Hierarchical structure and modules in the Escherichia coli transcriptional regulatory network revealed by a new top-down approach
© Ma et al; licensee BioMed Central Ltd. 2004
Received: 28 July 2004
Accepted: 16 December 2004
Published: 16 December 2004
Cellular functions are coordinately carried out by groups of genes forming functional modules. Identifying such modules in the transcriptional regulatory network (TRN) of organisms is important for understanding the structure and function of these fundamental cellular networks and essential for the emerging modular biology. So far, the global connectivity structure of TRN has not been well studied and consequently not applied for the identification of functional modules. Moreover, network motifs such as feed forward loop are recently proposed to be basic building blocks of TRN. However, their relationship to functional modules is not clear.
In this work we proposed a top-down approach to identify modules in the TRN of E. coli. By studying the global connectivity structure of the regulatory network, we first revealed a five-layer hierarchical structure in which all the regulatory relationships are downward. Based on this regulatory hierarchy, we developed a new method to decompose the regulatory network into functional modules and to identify global regulators governing multiple modules. As a result, 10 global regulators and 39 modules were identified and shown to have well defined functions. We then investigated the distribution and composition of the two basic network motifs (feed forward loop and bi-fan motif) in the hierarchical structure of TRN. We found that most of these network motifs include global regulators, indicating that these motifs are not basic building blocks of modules since modules should not contain global regulators.
The transcriptional regulatory network of E. coli possesses a multi-layer hierarchical modular structure without feedback regulation at transcription level. This hierarchical structure builds the basis for a new and simple decomposition method which is suitable for the identification of functional modules and global regulators in the transcriptional regulatory network of E. coli. Analysis of the distribution of feed forward loops and bi-fan motifs in the hierarchical structure suggests that these network motifs are not elementary building blocks of functional modules in the transcriptional regulatory network of E. coli.
Genome sequencing and high-throughput technologies of functional genomics generate a huge amount of information about cellular components and their functions in an unprecedented pace. These advances make it possible to reconstruct large scale biological networks (metabolism, gene regulation, signal transduction, protein-protein interaction etc.) at a whole cell level [1–4]. One of the key issues in the contemporary genomic biology is to understand the structure and function of these cellular networks at different molecular levels. Among them, the transcriptional regulatory network (TRN) plays a central role in cellular function because it regulates gene expression and metabolism and is often the final step of signal transduction [5, 6]. Genome scale TRNs have been reconstructed for well studied organisms such as Escherichia coli and Saccharomyces cerevisiae [4, 5, 7, 8]. Recent studies of TRNs have been concentrated on the topological structure and its correlation with gene expression data from microarray experiments, the evolutionary relationship between regulators, the network motifs and the global regulators in the network etc [7–15]. