- Open Access
Co-evolution of transcription factors and their targets depends on mode of regulation
© Hershberg and Margalit; licensee BioMed Central Ltd. 2006
- Received: 7 March 2006
- Accepted: 13 July 2006
- Published: 19 July 2006
Differences in the transcription regulation network are at the root of much of the phenotypic variation observed among organisms. These differences may be achieved either by changing the repertoire of regulators and/or their targets, or by rewiring the network. Following these changes and studying their logic is crucial for understanding the evolution of regulatory networks.
We use the well characterized transcription regulatory network of Escherichia coli K12 and follow the evolutionary changes in the repertoire of regulators and their targets across a large number of fully sequenced γ-proteobacteria. By focusing on close relatives of E. coli K12, we study the dynamics of the evolution of transcription regulation across a relatively short evolutionary timescale. We show significant differences in the evolution of repressors and activators. Repressors are only lost from a genome once their targets have themselves been lost, or once the network has significantly rewired. In contrast, activators are often lost even when their targets remain in the genome. As a result, E. coli K12 repressors that regulate many targets are rarely absent from organisms that are closely related to E. coli K12, while activators with a similar number of targets are often absent in these organisms.
We demonstrate that the mode of regulation exerted by transcription factors has a strong effect on their evolution. Repressors co-evolve tightly with their target genes. In contrast, activators can be lost independently of their targets. In fact, loss of an activator can lead to efficient shutdown of an unnecessary pathway.
- Regulatory Interaction
- Additional Data File
- Global Regulator
- Yersinia Pestis
- Related Bacterium
Comparison of gene repertoires in TRNs of various organisms
To learn about the evolution of transcription regulation, we focused on the changes that occur in the gene repertoire of the TRN. We used the well characterized TRN of E. coli K12  and examined which of the genes from this TRN (genes encoding TFs and target genes of TFs) are present in each of 30 fully sequenced bacteria (supplementary Table 1 in Additional data file 1). All these bacteria belong to the γ-proteobacteria, as does E. coli K12. By focusing on such a short evolutionary timescale, we gain insight into the dynamics of the evolution of the TRN, which is different from the insight that can be reached by looking at more distantly related organisms . The bacteria we examined can be further divided into two equally sized groups based on their evolutionary distance from E. coli K12: the first group contains organisms that, like E. coli K12, belong to the Enterobacteriaceae family; and the second group contains bacteria that belong to the same class as E. coli K12 (γ-proteobacteria), but are more distant relatives of E. coli K12 and do not belong to the Enterobacteriaceae family. We divided the TFs from the TRN of E. coli K12 into three groups based on their presence in the other organisms (see Materials and methods): the first group included those TFs that are present in all the examined organisms ('widely present'); the second group included those TFs that are present in all Enterobacteriaceae, but are absent from some of the more distantly related non-Enterobacteriaceae ('entero-present'); and the third group included those TFs that are already absent in some of the more closely related Enterobacteriaceae genomes ('entero-absent').
Repressors with many targets are more conserved than activators with many targets
Average number of targets of transcription factors classified based on conservation range
Type of targets
Widely present TFs*
6.7 ± 8.9
13.9 ± 23.5
66.6 ± 85.2
1.4 ± 2.6
6.2 ± 11.5
16.6 ± 17.8
5.1 ± 9
6.7 ± 12.8
42.3 ± 62.2
Presence of E. coli K12 transcription factors in close and remote relatives
In E. coli K12
TFs that regulate ≤ 5 targets
TFs that regulate >5 targets
Why are repressors that regulate many targets less likely than activators with many targets to be absent from close relatives of E. coli K12? This may be due to the different outcomes of losing a repressor or an activator. In eukaryotes the transcriptional ground state is restrictive , due to the influence of chromatin structure on the transcription of genes. Hence, in eukaryotes most genes will not be expressed in the absence of an activator TF. In contrast, in prokaryotes the transcriptional ground state is non-restrictive and genes will normally be transcribed unless they are repressed . It was argued that most of the promoters that are regulated by activators are intrinsically relatively weak . Thus, the loss of an activator will often result in a partial or total loss of function of its target genes. In cases in which this is detrimental to fitness, the bacteria that lost the TF would be removed from the population by selection. However, in other cases the loss of an activator may enhance fitness; if a pathway is no longer needed, losing the TF that activates that pathway may instantaneously shut down the pathway while conserving the energy that would have otherwise been spent on transcribing the genes responsible for that pathway. On the other hand, because of the non-restrictive transcriptional ground state, the loss of a repressor might lead to constitutive expression of its target genes, resulting almost always in a reduction in fitness. This conjecture implies that the loss of a repressor must be preceded by the loss of its targets or their rewiring, while this is less crucial when losing an activator. Thus, we next turned to examine the relationship between the status of a TF (absent/present) and the status of its targets.
