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Proc Natl Acad Sci U S A. Dec 9, 2008; 105(49): 19462–19467.
Published online Dec 3, 2008. doi:  10.1073/pnas.0807227105
PMCID: PMC2614783

Genome-scale reconstruction of the Lrp regulatory network in Escherichia coli


Broad-acting transcription factors (TFs) in bacteria form regulons. Here, we present a 4-step method to fully reconstruct the leucine-responsive protein (Lrp) regulon in Escherichia coli K-12 MG 1655 that regulates nitrogen metabolism. Step 1 is composed of obtaining high-resolution ChIP-chip data for Lrp, the RNA polymerase and expression profiles under multiple environmental conditions. We identified 138 unique and reproducible Lrp-binding regions and classified their binding state under different conditions. In the second step, the analysis of these data revealed 6 distinct regulatory modes for individual ORFs. In the third step, we used the functional assignment of the regulated ORFs to reconstruct 4 types of regulatory network motifs around the metabolites that are affected by the corresponding gene products. In the fourth step, we determined how leucine, as a signaling molecule, shifts the regulatory motifs for particular metabolites. The physiological structure that emerges shows the regulatory motifs for different amino acid fall into the traditional classification of amino acid families, thus elucidating the structure and physiological functions of the Lrp-regulon. The same procedure can be applied to other broad-acting TFs, opening the way to full bottom-up reconstruction of the transcriptional regulatory network in bacterial cells.

Keywords: ChIP-chip, transcription factor

Transcriptional regulatory systems often regulate the formation rates and the concentration of small molecules by 2 feedback loops that regulate the transporters and metabolic enzymes. In many cases, these 2 feedback loops are connected by a common transcription factor (TF) that senses the concentration of the small molecule (1). Little is known at present about the transition between the regulatory modes in the feedback loop motifs for global TFs in bacteria. One such transcription factor is the leucine-responsive protein (Lrp), which is a global transcription regulator widely distributed throughout the bacteria including Escherichia coli (24). The Lrp regulon includes genes involved in amino acid biosynthesis and degradation, small molecule transport, pili synthesis, and other cellular functions including 1-carbon metabolism (2, 46). The regulatory action of Lrp on target genes is often modulated by the binding of the small effector molecule leucine and in effect endows Lrp with the ability to affect transcriptional regulation in all possible ways. That is, upon addition of leucine to the environment, the activity of Lrp can be enhanced, reversed, or unaffected (2, 4, 7). Little is known about in vivo Lrp-binding events at the genome scale in the presence or absence of leucine and the extent to which the different modes of regulation are used for different metabolites. Such information is needed to reconstruct the Lrp regulon and the understanding of nitrogen metabolism.

In this study, we applied a systems approach by integrating genome-scale data from chromatin immunoprecipitation followed by microarray hybridization (ChIP-chip) for Lrp and RNA polymerase and from expression profiling to reconstruct the Lrp regulon. To achieve such reconstruction, we developed a 4-step process (Fig. 1). We first sought to comprehensively establish the Lrp-binding regions on the E. coli genome and any DNA sequence motif(s) correlated with the Lrp regulatory action. We measured the changes in RNA polymerase (RNAP) occupancies and mRNA transcript levels on a genome scale to determine the regulatory mode for each of the identified Lrp-binding regions under leucine-perturbed growth conditions. Second, we determined the regulatory modes governed by Lrp. Third, this enabled us to identify logical motif structures composed of 2 feedback loops for transporting and metabolizing small molecules. Fourth, we could classify the amino acids and other metabolites in to groups that had the same regulatory network motifs, and how these motifs were systematically shifted by the presence of leucine. The physiological role of the Lrp regulon is established through the reconstruction of its structure.

Fig. 1.
Overview of the method. (A) Comprehensive establishment of the Lrp-binding regions along with the changes in RNAP occupancies and mRNA transcript levels on a genome-scale to determine the regulatory mode for each of the identified Lrp-binding regions ...


Step 1: Identifying Lrp-Binding Regions and the Effects of Binding on Gene Expression.

Four sets of experiments on a genome-wide scale were performed to achieve these goals; (i) determination of Lrp-binding regions, (ii) promoter-profiling using rifampicin-treated cells, (iii) measurements of RNAP rearrangement, and (iv) measurements of changes in mRNA transcripts.

