi.nlm.nih.gov.brum.beds.ac.uk/sutils/blink.cgi?pid=16128856]
- Jeong KS, Xie Y, Hiasa H, Khodursky AB: Analysis of pleiotropic transcriptional profiles: a case study of DNA gyrase inhibition. PLoS Genet. 2006, 2 (9): e152-10.1371/journal.pgen.0020152.PubMed CentralPubMedGoogle Scholar
- Kerr MK, Martin M, Churchill GA: Analysis of variance for gene expression microarray data. J Comput Biol. 2000, 7 (6): 819-837. 10.1089/10665270050514954.PubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.