Prokaryotic Transcription Factors Research Articles

Dissecting the role of DNA sequence in Helicobacter pylori NikR/DNA recognition

HpNikR is a prokaryotic nickel binding transcription factor found in Helicobacter pylori, where it functions as a regulator of multiple genes including those involved in nickel ion homeostasis and acid adaptation. The target operator sequences of the genes that are regulated by HpNikR do not have symmetric recognition sites, and the mechanism by which HpNikR distinguishes between the genes it regulates is not well understood. HpNikR utilizes a two-tiered mode of DNA binding in which some target sequences are bound with high affinity (K(d) of nM) and others with low affinity (K(d) of μM). An alignment of the high affinity and low affinity binder sequences identified a consensus binding sequence. The consensus sequence was conserved to a greater degree for the high affinity binder sequences compared to the low affinity binder sequences. The exact bases within the consensus sequence that are crucial for a high affinity binding interaction have not been identified. Here we sought to identify key residues from the consensus sequence that are crucial for a tight binding interaction using a competitive fluorescence anisotropy assay. Systematic mutations were made to a weak binder operator sequence, P(nikR), so that it more closely resembled the consensus sequence and the effect of these mutations on protein-DNA binding was measured. Similarly, mutations that disrupted the consensus sequence were made to a tight binder operator sequence, P(ureA), and their effects on protein-DNA binding were measured. Taken together, these studies implicate thymine 10, located on the 3' end of the palindrome, as crucial for tight binding.

High-Affinity Quasi-Specific Sites in the Genome: How the DNA-Binding Proteins Cope with Them

Many prokaryotic transcription factors home in on one or a few target sites in the presence of a huge number of nonspecific sites. Our analysis of λ-repressor in the Escherichia coli genome based on single basepair substitution experiments shows the presence of hundreds of sites having binding energy within 3 Kcal/mole of the O R1 binding energy, and thousands of sites with binding energy above the nonspecific binding energy. The effect of such sites on DNA-based processes has not been fully explored. The presence of such sites dramatically lowers the occupation probability of the specific site far more than if the genome were composed of nonspecific sites only. Our Brownian dynamics studies show that the presence of quasi-specific sites results in very significant kinetic effects as well. In contrast to λ-repressor, the E. coli genome has orders of magnitude lower quasi-specific sites for GalR, an integral transcription factor, thus causing little competition for the specific site. We propose that GalR and perhaps repressors of the same family have evolved binding modes that lead to much smaller numbers of quasi-specific sites to remove the untoward effects of genomic DNA.

Novel roles of cAMP receptor protein (CRP) in regulation of transport and metabolism of carbon sources.

CRP (cAMP receptor protein), the global regulator of genes for carbon source utilization in the absence of glucose, is the best-studied prokaryotic transcription factor. A total of 195 target promoters on the Escherichia coli genome have been proposed to be under the control of cAMP-bound CRP. Using the newly developed Genomic SELEX screening system of transcription factor-binding sequences, however, we have identified a total of at least 254 CRP-binding sites. Based on their location on the E. coli genome, we predict a total of at least 183 novel regulation target operons, altogether with the 195 hitherto known targets, reaching to the minimum of 378 promoters as the regulation targets of cAMP-CRP. All the promoters selected from the newly identified targets and examined by using the lacZ reporter assay were found to be under the control of CRP, indicating that the Genomic SELEX screening allowed to identify the CRP targets with high accuracy. Based on the functions of novel target genes, we conclude that CRP plays a key regulatory role in the whole processes from the selective transport of carbon sources, the glycolysis-gluconeogenesis switching to the metabolisms downstream of glycolysis, including tricarboxylic acid (TCA) cycle, pyruvate dehydrogenase (PDH) pathway and aerobic respiration. One unique regulation mode is that a single and the same CRP molecule bound within intergenic regions often regulates both of divergently transcribed operons.

