Network motifs in the transcriptional regulation network of Escherichia coli

Abstract

Little is known about the design principles 1-10 of transcriptional regulation networks that control gene expression in cells. Recent advances in data collection and analysis 2,11,12 , however, are generating unprecedented amounts of information about gene regulation networks. To understand these complex wiring diagrams 1-10,13 , we sought to break down such networks into basic building blocks 2 . We generalize the notion of motifs, widely used for sequence analysis, to the level of networks. We define 'network motifs' as patterns of interconnections that recur in many different parts of a network at frequencies much higher than those found in randomized networks. We applied new algorithms for systematically detecting network motifs to one of the best-characterized regulation networks, that of direct transcriptional interactions in Escherichia coli 3,6 . We find that much of the network is composed of repeated appearances of three highly significant motifs. Each network motif has a specific function in determining gene expression, such as generating temporal expression programs and governing the responses to fluctuating external signals. The motif structure also allows an easily interpretable view of the entire known transcriptional network of the organism. This approach may help define the basic computational elements of other biological networks.We compiled a data set of direct transcriptional interactions between transcription factors and the operons they regulate (an operon is a group of contiguous genes that are transcribed into a single mRNA molecule). This database contains 577 interactions and 424 operons (involving 116 transcription factors); it was formed on the basis of on an existing database (RegulonDB) 3,14 . We enhanced RegulonDB by an extensive literature search, adding 35 new transcription factors, including alternative σ-factors (subunits of RNA polymerase that confer recognition of specific promoter sequences). The data set consists of established interactions in which a transcription factor directly binds a regulatory site.The transcriptional network can be represented as a directed graph, in which each node represents an operon and edges represent direct transcriptional interactions. Each edge is directed

Gene regulatory network growth by duplication

“…A number of topological features are common to the gene regulatory networks in E. coli and yeast 2,3 . A key common feature is that the number of target genes per transcription factor roughly obeys a power law, which is typical of 'scale-free' networks 19 (Fig.…”

“…These are sets of interactions connected in specific patterns called 'network motifs' 1,2,20 . These motifs have been engineered artificially 21,22 , but here we addressed how they were formed during evolution.…”

“…2a), or it may recognize a new binding site upstream of some other target gene(s). Investigation of the known network in both organisms 2,3,7 showed that duplication of transcription factor genes followed by inheritance of interaction has contributed considerably to the growth of the regulatory network: more than two-thirds of E. coli (77%) and yeast (69%) transcription factors have at least one interaction in common with their duplicates (Table 1). This accounts for 128 interactions (10%) in E. coli and 188 interactions (22%) in yeast ( Fig.…”

Just-in-time transcription program in metabolic pathways

“…The genes encoding these enzymes are governed by a transcriptional regulatory network 12,13 , which is an excellent model system for studying the design principles of metabolic regulation. To study the dynamics of transcription of AAB genes at high temporal resolution and accuracy, we constructed a library of 52 reporter strains that represent ∼50% of known AAB genes.…”

Bayesian statistical modelling of human protein interaction network incorporating protein disorder information

“…Various types of network motifs significantly enriched as compared to randomized networks were revealed by the study of transcription regulatory network of E.coli : feed-forward loops (FFL), single-input modules (SIM), dense overlapping regulons (DOR) [63]. The ERGM modelling of networks offers a natural way of assessing importance of the network motifs.…”