PMC:2726282 / 2865-9081 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/2726282","sourcedb":"PMC","sourceid":"2726282","source_url":"https://www.ncbi.nlm.nih.gov/pmc/2726282","text":"Topology of E. coli cross-regulatory transcriptional network\nAvailable experimental data point to more than 3000 regulatory interactions between TFs and their regulated genes in E. coli. This information is integrated and documented in a specialised database called RegulonDB.8 Global analyses of this huge network have already been published, emphasising a hierarchical organisation and statistically overrepresented regulatory motifs.9–11 Here, our aim is to analyse the flow of regulatory information within the network of transcriptional interactions among TFs and sigmas (E. coli transcriptional cross-regulatory network). This network encompasses 115 TFs and 7 sigma factors, i.e., around one-third of the total predicted TF proteins in this bacterium (Fig. 1).12,13 On average, every TF is connected to two other TFs (i.e., more technically, the mean degree of the regulatory graph is 2.74). However, the connectivity distribution of TFs is not uniform, with a small fraction of global TFs with high out-degrees dominating the network.15 Seven global regulators were defined previously based on a collection of criteria:15 (i) number of regulated genes; (ii) number of regulated genes encoding for TFs; (iii) propensity of cooperative regulation of targets with the aid of other TFs; (iv) ability to directly affect the expression of a variety of promoters that use different sigma factors; (v) belonging to evolutionary families with few paralogs; and (vi) heterogeneity of the functional classes of the regulated genes.\nIn order to better visualise the informational flow through the network, the following graphical conventions have been used in Fig. 1 (see also legend): (i) the size of the nodes representing TFs is proportional to the number of genes they regulate [e.g., cAMP receptor protein (CRP) regulates 413 genes and is represented by the second biggest node, after the housekeeping sigma factor rpoD]; (ii) arrows and colours refer to the direction and sign of the regulatory interaction; (iii) arrow thickness is proportional to the impact of the interaction, computed as the number of genes thereby (in)directly regulated.\nThe majority of the TFs in this network are autoregulated (∼ 70%), of which about two-thirds account for negative loops (see Table 1). This finding is consistent with the results of an analysis performed with a much smaller number of TFs about 10 years ago.17 This predominance of negative autoregulatory loops contrasts with the predominance of positive arcs between different TFs (about 54%, see Table 1). The dominance of positive regulatory interactions in the regulatory network of E. coli is not limited to those among TFs, as when we compute the regulation of all the target genes (3017 arcs) we found that about 54% (1630) are positively regulated, 40% (1206) are repressed, while about 6% (171) are dual regulated. This is especially interesting because a majority of the TFs in bacteria have been reported to act as repressors.12,18,19 The conventions used in Fig. 1 clearly display the hierarchical organisation of the network, with master regulators such as CRP, fumarate and nitrate regulatory protein (FNR) or integration host factor (IHF) each (in)directly regulating a large number of other TFs. Furthermore, the layout emphasises important variations regarding the length of the transcriptional cascades.\nAlthough functional annotations on TFs are still limited, it is possible to classify the cross-regulating TFs into broad categories according to the physiological functions of the target structural genes: carbohydrate initial catabolism, respiration, biofilm formation and chemotaxis, etc. As shown in Fig. 1, these broad classes correspond to different local network topologies. Due to their contrasting topologies, in what follows, we will focus our discussion on short regulatory cascades observed in the case of carbohydrate catabolism as opposed to long regulatory cascades seen in the case of biofilm and chemotaxis pathways (Fig. 2a). CRP resides at the top of both subnetworks. CRP is the only global TF acting hierarchically over local TFs for the usage of carbohydrates, whereas CRP's activity is comparable to the activity of other global regulators in the rest of the network. Note that the concentration of its effector metabolite, cyclic adenosine monophosphate (cAMP), is at par with that of adenosine triphosphate (ATP), which acts as the energetic currency of the cell.20 This suggests that CRP not only regulates the use of these substrates for producing ATP, but also senses the energetic status of the cell to decide the execution of other cellular programs.