C68 from the Sulfolobus islandicus plasmid–virus pSSVx is a novel member of the AbrB-like transcription factor family

The genetic element pSSVx from Sulfolobus islandicus, strain REY15/4, is a hybrid between a plasmid and a fusellovirus. This plasmid-virus hybrid infects several species of the hyperthermophilic acidophilic crenarchaeon Sulfolobus. The open reading frame orfc68 of pSSVx encodes a 7.7 kDa protein that does not show significant sequence homology with any protein with known three-dimensional structure. EMSA (electrophoretic mobility-shift assay) experiments, DNA footprinting and CD analyses indicate that recombinant C68, purified from Escherichia coli, binds to two different operator sites that are located upstream of its own promoter. The three-dimensional structure, solved by a single-wavelength anomalous diffraction experiment on a selenomethionine derivative, shows that the protein assumes a swapped-hairpin fold, which is a distinctive fold associated with a family of prokaryotic transcription factors, such as AbrB from Bacillus subtilis. Nevertheless, C68 constitutes a novel representative of this family because it shows several peculiar structural and functional features.

Efficient Online Transcription Factor Binding Site Adjustment by Integrating Transitive Graph Projection with MoRAine 2.0

SummaryWe investigated the problem of imprecisely determined prokaryotic transcription factor (TF) binding sites (TFBSs). We found that the identification and reinvestigation of questionable binding motifs may result in improved models of these motifs. Subsequent modelbased predictions of gene regulatory interactions may be performed with increased accuracy when the TFBSs annotation underlying these models has been re-adjusted.We present MoRAine 2.0, a significantly improved version of MoRAine. It can automatically identify cases of unfavorable TFBS strand annotations and imprecisely determined TFBS positions. With release 2.0, we close the gap between reasonable running time and high accuracy. Furthermore, it requires only minimal input from the user: (1) the input TFBS sequences and (2) the length of the flanking sequences.Conclusions: MoRAine 2.0 is an easy-to-use, integrated, and publicly available web tool for the re-annotation of questionable TFBSs. It can be used online or downloaded as a stand-alone version from http://moraine.cebitec.uni-bielefeld.de.

Novel sequence-based method for identifying transcription factor binding sites in prokaryotic genomes

Computational techniques for microbial genomic sequence analysis are becoming increasingly important. With next-generation sequencing technology and the human microbiome project underway, current sequencing capacity is significantly greater than the speed at which organisms of interest can be studied experimentally. Most related computational work has been focused on sequence assembly, gene annotation and metabolic network reconstruction. We have developed a method that will primarily use available sequence data in order to determine prokaryotic transcription factor (TF) binding specificities. Specificity determining residues (critical residues) were identified from crystal structures of DNA-protein complexes and TFs with the same critical residues were grouped into specificity classes. The putative binding regions for each class were defined as the set of promoters for each TF itself (autoregulatory) and the immediately upstream and downstream operons. MEME was used to find putative motifs within each separate class. Tests on the LacI and TetR TF families, using RegulonDB annotated sites, showed the sensitivity of prediction 86% and 80%, respectively. http://ural.wustl.edu/∼gsahota/HTHmotif/

ProTF: a comprehensive data and phylogenomics resource for prokaryotic transcription factors

Investigation of transcription factors (TFs) is of extreme significance for gleaning more information about the mechanisms underlying the dynamic transcriptional regulatory network. Herein, proTF is constructed to serve as a comprehensive data resource and phylogenomics analysis platform for prokaryotic TFs. It has many prominent characteristics: (i) detailed annotation information, including basic sequence features, domain organization, sequence homolog and sequence composition, was extensively collected, and then visually displayed for each TF entry in all prokaryotic genomes; (ii) workset was employed as the basic frame to provide an efficient way to organize the retrieved data and save intermediate records; and (iii) a number of elaborated tools for phylogenomics analysis were implemented to investigate the evolutionary roles of specific TFs. In conclusion, proTF dedicates to the prokaryotic TFs with integrated multi-function, which will become a valuable resource for prokaryotic transcriptional regulatory network in the post-genomic era. http://centre.bioinformatics.zj.cn/proTF.

Inclusion of neighboring base interdependencies substantially improves genome-wide prokaryotic transcription factor binding site prediction

Prediction of transcription factor binding sites is an important challenge in genome analysis. The advent of next generation genome sequencing technologies makes the development of effective computational approaches particularly imperative. We have developed a novel training-based methodology intended for prokaryotic transcription factor binding site prediction. Our methodology extends existing models by taking into account base interdependencies between neighbouring positions using conditional probabilities and includes genomic background weighting. This has been tested against other existing and novel methodologies including position-specific weight matrices, first-order Hidden Markov Models and joint probability models. We have also tested the use of gapped and ungapped alignments and the inclusion or exclusion of background weighting. We show that our best method enhances binding site prediction for all of the 22 Escherichia coli transcription factors with at least 20 known binding sites, with many showing substantial improvements. We highlight the advantage of using block alignments of binding sites over gapped alignments to capture neighbouring position interdependencies. We also show that combining these methods with ChIP-on-chip data has the potential to further improve binding site prediction. Finally we have developed the ungapped likelihood under positional background platform: a user friendly website that gives access to the prediction method devised in this work.

Mechanism of target specialization of prokaryotic transcription factors GreA, GreB and DksA

Bacterial transcription factors GreA, GreB and DksA regulate RNA polymerase (RNAP) function by acting through its secondary channel. GreA and GreB stimulate RNAP intrinsic nucleolytic activity, thereby suppressing transcriptional pause and arrest, enhancing transcription fidelity and helping promoter escape. DksA activates and/or inhibits transcription both in vivo and in vitro by destabilizing RNAP open promoter complexes. We characterized the molecular interfaces of GreA‐RNAP and DksA‐RNAP complexes using site‐specific protein‐protein crosslinking and site‐directed mutagenesis. We also determined the binding affinities of Gre/DksA towards different transcription complexes and analyzed their functional activities by in vitro transcription assays. We identified the key residues in Gre, DksA and RNAP secondary channel required for specific Gre/DksA‐RNAP interactions. Based on our data, we created plausible structural models of Gre‐ and DksA‐RNAP complexes. In the models, Gre and DksA bind to RNAP β’ N‐terminal coiled‐coil element. However, the contact residues for Gre and DksA on β’ are distinct. DksA coiled‐coil interacts with the β’ conserved G‐loop element. The conformational changes in the G‐loop define the binding of GreA to RNAP. Unlike Gre factors, DksA does not bind to elongation complexes and does not compete with GreA during transcription. These results indicate that Gre and DksA target different classes of transcription complexes in vivo, most likely, depending on the conformation of the flexible G‐loop element of RNAP.This work was supported by National Institute of Health.

Efficient online transcription factor binding site adjustment by integrating transitive graph projection with MoRAine 2.0.

We investigated the problem of imprecisely determined prokaryotic transcription factor (TF) binding sites (TFBSs). We found that the identification and reinvestigation of questionable binding motifs may result in improved models of these motifs. Subsequent modelbased predictions of gene regulatory interactions may be performed with increased accuracy when the TFBSs annotation underlying these models has been re-adjusted. We present MoRAine 2.0, a significantly improved version of MoRAine. It can automatically identify cases of unfavorable TFBS strand annotations and imprecisely determined TFBS positions. With release 2.0, we close the gap between reasonable running time and high accuracy. Furthermore, it requires only minimal input from the user: (1) the input TFBS sequences and (2) the length of the flanking sequences. MoRAine 2.0 is an easy-to-use, integrated, and publicly available web tool for the re-annotation of questionable TFBSs. It can be used online or downloaded as a stand-alone version from http://moraine.cebitec.uni-bielefeld.de.

Helicobacter pylori NikR’s Interaction with DNA: A Two-Tiered Mode of Recognition

HPNikR is a prokaryotic nickel binding transcription factor found in the virulent bacterium Helicobacter pylori. HPNikR regulates the expression of multiple genes as an activator or repressor, including those involved in nickel ion homeostasis, acid adaptation, and iron uptake. The target operator sequences of the genes regulated by HPNikR do not contain identifiable symmetrical recognition sites, and the mechanism by which HPNikR distinguishes between the genes it regulates is not understood. Using competitive fluorescence anisotropy (FA) and electrophoretic gel mobility shift (EMSA) assays, the interactions between HPNikR and the target operator sequences of the genes directly regulated (ureA, NixA, NikR, Fur OPI, Fur OPII, Frpb4, FecA3, and exbB) were characterized. These studies revealed that HPNikR utilizes a two-tiered mode of DNA recognition by binding to some genes with high affinity and others with low affinity. The genes that are tightly regulated by HPNikR encode proteins that utilize nickel, while those that are less tightly regulated encode other types of proteins. The affinities of low-affinity metal ions for a second metal binding site were determined to be in the micromolar regime, and a contribution of electrostatics to the HPNikR-DNA binding event was determined. Detailed studies of the role of sequence length and identity for the interaction between HPNikR and ureA revealed a specific length requirement for DNA binding.

High-Affinity Ni 2+ Binding Selectively Promotes Binding of Helicobacter pylori NikR to Its Target Urease Promoter

NikR is a prokaryotic transcription factor that regulates the expression of Ni 2 + enzymes and other proteins involved in Ni 2 + trafficking. In the human pathogen Helicobacter pylori, NikR controls transcription of the Ni 2 + enzyme urease, which allows survival of the bacterium in the acidic gastric niche. The in vitro affinity of NikR from H. pylori ( HpNikR) for different metal ions and the metal-ion-dependent capability of HpNikR to bind P ureA , the promoter of the urease operon, were the object of this study. Electrophoretic mobility shift and DNase I footprinting assays indicated that Ni 2+ is necessary and sufficient to promote HpNikR binding to P ureA , while the effect of other metal ions in identical conditions is significantly lower (Zn 2+ and Co 2+) or absent (Ca 2+ and Mg 2+). Isothermal titration calorimetry (ITC) demonstrated the absence of specific Ca 2+ and Mg 2+ binding to the protein. ITC also established the binding of Zn 2+ and Co 2+ to two sets of high-affinity sites on HpNikR, differing in stoichiometry ( n 1 = 2, n 2 = 4) and dissociation constant ( K d 1 = 6 nM, K d2 = 90 nM for Zn 2+; K d 1 = 0.3 μM, K d 2 = 2.7 μM for Co 2+). Additional low-affinity binding sites were observed for Zn 2+ ( n = 8, K d = 1.6 μM). Mobility shift assays and ITC proved that binding of stoichiometric Ni 2+ (but not Zn 2+ or Co 2+) to the high-affinity sites (but not to the low-affinity sites) selectively activates HpNikR to bind its target operator with 1:1 stoichiometry and K d = 56 nM. A protein conformational rearrangement is selectively induced by Ni 2+ and not by Zn 2+, as indicated by fluorescence spectroscopy and microcalorimetry. Accordingly, competition experiments showed that stoichiometric Ni 2+ outperforms Zn 2+, as well as Co 2+, in functionally activating HpNikR toward high affinity binding to P ureA . A general scheme for the nickel-selective HpNikR–DNA interaction is proposed.

Strong negative self regulation of Prokaryotic transcription factors increases the intrinsic noise of protein expression

BackgroundMany prokaryotic transcription factors repress their own transcription. It is often asserted that such regulation enables a cell to homeostatically maintain protein abundance. We explore the role of negative self regulation of transcription in regulating the variability of protein abundance using a variety of stochastic modeling techniques.ResultsWe undertake a novel analysis of a classic model for negative self regulation. We demonstrate that, with standard approximations, protein variance relative to its mean should be independent of repressor strength in a physiological range. Consequently, in that range, the coefficient of variation would increase with repressor strength. However, stochastic computer simulations demonstrate that there is a greater increase in noise associated with strong repressors than predicted by theory. The discrepancies between the mathematical analysis and computer simulations arise because with strong repressors the approximation that leads to Michaelis-Menten-like hyperbolic repression terms ceases to be valid. Because we observe that strong negative feedback increases variability and so is unlikely to be a mechanism for noise control, we suggest instead that negative feedback is evolutionarily favoured because it allows the cell to minimize mRNA usage. To test this, we used in silico evolution to demonstrate that while negative feedback can achieve only a modest improvement in protein noise reduction compared with the unregulated system, it can achieve good improvement in protein response times and very substantial improvement in reducing mRNA levels.ConclusionStrong negative self regulation of transcription may not always be a mechanism for homeostatic control of protein abundance, but instead might be evolutionarily favoured as a mechanism to limit the use of mRNA. The use of hyperbolic terms derived from quasi-steady-state approximation should also be avoided in the analysis of stochastic models with strong repressors.

Ultrasensitive gene regulation by positive feedback loops in nucleosome modification

Eukaryotic transcription involves the synergistic interaction of many different proteins. However, the question remains how eukaryotic promoters achieve ultrasensitive or threshold responses to changes in the concentration or activity of a single transcription factor (TF). We show theoretically that by recruiting a histone-modifying enzyme, a TF binding non-cooperatively to a single site can change the balance between opposing positive feedback loops in histone modification to produce a large change in gene expression in response to a small change in concentration of the TF. This mechanism can also generate bistable promoter responses, allowing a gene to be on in some cells and off in others, despite the cells being in identical conditions. In addition, the system provides a simple means by which the activities of many TFs could be integrated at a promoter.