Repressors, more than activators, are rarely lost while their targets remain in the genome
An additional factor that affects the association between the status of TFs and that of their targets is the probability of a target to be absent while its TF is present in the genome. This probability is higher for positive regulatory interactions than it is for negative regulatory interactions (supplementary Figure 2a in Additional data file 1). We found that this trend, which is observed in both the Enterobacteriaceae and non-Enterobacteriaceae, is caused to a large extent by regulatory interactions that involve global regulators. Global regulators tend to be well conserved and regulate a large number of targets. In addition, they regulate several different biological processes. If a certain function that is regulated by a global regulator is no longer needed, the genes encoding that function may be lost. However, the global regulator may still be needed, as it regulates additional functions. Therefore, we expect to see many cases in which a global regulator is conserved while its target is absent. There are more positive than negative regulatory interactions involving global regulators in our dataset (720 and 318 interactions, respectively), which may account for the enhanced probability of an activated target to be absent while its TF remains in the genome. Once the regulatory interactions involving the 15 known global regulators of E. coli are removed from our analysis this enhanced probability is no longer consistent (supplementary Figure 2b in Additional data file 1). At the same time the probability of activators to be absent while their targets are present in the genome remains consistently higher than that of repressors and this trend is even enhanced (supplementary Figure 1b in Additional data file 1).
In the non-Enterobacteriaceae genomes, which are more distantly related to E. coli K12, we find that the association observed between the absence or presence of the TFs and that of their targets is weaker than that observed in the more closely related organisms. A significant association was found for only 11 of the 15 non-Enterobacteriaceae when considering either positive or negative regulatory interactions. In the cases in which a statistically significant association was found, the p values for the association were generally higher than those found in the Enterobacteriaceae (p values range between 2.6e-11 and 0.031), while the phi-coefficients were generally lower (Figure 2b; supplementary Table 2 in Additional data file 1). This indicates that, in these organisms, the association between the status of the targets and the status of the TFs is less strong. In addition, in some of the organisms that are more distantly related to E. coli K12, the probability of an activator to be absent from the genome while its target is present is no longer higher than that of a repressor (supplementary Figure 1 in Additional data file 1). This may be explained by the fact that the evolution of the TRN is achieved not only through changes in the repertoire of TFs and targets, but also through the rewiring of the interactions between TFs and targets (Figure 1). With the passing of time both types of changes accumulate in the TRN. It is likely, therefore, that in the distantly related organisms more targets have alternative regulation. These targets are not regulated by the same TF that regulates them in E. coli K12, and, therefore, their absence or presence should not affect the likelihood that that TF will be absent. Thus, the weak associations we find between the status of the TFs and targets in the non-Enterobacteriaceae, compared to Enterobacteriaceae, suggest that the TRNs of E. coli K12 and these organisms are, to a large extent, wired differently.