Determination of Lrp-binding regions on a genome-wide scale.

Lrp has been extensively characterized by in vitro DNA-binding experiments and in vivo mutational analysis; however, direct analysis of in vivo Lrp binding has not been fully described (2, 4, 8). Here, we employ the ChIP-chip approach to determine the in vivo Lrp-binding regions in E. coli cells growing in minimal media in exponential phase in the presence and absence of exogenous leucine and in stationary phase. Before microarray hybridization, we used quantitative PCR (qPCR) to validate the enrichment fold of the previously characterized Lrp-binding regions with ilvIH (9), gcvTHP (10, 11), and gltBDF operons (12) in immunoprecipitated DNA (IP-DNA) obtained from the strain harboring 8 ×myc-tagged Lrp protein (13). The qPCR results demonstrate that Lrp-bound DNA fragments were selectively immunoprecipitated from the growing E. coli cells under the conditions (Fig. S1).

To determine the genome-wide Lrp-binding regions, we next performed a hybridization of the IP-DNA (Cy5 channel) and mock IP-DNA (Cy3 channel) onto the high-resolution whole-genome tiling microarrays, which contained a total of 371,034 oligonucleotides with 50-bp tiles overlapping every 25-bp on both forward and reverse strands (14). The normalized log2 ratios obtained from the hybridization identify the genomic regions enriched in the IP-DNA sample compared with the mock IP-DNA sample and thereby represent a genome-wide map of in vivo interactions between Lrp protein and E. coli genome (Fig. 2A). The genome-wide Lrp-binding maps obtained from 3 different conditions, i.e., exponential growth phase in the presence (Fig. 2Ai) and the absence (Fig. 2Aii) of exogenous leucine and stationary growth phase in the absence of leucine (Fig. 2Aiii) indicated that the Lrp association on the E. coli genome is dramatically sensitive to the nutrient richness. Using a peak detection algorithm based on the double-regression model (15), 34, 92, and 134 unique and reproducible Lrp-binding regions were identified from the hybridizations in exponential phase in the presence and absence of leucine and in stationary phase in the absence of leucine, respectively (Tables S1 and S2).

Fig. 2.
Genome-wide distribution of Lrp-binding regions. (A) An overview of Lrp-binding profiles across the E. coli genome at exponential growth phase in the presence (i) or absence (ii) of leucine and at stationary growth phase (iii). Enrichment fold on the ...

Identification and sequence analysis of Lrp-binding regions.

Among a total of 138 Lrp-binding regions, 29 regions (21%) were bound under all 3 conditions examined (Fig. 2B). As expected, the Lrp associations with target regions were very sensitive to the addition of exogenous leucine, as judged by the number of Lrp-binding regions found under conditions in the absence and presence of exogenous leucine. Only 30% of binding sites (29 of 97) overlapped under the conditions in the absence and presence of exogenous leucine, and 65% of binding sites (63 of 97) were newly found in the absence of exogenous leucine. A total of 41 Lrp-binding regions were newly identified in the IP-DNA sample obtained from the stationary condition, supporting the hypothesis that Lrp plays an important role in transcriptional regulation under stationary growth conditions (16). Before this study, 23 Lrp-binding sites had been characterized by DNA-binding experiments in vitro and mutational analysis in vivo, 74% (17 of 23) of which were identified in this study (Fig. 2 A and C, Tables S1 and S2). The exceptions were lrp, osmC, ompC, micF, aidB, and csiD promoters, whose functions are related to responses to osmotic stress and stationary growth phase. To determine whether the failure to detect Lrp binding at these 6 sites was due to the sensitivity of the microarrays, we performed conventional ChIP assays followed by qPCR analysis and confirmed the absence of Lrp binding in those regions under the experimental conditions used in this study. Furthermore, the ChIP-chip results were experimentally confirmed by using qPCR on arbitrarily selected sites from the 138 Lrp-binding regions (gltB, ilvI, gcvT, kbl, leuE, dadA, yffQ, C0719, sbcD, fimA, tppB, ftsQ, lysU, brnQ, sdaA, oppA, stpA, dppA, livJ, ydeN, and ynfF) and 3 control regions (dmsA, sdhC, and paaZ). All of the selected Lrp-binding regions exhibited enrichment as a log2 ratio range of 1.5 ≈5.1, whereas the 3 control regions showed no significant enrichment (Fig. S1). On the basis of this analysis, we concluded that all or nearly all Lrp-binding regions identified here are bona fide binding sites.

