Evolutionary dynamics of prokaryotic transcriptional regulatory networks.
The structure of complex transcriptional regulatory networks has been studied extensively in certain model organisms. However, the evolutionary dynamics of these networks across organisms, which would reveal important principles of adaptive regulatory changes, are poorly understood. We use the known transcriptional regulatory network of Escherichia coli to analyse the conservation patterns of this network across 175 prokaryotic genomes, and predict components of the regulatory networks for these organisms. We observe that transcription factors are typically less conserved than their target genes and evolve independently of them, with different organisms evolving distinct repertoires of transcription factors responding to specific signals. We show that prokaryotic transcriptional regulatory networks have evolved principally through widespread tinkering of transcriptional interactions at the local level by embedding orthologous genes in different types of regulatory motifs. Different transcription factors have emerged independently as dominant regulatory hubs in various organisms, suggesting that they have convergently acquired similar network structures approximating a scale-free topology. We note that organisms with similar lifestyles across a wide phylogenetic range tend to conserve equivalent interactions and network motifs. Thus, organism-specific optimal network designs appear to have evolved due to selection for specific transcription factors and transcriptional interactions, allowing responses to prevalent environmental stimuli. The methods for biological network analysis introduced here can be applied generally to study other networks, and these predictions can be used to guide specific experiments.
Madan Babu
M
M
National Center for Biotechnology Information, National Institutes of Health, MD 20894, USA. madanm@mrc-lmb.cam.ac.uk
Teichmann
Sarah A
SA
Aravind
L
L
D020816:Amino Acid Motifs; D001412:Bacillus subtilis / Q000235:genetics; D016000:Cluster Analysis; D019295:Computational Biology; D004926:Escherichia coli / Q000235:genetics*; D029968:Escherichia coli Proteins / Q000235:genetics*; D019143:Evolution, Molecular; D005786:Gene Expression Regulation; D016678:Genome; D007255:Information Services; D008954:Models, Biological; D011387:Prokaryotic Cells / Q000502:physiology*; D014158:Transcription, Genetic
D029968:Escherichia coli Proteins