Shutting down a pathway by loss of an activator
It is very interesting to note that all of the S. flexneri flagellar genes that underwent nonsense mutations are still maintained in the genome. This includes both the flhD gene and the fliA gene. Other than flhD, which has been truncated in S. flexneri and is only conserved along approximately 60% of its DNA sequence, all the flagellar genes with nonsense mutations have more than 90% sequence identity at the DNA level with their E. coli K12 counterparts. While S. flexneri is described in the Bergey's manual of systematic bacteriology  as a non-motile non-flagellated bacterium, Giron et al.  have identified surface appendages resembling flagella in Shigella. They termed these appendages flash (flagella of Shigella). Unlike the flagella of E. coli and Salmonella that emanate peritrichously with an average number of eight, flagellated Shigella produced only one polar flagellum. In addition, only 1 in 300 to 1,000 Shigella organisms grew flash, a frequency that is much lower than that observed in E. coli and Salmonella . In the study of Giron et al., which was conducted before the genome sequence of Shigella became available, they suggested that their findings may imply that Shigella does grow flagella and is motile, but the regulation of the biosynthesis is different. Our findings suggest a different explanation for the observation that Shigella can grow flagella at low frequencies: it may be possible that the flagellar genes that are maintained in the Shigella genome along with the genes encoding the regulator allow a small fraction of the organisms to revert to a partially flagellated phenotype.
An additional example of the way in which loss of an activator can lead to the shutting down of an entire pathway is the loss of the maltose utilization pathway in S. flexneri. In E. coli K12 and its maltose utilizing relatives, the activator MalT induces the transcription of 10 genes of the maltose utilization pathway. This activator is absent from S. flexneri. It has been shown that S. flexneri cannot utilize maltose and that malE, which is one of the genes regulated by MalT in E. coli K12, is not expressed in S. flexneri [17, 18]. However, the malE gene and the other nine maltose utilization genes are intact in the S. flexneri genome. These observations together show that, similar to the flagellar biosynthesis example, the shutting down of the maltose utilization pathway was achieved through the loss of the activator regulating the pathway.
In this study we focused on the evolution of the TRN in a relatively large number of closely related bacteria representing a short evolutionary timescale. The TRN evolves both by removing and adding nodes (TFs and/or gene targets) and by rewiring the connections between the nodes. As evolutionary distance increases, so does the number of changes observed between two TRNs: the TRNs of two more distantly related bacteria would thus show more differences, both in the repertoire of their TFs and in the ways in which the TFs and targets are connected. We show an interesting difference in the way in which the repertoires of repressors and activators evolve. In order for a repressor to be removed from the TRN, its targets need to either acquire alternative regulation through the rewiring of the network, or be removed themselves. For this reason, among closely related bacteria we rarely observe the removal of repressors, especially those that regulate many targets, and when such changes do occur they are frequently preceded by the removal of the target genes. In contrast, we observe changes in the repertoire of activators even among TRNs of very closely related bacteria. Activators may be lost as a way of turning off a pathway. In these cases the activator may be lost prior to the loss of its targets.
The TRN of E. coliK12
Data on E. coli K12 transcription factors and their target genes were extracted from Ma et al . This data set includes regulatory interactions of TFs in E. coli K12, including the sigma factors RpoS, RpoN, RpoE and RpoH. The sigma factors were not included in the analysis because they function as part of the RNA polymerase holoenzyme [3, 4], and are not considered as TFs. Interactions involving RyhB, glnL, Hfq or UidA as the regulators were also excluded because these molecules are not TFs [19–22]. In addition, all auto-regulatory interactions and all regulatory interactions for which the mode of regulation (positive, negative or dual) is unknown were also excluded. The resulting data set contains 2,285 regulatory interactions between 143 TFs and 1,048 target genes (Additional data file 2).
Of the 143 TFs included in our analysis, 15 have previously been characterized as global regulators, or as regulators that are located at the top layers of the hierarchical structure of the TRN [9, 11]. Such TFs are expected to affect several biological processes and integrate between them. These TFs are: CRP, IhfA, IhfB, FNR, Hns, ArcA, FIS, LRP, PhoB, ArgP, CspA, CspE, CytR, SoxR, and DnaA.
The regulatory interactions that were collected by Ma et al.  have since been included in the RegulonDB  and Ecocyc  databases. These regulatory interactions and their mode of regulation were gathered from publications and were determined by small-scale experiments.
Determining the presence or absence of genes from E. coliK12 in other γ-proteobacteria
Gene sequences were extracted from version NC_000913.1 of the E. coli K12 genome, and annotations of the genes were extracted from the Ecogene database . The genomic and protein sequences and the annotations of the 30 genomes in supplementary Table 1 in Additional data file 1 were downloaded from the NCBI ftp server . These 30 organisms can be divided into two groups, each containing 15 bacteria. The first group includes bacteria that, like E. coli K12, belong to the Enterobacteriaceae family. The second group contains bacteria that are not members of the Enterobacteriaceae family, but are included in the same class as E. coli (γ-proteobacteria). All amino acid sequences of the proteins encoded in E. coli K12 were compared to the sequences of the annotated proteins of each of the 30 organisms, using a locally installed version of the FASTA program . For each protein we recorded its best hit in each of the 30 organisms and the percentage identity across the entire E. coli K12 protein sequence. At the DNA level, each E. coli K12 protein-coding gene was compared to the complete genomic sequence of each of the 30 organisms, and the best hit and percentage identity were recorded for each organism.
For each gene in E. coli K12 and each organism, we compared the genomic location of the gene encoding the best hit at the protein level to the genomic location of the best hit at the DNA level. If in a certain genome the best hit at the protein level is located in the same location as the best hit at the DNA level, we consider the E. coli K12 gene and protein to be present in that genome. If the location of the protein best hit is different from that of the DNA best hit, we regard this protein as present in the genome if the percentage identity at the protein level is at least 40%.
We expect that for the proteins that are present in the different genomes the average percent identity will decrease as the evolutionary distance from E. coli K12 increases. The percentage of E. coli K12 genes that are maintained in a genome can be used as a measure of the distance of that genome from E. coli K12. Thus, if our threshold is reasonable, we expect to find a strong correlation between the average percent identity and the percentage of the E. coli K12 proteins that we annotated as present in the different organisms. Indeed, the Pearson correlation coefficient between the percentage of proteins that, according to our threshold, are present in the genome and their average percent identity is 0.97 (supplementary Table 1 in Additional data file 1). In contrast, the average percent identity of the best hits for the proteins that did not pass our threshold does not change with the evolutionary distance from E. coli K12 (Pearson correlation of -0.05; supplementary Table 1 in Additional data file 1). We therefore conclude that our threshold allows the separation of those proteins that are present in a genome from hits that are generated by chance.
Our method is different from the best bidirectional hit method that is commonly used to assign orthologs across large evolutionary time scales. We believe that when comparing closely related organisms for assigning a status of absence or presence to a gene our method is more suitable. However, to make sure that our results were not strongly affected by our assignment methodology we compared it to the best bidirectional hit method. We found that when comparing all of the proteins of E. coli K12 across the 30 organisms examined, the methods assign the genes differently in less than 4% of the cases.
Classifying TFs based on their presence in the various organisms
Evaluating the association between the status (present/absent) of the TFs and their targets
Regulatory interactions from E. coli K12 were divided based on their mode of regulation into positive and negative interactions. For each mode of regulation in each of the 30 organisms a contingency table of size 2 × 2 was created. Each contingency table contains the number of regulatory interactions in each of the four following categories: both the TF and its target are present in the genome (TFpres, targpres); the TF is absent but its target is present (TFabs, targpres); the TF is present but its target is absent (TFpres, targabs); and both the TF and its target are absent (TFabs, targabs). For each contingency table we carried out a χ2 test, testing the null hypothesis that the status of the targets (absent/present) and the status of the TFs are not associated. Rejection of the null hypothesis with p ≤ 0.05 implied a statistically significant association. We also estimated the strength of association by the phi-coefficient. The phi-coefficient is a derivative of the χ2 test. It is calculated as:
where f11, f12, f21, and f22 represent the counts appearing in the four cells of the 2 × 2 contingency tables, C1 and C2 represent the column sums of the values and R1 and R2 represent their row sums (Figure 2a).
Phi values can range from -1 to 1. The further the value is from zero, the stronger the association. Positive values indicate a positive association, while negative values indicate an inverse association. Thus, in our case a value of 1 would mean that there is complete agreement between the status of the TF and that of its targets. In such a case if the TF is present, all its targets would be present, and if a TF is absent, all its targets would be absent. A value of -1 would indicate a negative association. All the targets of an absent TF would be present and vice versa.