We next assessed the locations of the Lrp-binding regions against the current annotated genome information (Genbank accession number NC_000913). The Lrp-binding regions were observed not only within intergenic (i.e., promoter and promoter-like) regions but also within intragenic (i.e., ORF) and convergent regions (i.e., intergenic region between 3′-ends of convergently transcribed genes) (Fig. S2). Judging from the fact that <10% of the E. coli genome is noncoding, our results indicate there is a strong preference for Lrp-binding targets to be located within the noncoding intergenic regions. To identify common DNA sequence motifs of the Lrp-binding regions, we developed an algorithm using both the DNA-binding position weight matrix (PWM) and the spacing between binding sites. The identified 15-bp sequence motif is structured with flanking CAG/CTG triplets and a central AT-rich signal that together are reminiscent of DNA sequence characteristics important for nucleosome positioning and stability (Fig. 2D, Fig. S2). We were not able to detect different LRP-binding motifs for subsets of LRP-binding regions corresponding to different LRP regulatory modes (see below.)

Genome-wide rearrangement of RNAP association.

To gain a better mechanistic understanding of the transcriptional regulatory roles of Lrp in response to exogenous leucine, we measured the RNAP occupancy on a genome scale to identify locations where RNAP occupancy is increased or decreased because of changes in Lrp-binding levels and exogenous leucine levels (see table available at http://systemsbiology.ucsd.edu/publications). In parallel, to identify the promoter regions on a genome scale, the cells were treated with rifampicin to inhibit the transcription elongation step (17). The RNAP-binding patterns obtained from rifampicin-treated cells (see table available at http://systemsbiology.ucsd.edu/publications) show similar patterns between the 2 conditions; however, those from rifampicin nontreated cells indicate differential RNAP binding, representing the genome-wide rearrangement of RNAP caused by the exogenous leucine. Fig. 3 shows examples of the differential bindings of RNAP and Lrp on the gcvTHP, serA, oppABCDF, and sdaA operons in response to exogenous leucine. For example, the decrease in RNAP occupancy at the serA locus was observed because of the exogenous leucine. At the same time, the consequence of leucine addition was the sharp reduction of the Lrp-binding levels at the promoter region of serA. In this case, the role of Lrp is that of an activator and the exogenous leucine serve as an antagonist to repress the expression of the serA (Fig. 3A). However, exogenous leucine represses the binding of Lrp, whose role is that of an inhibitor of RNAP binding to the oppABCDF and sdaA operons (a repressor in these cases), so that the transcription of the genes in the operons becomes induced in response to the exogenous leucine (Fig. 3 B and C).

Fig. 3.
Genome-wide rearrangement of RNAP occupancy. Upper and Lower show changes in RNAP- and Lrp-binding signals on the selected regions, (A) gcvTHP and serA, (B) oppABCDF, and (C) sdaA, respectively. Green (solid and dotted) lines and red (solid and dotted) ...

Measurements of changes in mRNA transcripts.

Next, we examined the effects of exogenous leucine on the changes in the mRNA transcript levels of the exponentially growing cells using Affymetrix GeneChip E. coli Genome 2.0 arrays. To begin with, 629 genes were differentially expressed in response to the exogenous leucine with P value <0.05 and log2 ratio >0.5 (see table available at http://systemsbiology.ucsd.edu/publications). Then, we compared the changes in mRNA transcript levels with the differential RNAP-binding levels. The differential RNAP-binding levels are defined as the difference between the sums of RNAP-binding levels across all probes in a targeted gene's ORF under the 2 conditions. In the previous report (14), there was very little correlation between the log2 ratio of RNAP-binding peaks obtained from the rifampicin-treated cells and the expression of the nearest downstream ORF. However, we observed that the changes in RNAP-binding levels are correlated with the changes in the mRNA transcript levels in response to the exogenous leucine (Fig. S3).

Given that Lrp differentially binds at least 91 promoter regions (≈194 genes) within exponentially growing cells in the absence and presence of exogenous leucine, we expected the deletion of lrp to result in a substantial effect on the global gene expression patterns during the exponential growth phase in the absence and presence of exogenous leucine. Statistical analysis of the gene expression profiles of a parental (MG1655) and an lrp deletion strains based on the 2-way ANOVA analysis (P value <0.005) shows that the Lrp directly and indirectly regulates ≈52% (≈330 genes) of differentially expressed genes in response to the exogenous leucine (see table available at http://systemsbiology.ucsd.edu/publications). The genes whose transcription levels we assumed to be controlled by the promoters directly bound by Lrp are summarized in Table S3.

Step 2: Regulatory Modes of Lrp in Response to Exogenous Leucine.

The most interesting aspect of the regulatory modes of Lrp in response to exogenous leucine is its variable response; in some cases, exogenous leucine has no effect on the action of Lrp; for others, it increases the effect of Lrp, and for others it reverses the effect of Lrp (7). We reconfirmed by qPCR the changes in the mRNA transcript levels of the selected Lrp-binding regions (36 regions) to use genome-wide expression profiling to define the regulatory modes of Lrp in response to exogenous leucine (Fig. S3). The results agreed well with the gene expression profiling data. Fig. 4A illustrates examples of changes in the levels of Lrp association at the promoters of gcvTHP, ftsQAZ, fimAICDFGH, livKHMGF, serA, and oppABCDF and changes in the levels of mRNA transcripts of the genes under the presence (L) and absence (E) of exogenous leucine. The Lrp associations at the promoter regions of gcvTHP and ftsQAZ were not strongly affected by the addition of exogenous leucine (Fig. 4A i and ii), strongly supporting the observation that exogenous leucine has no effect on the transcriptional regulatory action of Lrp at these promoters. To confirm the unique transcriptional regulation mediated by Lrp, we next measured the level of mRNA transcripts of gcvTHP and ftsQAZ operon by qPCR. As expected, the addition of exogenous leucine had no effect on the level of mRNA transcripts (Fig. 4A i and ii). Therefore, the first category of Lrp regulatory modes was denoted by the independent mode as shown in Fig. 4Bi. In this category, there are ftsQAZ and gcvTHP operons. In contrast, the Lrp associations at the promoter regions of fimAICDFGH and livKHMGF, which are known to be activated and repressed by Lrp, respectively, showed slight changes in the Lrp-binding levels in the presence of exogenous leucine (18, 19). However, the exogenous leucine strongly stimulated the changes in the levels of mRNA transcript levels of those operons (Fig. 4A iii and iv). Because the exogenous leucine stimulates the effect of Lrp bindings on the promoters, the second category was denoted by the concerted mode (Fig. 4Bii). Last, for most Lrp-regulated promoters identified (74 regions), the exogenous leucine relieved the effect of Lrp in vivo (Fig. 4Biii). For example, Lrp activates and represses the transcription of serA and oppABCDF operon in the absence of exogenous leucine, respectively (20, 21), and exogenous leucine caused the strong repression of the Lrp-bindings on those promoter regions (Fig. 4A v and vi). This third category was denoted by the reciprocal mode.

Fig. 4.
Regulatory modes of individual ORFs governed by Lrp in response to exogenous leucine. (A) Examples of independent mode [(I) gcvTHP and (II) ftsQAZ], concerted mode [(III) fimAICDFGH and (IV) livKHMGF], and reciprocal mode [(V) serA and (VI) oppABCDF]. ...

Step 3: Lrp-Regulated Feedback Loop Motifs.

We next functionally classified the ≈236 genes directly regulated by Lrp and found that the products of many of the genes are known or predicted to function in a wide range of cellular processes and molecular functions, which included amino acid biosynthesis, amino acid degradation, nutrient transport, and synthesis of fimbriae (Fig. S4). We noticed that the functions of 45% (≈118 genes) of those genes are mainly localized to the small molecule transport and metabolism. Fig. 5A shows the integrative analysis of Lrp-binding profiles, RNAP occupancy profiles, and mRNA transcript levels of the selected transporters and metabolic enzymes for amino acids. In the case of transporters, the concerted and reciprocal regulatory modes exist in response to the exogenous leucine. Specifically, the transporters for branched-chain amino acids (brnQ, ilvKHMGF, and ilvJHMGF) are regulated by concerted mode (Fig. 4Biv), whereas transporters for arginine, serine, alanine, and proline (artMQIP, sdaC, cycA, and proY, respectively) regulated by reciprocal mode (Fig. 4vi). Interestingly, Lrp reciprocally regulates the aromatic amino acid transporters (tyrP and mtr) and indirectly regulates the general aromatic amino acid transporter (aroP) via TyrR transcription factor (i.e., Lrp directly regulates the TyrR by reciprocal mode). However, Lrp regulates the metabolic enzymes such as ilvE, tdcB, sdaA, and dadA through only reciprocal mode.

Fig. 5.
Regulatory network motif reconstruction and the behavior of feedback loop motifs in response to the exogenous leucine. (A) Data integration between Lrp binding, RNAP occupancy, and gene expression profiles on the selected Lrp-regulated target genes (transporters ...

Step 4: Elucidation of the Function of the LRP Regulon.

From this analysis, we were able to connect transport (Uptake) and metabolic (Use) feedback loop pairs (Fig. 5Bi and Table S4) and characterize them by 1 of 4 possible combinations of feedback loop motifs (1). In the left loop, Lrp regulates transcription of the transport protein (T), facilitating the uptake of the small molecule (Xout), whereas in the right loop, Lrp controls transcription of metabolic enzymes (E) responsible for transforming Xin into Y (i.e., metabolites). The logic of the coupled loop motifs can be described by a notation that uses 2 signs (Fig. 5Bii). For example, the AA(+/+) loop motif indicates that the transcription of both transport and metabolic genes are activated, whereas the RR(−/−) motif demonstrates that the transcription of both genes are repressed. The possible logical structures of the feedback loop motifs can be characterized depending on how Lrp (or Lrp-leucine complex) activates or represses both T and E in response to the exogenous leucine: homeostasis (−/+), nutrition (+/+), flow homeostasis (−/−), and accumulation (+/−) (1). Based on the feedback loop motifs, we analyzed the behavior of logical structures of the transporters and metabolic enzymes in response to the exogenous leucine (Table S4). For example, there are 3 possible T-E combinations between branched-chain amino acids transporters (brnQ, livKHMGF, and livJHMGF) and 1 metabolic enzyme (ilvE) in E. coli. The combined feedback loop motifs for the branched-chain amino acids indicate the RR(−/−) logical structure in the absence of the exogenous leucine, whereas those are switched to RA(−/+) in exposing to exogenous leucine (Fig. 5C). However, the combined feedback loop motifs in the T-E combinations for the aromatic amino acids show the transition between RA(−/+) and AR(+/−) logical structures. In the end, we classified the logical structures of the feedback loop motifs into 3 categories based on the behavior of logical structures in response to the exogenous leucine (Fig. 5C).


We reconstructed the Lrp regulon in E. coli by combining genome-scale location analysis, promoter-profiling using rifampicin-treated cells, RNAP occupancy profiles, and gene expression data. We identified (i) regulatory modes for individual genes in the Lrp regulon, (ii) the behavior of the logical structures in the Lrp-regulatory feedback loop motifs composed of transporters and metabolic enzymes in response to the exogenous leucine, and (iii) the overall structure of the Lrp regulon and how it regulates the metabolism of families of amino acids and other metabolites.

The genome-wide maps of Lrp-binding regions presented here not only confirm the previously characterized Lrp-binding regions (≈17 regions) but also increase the number of known sites (≈138 regions) by a factor of 5. From the genome-wide mapping results, we were also able to show that: (i) A total of 138 Lrp-binding regions were identified, ≈84% of which were located within noncoding regions, whereas the remaining ≈16% were found within coding regions; (ii) 34 and 92 Lrp-binding regions were identified in exponentially growing cells in the presence and absence of exogenous leucine, respectively, indicating that Lrp bindings to the E. coli genome are dramatically sensitive to the addition of exogenous leucine; and (iii) the Lrp-binding sites on the E. coli genome under stationary growth condition (134 Lrp-binding regions identified) indicated that Lrp plays pivotal roles in the transcriptional regulation under stationary growth conditions. The high number of Lrp-binding regions was not surprising, given that global transcription factors such as Fnr, Crp, Ihf, Fis, and Hns specifically bind to between ≈63 and ≈224 target regions (2224).

The genome-wide location analysis showed Lrp binding at 17 intragenic regions within ORFs and at 2 intergenic regions adjacent to convergently transcribed genes where current genomic annotation did not indicate a possible promoter. These Lrp-binding regions may indicate that genomic features have not been yet discovered, such as promoters of novel genes, or that transcription factor–DNA interactions occur by chance and have not been removed by evolution (25). As more experiments of this type are performed and as more functions are assigned to the gene products of hypothetical ORFs, an even clearer picture of the Lrp-binding regions in E. coli will emerge. It is indicative of the power of the ChIP-chip approach that four-fifths of the Lrp-binding regions observed here were previously unknown. Furthermore, the physiological functions of nearly one-third of directly Lrp-regulated genes are currently unknown, most of which (≈98%) are likely to be regulated by Lrp under the stationary growth condition. Therefore, much of this regulation can be understood by the principle of “feast or famine” adaptation for survival in nutrient-rich or depleted environments (16). Although the physiological functions of the genes are currently unknown, the results presented here support previous suggestions that the physiological role of Lrp is to monitor the nutritional state of the cell to adjust its metabolism to changing nutritional conditions and, in cooperation with other regulatory networks such as alternative sigma factor σS, to coordinate these changes with the physical environment of the cell.

In general, the global transcription factors located at the higher level of hierarchy in the transcriptional regulatory network have many direct regulatory targets that are transcription factors located in the lower levels of the transcriptional regulatory network hierarchy (26). We have identified 11 such transcription factors that are under Lrp control. They are DhaR, CysB, TyrR, SlyA, EutR, TdcA, GadW, LrhA, DeoT, YkgK, and Yhe, the latter 2 of which are predicted regulatory proteins. Most of these transcription factors participate in the regulation of the amino acid metabolism and small molecule transport. These regulatory proteins altogether are known to regulate the transcription of at least 34 genes. There are likely to be additional genes indirectly regulated by Lrp, such as genes regulated by metabolites produced by the metabolic enzymes in the regulatory interaction. Therefore, the experimental data presented here support the previous suggestion that the size of Lrp regulon is ≈10% of all ORFs in E. coli (16, 27).

The data integration of Lrp-binding information with the gene expression profiles, promoter profiling using rifampicin-treated cells, and RNAP occupancy profiles has enabled us to elucidate a fuller mechanistic understanding of the differential Lrp-binding profiles in response to exogenous leucine. We were able to show that (i) the differential Lrp-binding profiles in response to the exogenous leucine described 3 unique regulatory modes (independent, concerted, and reciprocal); (ii) the functional classification of genes directly regulated by Lrp represents the diverse roles of Lrp in the E. coli metabolism and the 45% of genes in Lrp regulon to the small molecule transport and metabolism; (iii) the feedback loop motifs composed of transporters and metabolic enzymes can be reconstructed based on the unique regulatory modes governed by Lrp and the functional localization of the genes; and (iv) having described the behavior of the feedback loop motifs in response to exogenous leucine, we finally show that the 3 regulatory circuits for controlling small molecules uptake and utilization by the global transcription factor Lrp.

In summary, we have described an integrative analysis of various types of genome-scale data to comprehensively understand the design principles of a global transcription factor, Lrp in E. coli. In the future, this systems approach will enable us to derive a similar understanding for how broad-acting transcription factors coordinate their activities to arrive at a functional organism.

Materials and Methods

Bacterial Strains, Media, and Growth Conditions.

E. coli BOP508 cells harboring Lrp-8×myc, BOPΔ508 deleted for lrp, and MG1655 were grown in glucose (2 g/L) minimal M9 medium supplemented with or without 10 mM leucine. Glycerol stocks of the E. coli strains were inoculated into the minimal medium and cultured at 37 °C with constant agitation overnight. The cultures were diluted 1:100 into 50 mL of the fresh minimal medium and then cultured at 37 °C to an appropriate cell density. For the rifampicin-treated cells, the rifampicin dissolved in methanol was added to a final concentration of 150 μg/mL and stirred for 20 min. Cultures were monitored by OD600 nm to verify the inhibitory effects of rifampicin.


Cultures at midlog or stationary growth phase were cross-linked by 1% formaldehyde at room temperature for 25 min. After cell lysis and sonication, the cross-linked DNA-Lrp and DNA-RNAP complexes were immunoprecipitated by using antibody against myc-tag and RNA polymerase β subunit (rpoB), respectively, and Dynabeads Pan Mouse IgG magnetic beads (Invitrogen) followed by stringent washings (see SI Text for the detailed ChIP-chip protocol). After reversal of the cross-links by incubation at 65 °C overnight, the samples were treated by RNaseA (Qiagen) and proteaseK (Invitrogen) and then purified with a PCR purification kit (Qiagen). To verify the enrichment of the Lrp-binding regions in the DNA samples, 1 μL of IP or mock-IP DNA was used to perform gene-specific real-time qPCR with the specific primers to the promoter regions. The IP and mock-IP DNA were then amplified by random DNA amplification method (14). Then, the amplified DNA samples were labeled and hybridized onto whole-genome-tiled microarrays (NimbleGen). Detailed methods used for ChIP-chip analysis is described in SI Text.

Transcriptional Analysis.

Cultures were grown to midlog growth phase aerobically (OD A600≈ 0.6). The cultures (3 mL) were then added to 2 volumes of RNAprotect Bacteria Reagent (Qiagen) and total RNA was isolated by using RNAeasy columns (Qiagen) with DNaseI treatment. Total RNA yields were measured by using a spectrophotometer (A260) and quality was checked by visualization on agarose gels and by measuring the sample A260/A280 ratio (>1.8). cDNA preparation was performed as described in ref. 13. Each qPCR contained 0.5 μM of each forward and reverse primer (SI Text for the detailed qPCR protocol), 150 ng of cDNA, and 25 μL of SYBR Master Mix (Qiagen). Affymetrix GeneChip E. coli Genome 2.0 arrays were used for genome-scale transcriptional analyses. cDNA synthesis, fragmentation, end-terminus biotin labeling, and array hybridization were performed as recommended by Affymetrix standard protocol.

Data Analysis.

To identify enriched regions in the ChIP-chip data, we used the previously developed peak-finding algorithm (15). For the Lrp-binding site profile, the details of the manner in which we identified high-probability Lrp-binding regions using ChIP-chip peaks and of the algorithm that we developed to learn the Lrp DNA-binding signals are contained in SI Text.

Raw Experimental Data.

SI Text and all raw data files can be downloaded from http://systemsbiology.ucsd.edu/publications.

Supplementary Material

Supporting Information:


This work was supported by National Institutes of Health Grant GM062791 and the Office of Science (BER), U. S. Department of Energy, cooperative agreement DE-FC02-02ER63446.


The authors declare no conflict of interest.

This article is a PNAS Direct Submission.

This article contains supporting information online at www.pnas.org/cgi/content/full/0807227105/DCSupplemental.


1. Krishna S, Semsey S, Sneppen K. Combinatorics of feedback in cellular uptake and metabolism of small molecules. Proc Natl Acad Sci USA. 2007;104:20815–20819. [PMC free article] [PubMed]
2. Calvo JM, Matthews RG. The leucine-responsive regulatory protein, a global regulator of metabolism in Escherichia coli. Microbiol Rev. 1994;58:466–490. [PMC free article] [PubMed]
3. Yokoyama K, et al. Feast/famine regulatory proteins (FFRPs): Escherichia coli Lrp, AsnC and related archaeal transcription factors. FEMS Microbiol Rev. 2006;30:89–108. [PubMed]
4. Newman EB, Lin R. Leucine-responsive regulatory protein: a global regulator of gene expression in E. coli. Annu Rev Microbiol. 1995;49:747–775. [PubMed]
5. Landgraf JR, Wu J, Calvo JM. Effects of nutrition and growth rate on Lrp levels in Escherichia coli. J Bacteriol. 1996;178:6930–6936. [PMC free article] [PubMed]
6. Faith JJ, et al. Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol. 2007;5:e8. [PMC free article] [PubMed]
7. Lin R, D'Ari R, Newman EB. Lambda placMu insertions in genes of the leucine regulon: Extension of the regulon to genes not regulated by leucine. J Bacteriol. 1992;174:1948–1955. [PMC free article] [PubMed]
8. Cui Y, Wang Q, Stormo GD, Calvo JM. A consensus sequence for binding of Lrp to DNA. J Bacteriol. 1995;177:4872–4880. [PMC free article] [PubMed]
9. Marasco R, et al. In vivo footprinting analysis of Lrp binding to the ilvIH promoter region of Escherichia coli. J Bacteriol. 1994;176:5197–5201. [PMC free article] [PubMed]
10. Stauffer LT, Stauffer GV. Characterization of the gcv control region from Escherichia coli. J Bacteriol. 1994;176:6159–6164. [PMC free article] [PubMed]
11. Stauffer LT, Fogarty SJ, Stauffer GV. Characterization of the Escherichia coli gcv operon. Gene. 1994;142:17–22. [PubMed]
12. 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;175:7160–7169. [PMC free article] [PubMed]
13. Cho BK, Knight EM, Palsson BO. PCR-based tandem epitope tagging system for Escherichia coli genome engineering. BioTechniques. 2006;40:67–72. [PubMed]
14. Herring CD, et al. Immobilization of Escherichia coli RNA polymerase and location of binding sites by use of chromatin immunoprecipitation and microarrays. J Bacteriol. 2005;187:6166–6174. [PMC free article] [PubMed]
15. Zheng M, Barrera LO, Ren B, Wu YN. ChIP-chip: Data, model, and analysis. Biometrics. 2007;63:787–796. [PubMed]
16. Tani TH, et al. Adaptation to famine: A family of stationary-phase genes revealed by microarray analysis. Proc Natl Acad Sci USA. 2002;99:13471–13476. [PMC free article] [PubMed]
17. Campbell EA, et al. Structural mechanism for rifampicin inhibition of bacterial RNA polymerase. Cell. 2001;104:901–912. [PubMed]
18. Kelly A, et al. DNA supercoiling and the Lrp protein determine the directionality of fim switch DNA inversion in Escherichia coli K-12. J Bacteriol. 2006;188:5356–5363. [PMC free article] [PubMed]
19. 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;174:108–115. [PMC free article] [PubMed]
20. Platko JV, Willins DA, Calvo JM. The ilvIH operon of Escherichia coli is positively regulated. J Bacteriol. 1990;172:4563–4570. [PMC free article] [PubMed]
21. Rex JH, Aronson BD, Somerville RL. The tdh and serA operons of Escherichia coli: Mutational analysis of the regulatory elements of leucine-responsive genes. J Bacteriol. 1991;173:5944–5953. [PMC free article] [PubMed]
22. Grainger DC, et al. Studies of the distribution of Escherichia coli cAMP-receptor protein and RNA polymerase along the E. coli chromosome. Proc Natl Acad Sci USA. 2005;102:17693–17698. [PMC free article] [PubMed]
23. Grainger DC, Hurd D, Goldberg MD, Busby SJ. Association of nucleoid proteins with coding and non-coding segments of the Escherichia coli genome. Nucleic Acids Res. 2006;34:4642–4652. [PMC free article] [PubMed]
24. Grainger DC, et al. Transcription factor distribution in Escherichia coli: Studies with FNR protein. Nucleic Acids Res. 2007;35:269–278. [PMC free article] [PubMed]
25. Shimada T, Ishihama A, Busby SJ, Grainger DC. The Escherichia coli RutR transcription factor binds at targets within genes as well as intergenic regions. Nucleic Acids Res. 2008;36:3950–3955. [PMC free article] [PubMed]
26. Martinez-Antonio A, Collado-Vides J. Identifying global regulators in transcriptional regulatory networks in bacteria. Curr Opin Microbiol. 2003;6:482–489. [PubMed]
27. 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;277:40309–40323. [PubMed]

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