Identifying global regulators in transcriptional regulatory networks in bacteria
Introduction
The goal of modeling an entire E. coli cell has been set recently by a team of researchers [1]. We feel a more simplistic approach to modeling the behavioral repertoire of a cell would be to analyze a subset of interactions and molecular components of the cell responsible for cellular decisions at the level of transcription initiation. Transcription factors (TFs) have been described by François Jacob as ‘two-headed molecules’ [2] consisting of a DNA-binding site and an allosteric–metabolite interaction. Metabolites or covalent modifications link the sensing of extracellular stimuli with intracellular stimuli in the switch governing the expression or repression of genes, operons and regulons [3]. The relationship between sensing stimuli and deciding a pattern of expression involves the interplay of transporters, signal transduction mechanisms, thresholds of interactions, together with the organization of genes into operons, regulons and stimulons, and the control of chromosome structure 4., 5..
Global regulators have been previously defined by Gottesman [6] on the basis of their pleiotropic phenotype and their ability to regulate operons that belong to different metabolic pathways. This definition excludes proteins involved in the essential cellular machinery. In this review, using what we currently know about transcriptional regulation in E. coli, we analyze different properties of TFs with a genomic perspective, and determine if a set of diagnostic criteria can be identified to provide an explicit way of distinguishing a global regulator from a local or dedicated regulator. The transcriptional network of E. coli is probably the best known for any cell, with 4405 ORFs identified [7] and an estimated 8%, or roughly 300 genes as predicted or known TFs [8]. RegulonDB, a database with information on transcriptional regulation and operon organization in E. coli [9], contains experimental evidence on regulation from 105 regulators affecting 749 genes. A large number of mapped promoters and sites, as well as operons, complete the description of around 20% of the regulatory interactions of transcription in the cell. The complexity of the network of currently known interactions is shown in Figure 1.
Section snippets
Number of genes regulated by transcription factors
It is surprising to know that seven regulatory proteins (CRP, FNR, IHF, FIS, ArcA, NarL and Lrp) are sufficient for directly modulating the expression of 51% of genes in E. coli, see Table 1, column 2. This large influence of so few regulators is consistent with the general statistical properties of this network, the connectivity of which follows a power-law distribution [10]. In a power-law network, very few nodes have a large number of connections, whereas many nodes interact with few others
Frequency of co-regulation
As mentioned above, the expression of many genes is modulated by only a few regulators; however, the regulation by multiple TFs occurs in 49% of genes and in most cases, it seems that a global regulator works together with more specific local regulators (Table 1, column 3). Specific examples of co-regulation include, the melAB promoter by CRP and MelR [12], ansB promoters by CRP and FNR [13], cytR regulon by CytR and CRP [14•], proP2 promoter by CRP and FIS co-activation [15••], metY by CRP and
Regulation of transcription factors and club co-regulation
In a process of decisions and information flux, the number of controlled or affected elements is not the only factor to be considered. A hierarchy of different levels of decision is natural to our understanding of how things get done [26]. One may consider that architecturally, the highest level in a hierarchy would be σ factors. Certainly, σ70 transcribes an enormous number of genes (830 genes compared with 31 for σ54), as currently annotated in RegulonDB, and is therefore known for its
Transcriptional requirements of σ factors
DNA-dependent RNA polymerase is the enzyme responsible for all cellular mRNA synthesis in E. coli [28]. In addition to the major housekeeping sigma factor σ70, E. coli has six other σ factors 29., 30.. Changes in the synthesis of σ factors, or competition of different σ factors to bind to the core RNA-polymerase, even if in a delayed fashion [31], cause different programs of gene expression to be induced and repressed. In some cases this is a global mechanism for differential gene expression
Response to changes in environmental conditions
The second function of transcription factors is to sense changes in environmental conditions or other internal signals encoding changes. Bacteria constantly monitor extracellular physicochemical conditions, so that they can respond by modifying their gene expression patterns to adjust growth 38., 39.. The link between changes in environmental conditions and changes in transcriptional regulation involves signal-transduction pathways, which may involve the direct production of an isomer such as
Autoregulation and isolated transcription units
CRP is auto-regulated both positively and negatively. Lrp, FIS, IHF, Hns, FNR, Fur, PurR and SoxR among others, are negatively auto-regulated. In fact, auto-regulation is quite frequent in TFs. 55% (58 of 105) of all TFs known are auto-regulated, and of these 68% are negatively auto-regulated, 29% are positively auto-regulated (only PhoB of the TFs in Table 1) and 3% are auto-regulated both positively and negatively. Thus, auto-regulation is not limited to global regulators. Negative
Global transcription factors
Babu and Teichmann [42••] and Shen-Orr et al. [50••] have identified global regulators in E. coli (see Table 1). Seven of them satisfy our diagnostic criteria to be considered true global regulators; CRP, IHF, FNR, FIS, ArcA, Lrp and Hns. CRP is the master regulatory protein that senses the energetic status of the cell by cAMP levels 43., 51.•. FNR and ArcA are directly related to energy production by regulating respiratory modes. Lrp monitors the general nutritional state by sensing l-leucine
Concluding remarks
This network of two-headed molecules that sense specificities of the environment, together with particular (overlapping) subsets of co-regulated genes that are expressed or repressed, describes what the cell has implemented to respond to the changes in the environment given its history in evolution. The armament for our understanding of regulatory networks, co-regulated groups, clusters and their overlapping genes, has recently increased beyond the operons, regulons and stimulons, to
References and recommended reading
Papers of particular interest, published within the annual period of review, have been highlighted as:
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of special interest
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of outstanding interest
Acknowledgements
We apologize colleagues whose research was not mentioned here because of space restrictions. We acknowledge support by NIH grant number GM62205-02, and grant number 0028 from CONACYT-Mexico. We acknowledge Steve Busby for his encouragement on the writing of this review, Heladia Salgado, RM Gutiérrez and Socorro Gama for fruitful discussions and, Verónica Jiménez, Edgar Dı́az and Fabiola Sánchez for their computer support.
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