Our method of assigning orthologous relations depends on analyzing conservation at both the protein and the DNA levels. For this reason the 95 regulatory interactions in which the target is an RNA gene (tRNA, rRNA or ncRNA) were not considered in this analysis. These 95 interactions are marked by an asterisk in Additional data file 2.
The following additional data are available with the online version of this paper. Additional data file 1 contains supplementary figures and tables: supplementary Table 1 lists information regarding the 30 organisms used in the study; supplementary Table 2 lists the association between the status of TFs and the status of their targets; supplementary Figure 1 shows the probability of activators and repressors to be absent in the different genomes, while their targets are present; supplementary Figure 2 shows the probability of repressed and activated targets to be absent from the different genomes, while their regulating TFs are present. Additional data file 2 lists the regulatory interactions included in this study. Additional data file 3 lists the classification of TFs into three groups based on their presence in the different organisms.
We are thankful to Esti Yeger-Lotem, Yael Altuvia, Gila Lithwick and Eyal Akiva for helpful comments on the manuscript and to Norman Grover, Samuel Sattath, Guy Sella and Dmitri Petrov for stimulating discussions. This work was supported by the Israeli Science Foundation administered by the Israeli Academy of Sciences and Humanities. RH is supported by the Yeshaya Horowitz association through the Center of Complexity Science.
- Carroll SB: Evolution at two levels: on genes and form. PLoS Biol. 2005, 3: e245-10.1371/journal.pbio.0030245.PubMedPubMed CentralView ArticleGoogle Scholar
- Olson MV, Varki A: Sequencing the chimpanzee genome: insights into human evolution and disease. Nat Rev Genet. 2003, 4: 20-28. 10.1038/nrg981.PubMedView ArticleGoogle Scholar
- Wagner R: Transcription Regulation in Prokaryotes. 2000, Oxford: Oxford University press, 1Google Scholar
- Browning DF, Busby SJ: The regulation of bacterial transcription initiation. Nat Rev Microbiol. 2004, 2: 57-65. 10.1038/nrmicro787.PubMedView ArticleGoogle Scholar
- Ihmels J, Bergmann S, Gerami-Nejad M, Yanai I, McClellan M, Berman J, Barkai N: Rewiring of the yeast transcriptional network through the evolution of motif usage. Science. 2005, 309: 938-940. 10.1126/science.1113833.PubMedView ArticleGoogle Scholar
- Gasch AP, Moses AM, Chiang DY, Fraser HB, Berardini M, Eisen MB: Conservation and evolution of cis-regulatory systems in ascomycete fungi. PLoS Biol. 2004, 2: e398-10.1371/journal.pbio.0020398.PubMedPubMed CentralView ArticleGoogle Scholar
- Tanay A, Regev A, Shamir R: Conservation and evolvability in regulatory networks: the evolution of ribosomal regulation in yeast. Proc Natl Acad Sci USA. 2005, 102: 7203-7208. 10.1073/pnas.0502521102.PubMedPubMed CentralView ArticleGoogle Scholar
- Teichmann SA, Babu MM: Gene regulatory network growth by duplication. Nat Genet. 2004, 36: 492-496. 10.1038/ng1340.PubMedView ArticleGoogle Scholar
- Ma HW, Kumar B, Ditges U, Gunzer F, Buer J, Zeng AP: An extended transcriptional regulatory network of Escherichia coli and analysis of its hierarchical structure and network motifs. Nucleic Acids Res. 2004, 32: 6643-6649. 10.1093/nar/gkh1009.PubMedPubMed CentralView ArticleGoogle Scholar
- Madan Babu M, Teichmann SA, Aravind L: Evolutionary dynamics of prokaryotic transcriptional regulatory networks. J Mol Biol. 2006, 358: 614-633. 10.1016/j.jmb.2006.02.019.PubMedView ArticleGoogle Scholar
