PMC:2726282 / 23543-26935 JSONTXT

{"project":"2_test","denotations":[{"id":"18599074-18421347-62517948","span":{"begin":1750,"end":1752},"obj":"18421347"},{"id":"18599074-18158297-62517949","span":{"begin":2616,"end":2618},"obj":"18158297"},{"id":"18599074-12547520-62517950","span":{"begin":3172,"end":3174},"obj":"12547520"},{"id":"18599074-11701124-62517950","span":{"begin":3172,"end":3174},"obj":"11701124"},{"id":"18599074-15819616-62517950","span":{"begin":3172,"end":3174},"obj":"15819616"},{"id":"18599074-14527278-62517950","span":{"begin":3172,"end":3174},"obj":"14527278"},{"id":"18599074-15187183-62517950","span":{"begin":3172,"end":3174},"obj":"15187183"}],"text":"Conclusions\nOur structural analysis of the transcriptional cross-regulatory network in E. coli suggests that regulatory interactions between TFs are predominantly positive, while autoregulatory interactions are mostly negative. However, this general trend appears to be reversed in the case of most downstream TFs involved in the regulation of biofilm/chemotaxis modules.\nWe also note that there are striking topological differences between the subnetwork controlling metabolic activities, such as carbon metabolism, and that controlling developmental processes; the former encompasses many parallel short transcriptional cascades and multiple FFLs, each enabling the use of one alternative carbon source, while the latter involves long and intertwined regulatory cascades. These long transcriptional cascades typically include multiple autoactivated intermediate TFs, as well as regulatory circuits between TFs and sigma factors in the case of biofilm formation.\nWe further observe that TFs acting at the end of these regulatory cascades often belong to two-component systems. This topology suggests that cell homeostasy is maintained through multiple regulatory cascades with commonly autorepressed TFs, while the regulatory memory within the network is preserved by the sequential activation of TFs and by multi-element circuits at the core of the network. Downstream of the hierarchical network, two-component systems can memorise transient external signals through autoactivation loops, thus acting as molecular switches enabling the coexistence of alternative phenotypes.\nAs shown in a recent study, the E. coli cross-regulatory network appears to be robust enough to tolerate the rewiring between members high and low in the network hierarchy.66 This study also indicates that the allosteric signals are the mandatory input elements for network function. Thus, TFs present in a condition different from the natural one(s) would have limited activity due to the absence of their effector signals.\nIn this respect, a proper global understanding of the organisation of the E. coli transcriptional network (combining sigma and TFs) could contribute to the interpretation of network-rewiring experiments as well as foster more efficient design of synthetic regulatory circuits.\nThe general significance of the observed organisation of the E. coli transcriptional cross-regulatory network remains to be assessed. A more comprehensive picture of the network organisation in bacteria will progressively be drawn as additional regulatory elements such as small RNAs, anti-sigma factors and riboswitches are integrated.70 In addition, the combination of transcriptional and metabolic networks should provide important insights by linking effector metabolites and regulatory elements. Clearly, variations in regulatory network topology might be expected in the case of bacteria with asymmetric cell division (mostly α-proteobacteria), where the offspring asymmetric cells cause a transient genetic asymmetry that triggers different developmental processes, such as the formation of stalked and swarmer cells in Caulabacter or vegetative and spore-forming cells in Bacillus.71–75 Future comparisons between network topologies for different model systems should further enhance our understanding of regulatory network organisation and its conservation or variations among different bacterial phyla."}