title: General trends in the evolution of prokaryotic transcriptional regulatory networks.
abstract: Gene expression in organisms is controlled by regulatory proteins termed transcription factors, which recognize and bind to specific nucleotide sequences. Over the years, considerable information has accumulated on the regulatory interactions between transcription factors and their target genes in various model prokaryotes, such as Escherichia coli and Bacillus subtilis. This has allowed the representation of this information in the form of a directed graph, which is commonly referred to as the transcriptional regulatory network. The network representation provides us with an excellent conceptual framework to understand the structure of the transcriptional regulation, both at local and global levels of organization. Several studies suggest that the transcriptional network inferred from model organisms may be approximated by a scale-free topology, which in turn implies the presence of a relatively small group of highly connected regulators (hubs or global regulators). While the graph theoretical principles have been applied to infer various properties of such networks, there have been few studies that have actually investigated the evolution of the transcriptional regulatory networks across diverse organisms. Using recently developed computational methods that exploit various evolutionary principles, we have attempted to reconstruct and compare these networks across a wide-range of prokaryotes. This has provided several insights on the modification and diversification of network structures of various organisms in course of evolution. Firstly, we observed that target genes show a much higher level of conservation than their transcriptional regulators. This in turn suggested that the same set of functions could be differently controlled across diverse organisms, contributing significantly to their adaptive radiations. In particular, at the local level of network structure, organism-specific optimization of the transcription network has evolved primarily via tinkering of individual regulatory interactions rather than whole scale reuse or deletion of network motifs (local structure). In turn, as phylogenetic diversification proceeds, this process appears to have favored repeated convergence to scale-free-like structures, albeit with different regulatory hubs.
authors.affiliation: National Center for Biotechnology Information, National Institutes of Health, Bethesda, Md., USA.
authors.forename: M Madan
authors.initials: MM
authors.lastname: Babu
authors.forename: S
authors.initials: S
authors.lastname: Balaji
authors.forename: L
authors.initials: L
authors.lastname: Aravind