Structure‐Based Hypothesis on the Activation of the CO‐sensing Transcription Factor CooA

The CooA family of proteins are prokaryotic CO-sensing transcription factors that regulate the expression of genes involved in the utilization of CO as an energy source. They are homodimeric proteins that contain two hemes. Each monomer contains an N-terminal heme-binding domain and a C-­terminal DNA-binding domain. Binding of CO to the heme leads to activation by a large reorientation of the DNA-binding domain such that the DNA-binding domain is in position for specific DNA recognition. The crystal structure of CooA from Rhodospirillum rubrum [RrCooA; Lanzilotta et al. (2000), Nature Struct. Biol. 7, 876–880] in the inactive CO-free off-state shows that the N-terminal Pro residue of monomer A coordinates the heme of monomer B and vice versa. It now appears that the CO replaces the Pro ligand and that this change is coupled to the activation process. However, precisely how the replacement of the Pro ligand by CO results in structural changes some 25 A from the CO-binding site remains unknown. Here, the structure of a CooA variant from the thermophilic bacterium Carboxydothermus hydrogeno­formans (ChCooA) is reported in which one monomer is fully in the on-state. The N-terminal region that is displaced by CO binding is now positioned between the heme-binding and DNA-binding domains, which requires movement of the N-­terminus by ∼20 A and thus serves as a bridge between the two domains that helps to orient the DNA-binding domain in position for DNA binding.

Structure-based hypothesis on the activation of the CO-sensing transcription factor CooA

The CooA family of proteins are prokaryotic CO-sensing transcription factors that regulate the expression of genes involved in the utilization of CO as an energy source. They are homodimeric proteins that contain two hemes. Each monomer contains an N-terminal heme-binding domain and a C-terminal DNA-binding domain. Binding of CO to the heme leads to activation by a large reorientation of the DNA-binding domain such that the DNA-binding domain is in position for specific DNA recognition. The crystal structure of CooA from Rhodospirillum rubrum [RrCooA; Lanzilotta et al. (2000), Nature Struct. Biol. 7, 876-880] in the inactive CO-free off-state shows that the N-terminal Pro residue of monomer A coordinates the heme of monomer B and vice versa. It now appears that the CO replaces the Pro ligand and that this change is coupled to the activation process. However, precisely how the replacement of the Pro ligand by CO results in structural changes some 25 A from the CO-binding site remains unknown. Here, the structure of a CooA variant from the thermophilic bacterium Carboxydothermus hydrogenoformans (ChCooA) is reported in which one monomer is fully in the on-state. The N-terminal region that is displaced by CO binding is now positioned between the heme-binding and DNA-binding domains, which requires movement of the N-terminus by approximately 20 A and thus serves as a bridge between the two domains that helps to orient the DNA-binding domain in position for DNA binding.

Characterization of the Helicobacter pylori NikR−PureA DNA Interaction: Metal Ion Requirements and Sequence Specificity

HPNikR, a prokaryotic nickel binding transcription factor, is found in Helicobacter pylori where it functions as a regulator of multiple genes, including those involved in acid adaptation and nickel ion homeostasis. Particularly important is HPNikR's role in the regulation of the nickel-dependent enzyme urease which is critical for the organism's survival in the acidic environment of the gastric epithelium. The target operator sequences of the genes regulated by HPNikR do not contain any identifiable palindromes, and the exact mechanism(s) of the HPNikR-DNA recognition event is unknown. HPNikR was expressed and purified as a soluble protein containing mixed alpha/beta secondary structure with evidence of a tertiary fold. A direct and competitive fluorescence anisotropy (FA) assay to probe both the metal ion requirements and sequence specificity of HPNikR for PureA, the operator sequence for the urease gene, was developed. FA studies revealed that apo-HPNikR did not bind to PureA while Ni(II)HPNikR bound PureA with nanomolar affinity, but only in the presence of a second metal ion [magnesium, calcium, or manganese(II)], suggesting that HPNikR contains a second, low-affinity metal binding site. Cu(II)HPNikR also exhibited a requirement for a second metal ion to accomplish PureA binding. Removal of a loosely conserved "putative" palindrome sequence in the PureA operator abrogated HPNikR binding. Together, these results support a model of HPNikR-PureA binding in which specific metal ions must be coordinated to high- and low-affinity sites to modulate binding.