\nThis study aims at understanding the network structure in relation to physiological roles played by the different modules, focusing on differences in the topologies of the subnetworks controlling metabolism versus motility and chemotaxis (cf. the following sections).\nHowever, other subnetworks are also worth mentioning. In particular, all nine TFs controlling the expression of genes for amino acid biosynthesis seem to be expressed constitutively by sigma 70. Each TF regulates the transcription of the required genes for producing different amino acids. At high concentrations of the amino acids, allosteric modifications of TFs follow binding to their respective amino acids, resulting in TF autorepression as well as to the repression of the corresponding biosynthetic genes. Interestingly, the logic behind negative autoregulation in this case is different from that of the catabolism of carbohydrates. While in the latter case TFs are autorepressed until the substrate is available, in the case of amino acids, TFs are autorepressed only in the presence of an excess of the synthesized final product. Another interesting subnetwork is that for alleviating the stresses by drugs, solvents and weak organic acids. The regulatory logic in this complex subnetwork is peculiar, as their components form multi-element circuits (see Fig. 3) and their inputs are directed by Rob and SoxR, two small proteins constitutively expressed but with very short half-lives (1–2 min). Their stability/degradation depends on the presence/absence of their effector signals.21,22,23","divisions":[{"label":"title","span":{"begin":0,"end":60}},{"label":"p","span":{"begin":61,"end":1528}},{"label":"p","span":{"begin":1529,"end":2145}},{"label":"p","span":{"begin":2146,"end":3367}},{"label":"p","span":{"begin":3368,"end":4646}},{"label":"p","span":{"begin":4647,"end":4914}}],"tracks":[{"project":"2_test","denotations":[{"id":"18599074-16381895-62517914","span":{"begin":276,"end":277},"obj":"16381895"},{"id":"18599074-11967538-62517915","span":{"begin":438,"end":440},"obj":"11967538"},{"id":"18599074-15604458-62517915","span":{"begin":438,"end":440},"obj":"15604458"},{"id":"18599074-10734204-62517916","span":{"begin":770,"end":772},"obj":"10734204"},{"id":"18599074-12582243-62517916","span":{"begin":770,"end":772},"obj":"12582243"},{"id":"18599074-14572541-62517917","span":{"begin":1042,"end":1044},"obj":"14572541"},{"id":"18599074-14572541-62517918","span":{"begin":1127,"end":1129},"obj":"14572541"},{"id":"18599074-9670816-62517919","span":{"begin":2403,"end":2405},"obj":"9670816"},{"id":"18599074-10734204-62517920","span":{"begin":2989,"end":2991},"obj":"10734204"},{"id":"18599074-16772031-62517920","span":{"begin":2989,"end":2991},"obj":"16772031"},{"id":"18599074-10412974-62517920","span":{"begin":2989,"end":2991},"obj":"10412974"},{"id":"18599074-17675376-62517921","span":{"begin":4454,"end":4456},"obj":"17675376"},{"id":"18599074-15465042-62517922","span":{"begin":6214,"end":6216},"obj":"15465042"},{"id":"18599074-10672184-62517922","span":{"begin":6214,"end":6216},"obj":"10672184"},{"id":"18599074-15504398-62517922","span":{"begin":6214,"end":6216},"obj":"15504398"}],"attributes":[{"subj":"18599074-16381895-62517914","pred":"source","obj":"2_test"},{"subj":"18599074-11967538-62517915","pred":"source","obj":"2_test"},{"subj":"18599074-15604458-62517915","pred":"source","obj":"2_test"},{"subj":"18599074-10734204-62517916","pred":"source","obj":"2_test"},{"subj":"18599074-12582243-62517916","pred":"source","obj":"2_test"},{"subj":"18599074-14572541-62517917","pred":"source","obj":"2_test"},{"subj":"18599074-14572541-62517918","pred":"source","obj":"2_test"},{"subj":"18599074-9670816-62517919","pred":"source","obj":"2_test"},{"subj":"18599074-10734204-62517920","pred":"source","obj":"2_test"},{"subj":"18599074-16772031-62517920","pred":"source","obj":"2_test"},{"subj":"18599074-10412974-62517920","pred":"source","obj":"2_test"},{"subj":"18599074-17675376-62517921","pred":"source","obj":"2_test"},{"subj":"18599074-15465042-62517922","pred":"source","obj":"2_test"},{"subj":"18599074-10672184-62517922","pred":"source","obj":"2_test"},{"subj":"18599074-15504398-62517922","pred":"source","obj":"2_test"}]}],"config":{"attribute types":[{"pred":"source","value type":"selection","values":[{"id":"2_test","color":"#9e93ec","default":true}]}]}}