- Martinez-Antonio A, Collado-Vides J: Identifying global regulators in transcriptional regulatory networks in bacteria. Curr Opin Microbiol. 2003, 6: 482-489. 10.1016/j.mib.2003.09.002.PubMedView ArticleGoogle Scholar
- Struhl K: Fundamentally different logic of gene regulation in eukaryotes and prokaryotes. Cell. 1999, 98: 1-4. 10.1016/S0092-8674(00)80599-1.PubMedView ArticleGoogle Scholar
- Soutourina OA, Bertin PN: Regulation cascade of flagellar expression in Gram-negative bacteria. FEMS Microbiol Rev. 2003, 27: 505-523. 10.1016/S0168-6445(03)00064-0.PubMedView ArticleGoogle Scholar
- Tominaga A, Lan R, Reeves PR: Evolutionary changes of the flhDC flagellar master operon in Shigella strains. J Bacteriol. 2005, 187: 4295-4302. 10.1128/JB.187.12.4295-4302.2005.PubMedPubMed CentralView ArticleGoogle Scholar
- Krieg N: Bergey's Manual of Systematic Bacteriology. 1984, Baltimore: Williams & Wilkins, 1:Google Scholar
- Giron JA: Expression of flagella and motility by Shigella. Mol Microbiol. 1995, 18: 63-75. 10.1111/j.1365-2958.1995.mmi_18010063.x.PubMedView ArticleGoogle Scholar
- Dahl MK, Manson MD: Interspecific reconstitution of maltose transport and chemotaxis in Escherichia coli with maltose-binding protein from various enteric bacteria. J Bacteriol. 1985, 164: 1057-1063.PubMedPubMed CentralGoogle Scholar
- Jin Q, Yuan Z, Xu J, Wang Y, Shen Y, Lu W, Wang J, Liu H, Yang J, Yang F, et al: Genome sequence of Shigella flexneri 2a: insights into pathogenicity through comparison with genomes of Escherichia coli K12 and O157. Nucleic Acids Res. 2002, 30: 4432-4441. 10.1093/nar/gkf566.PubMedPubMed CentralView ArticleGoogle Scholar
- Masse E, Gottesman S: A small RNA regulates the expression of genes involved in iron metabolism in Escherichia coli. Proc Natl Acad Sci USA. 2002, 99: 4620-4625. 10.1073/pnas.032066599.PubMedPubMed CentralView ArticleGoogle Scholar
- Atkinson MR, Ninfa AJ: Characterization of Escherichia coli glnL mutations affecting nitrogen regulation. J Bacteriol. 1992, 174: 4538-4548.PubMedPubMed CentralGoogle Scholar
- Zhang A, Wassarman KM, Rosenow C, Tjaden BC, Storz G, Gottesman S: Global analysis of small RNA and mRNA targets of Hfq. Mol Microbiol. 2003, 50: 1111-1124. 10.1046/j.1365-2958.2003.03734.x.PubMedView ArticleGoogle Scholar
- Beaud D, Tailliez P, Anba-Mondoloni J: Genetic characterization of the beta-glucuronidase enzyme from a human intestinal bacterium, Ruminococcus gnavus. Microbiology. 2005, 151: 2323-2330. 10.1099/mic.0.27712-0.PubMedView ArticleGoogle 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, et al: RegulonDB (version 5.0): Escherichia coli K-12 transcriptional regulatory network, operon organization, and growth conditions. Nucleic Acids Res. 2006, 34: D394-397. 10.1093/nar/gkj156.PubMedPubMed CentralView ArticleGoogle Scholar
- Keseler IM, Collado-Vides J, Gama-Castro S, Ingraham J, Paley S, Paulsen IT, Peralta-Gil M, Karp PD: EcoCyc: a comprehensive database resource for Escherichia coli. Nucleic Acids Res. 2005, 33: D334-337. 10.1093/nar/gki108.PubMedPubMed CentralView ArticleGoogle Scholar
- Rudd KE: EcoGene: a genome sequence database for Escherichia coli K-12. Nucleic Acids Res. 2000, 28: 60-64. 10.1093/nar/28.1.60.PubMedPubMed CentralView ArticleGoogle Scholar
- NCBI ftp Server. [http://www.ncbi.nlm.nih.gov/Ftp/]
- Pearson WR, Lipman DJ: Improved tools for biological sequence comparison. Proc Natl Acad Sci USA. 1988, 85: 2444-2448. 10.1073/pnas.85.8.2444.PubMedPubMed CentralView ArticleGoogle Scholar
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