Stimulation of the λ p R promoter by Escherichia coli SeqA protein requires downstream GATC sequences and involves late stages of transcription initiation

Escherichia coli SeqA protein is a major negative regulator of chromosomal DNA replication acting by sequestration, and thus inactivation, of newly formed oriC regions. However, other activities of this protein have been discovered recently, one of which is regulation of transcription. SeqA has been demonstrated to be a specific transcription factor acting at bacteriophage lambda promoters p(I), p(aQ) and p(R). While SeqA-mediated stimulation of p(I) and p(aQ) occurs by facilitating functions of another transcription activator protein, cII, a mechanism for stimulation of p(R) remains largely unknown. Here, it has been demonstrated that two GATC sequences, located 82 and 105 bp downstream of the p(R) transcription start site, are necessary for this stimulation both in vivo and in vitro. SeqA-mediated activation of p(R) was as effective on a linear DNA template as on a supercoiled one, indicating that alterations in DNA topology are not likely to facilitate the SeqA effect. In vitro transcription analysis demonstrated that the most important regulatory effect of SeqA in p(R) transcription occurs after open complex formation, namely during promoter clearance. SeqA did not influence the appearance and level of abortive transcripts or the pausing during transcription elongation. Interestingly, SeqA is one of few known prokaryotic transcription factors which bind downstream of the regulated promoter and still act as transcription activators.

Intrinsic Disorder in Transcription Factors

Intrinsic disorder (ID) is highly abundant in eukaryotes, which reflect the greater need for disorder-associated signaling and transcriptional regulation in nucleated cells. Although several well-characterized examples of intrinsically disordered proteins in transcriptional regulation have been reported, no systematic analysis has been reported so far. To test for the general prevalence of intrinsic disorder in transcriptional regulation, we used the predictor of natural disorder regions (PONDR) to analyze the abundance of intrinsic disorder in three transcription factor datasets and two control sets. This analysis revealed that from 94.13 to 82.63% of transcription factors possess extended regions of intrinsic disorder, relative to 54.51 and 18.64% of the proteins in two control datasets, which indicates the significant prevalence of intrinsic disorder in transcription factors. This propensity of transcription factors to intrinsic disorder was confirmed by cumulative distribution function analysis and charge-hydropathy plots. The amino acid composition analysis showed that all three transcription factor datasets were substantially depleted in order-promoting residues and significantly enriched in disorder-promoting residues. Our analysis of the distribution of disorder within the transcription factor datasets revealed that (a) the AT-hooks and basic regions of transcription factor DNA-binding domains are highly disordered; (b) the degree of disorder in transcription factor activation regions is much higher than that in DNA-binding domains; (c) the degree of disorder is significantly higher in eukaryotic transcription factors than in prokaryotic transcription factors; and (d) the level of alpha-MoRF (molecular recognition feature) prediction is much higher in transcription factors. Overall, our data reflected the fact that eukaryotes with well-developed gene transcription machinery require transcription factor flexibility to be more efficient.

Genome-Wide Survey of Transcription Factors in Prokaryotes Reveals Many Bacteria-Specific Families Not Found in Archaea

Assignment of all transcription factors (TFs) from genome sequence data is not a straightforward task due to the wide variation in TFs among different species. A DNA binding domain (DBD) and a contiguous non-DBD with a characteristic SCOP or Pfam domain combination are observed in most members of TF families. We found that most of the experimentally verified TFs in prokaryotes are detectable by a combination of SCOP or Pfam domains assigned to DBDs and non-DBDs. Based on this finding, we set up rules to detect TFs and classify them into 52 TF families. Application of the rules to 154 entirely sequenced prokaryotic genomes detected >18,000 TFs classified into families, which have been made publicly available from the 'GTOP_TF' database. Despite the rough proportionality of the number of TFs per genome with genome size, species with reduced genomes, i.e. obligatory parasites and symbionts, have only a few if any TFs, reflecting a nearly complete loss. Also the number of TFs is significantly lower in archaea than in bacteria. In addition, all but 1 of the 19 TF families present in archaea is present in bacteria, whereas 33 TF families are found exclusively in bacteria. This observation indicates that a number of new TF families have evolved in bacteria, making the transcription regulatory system more divergent in bacteria than in archaea.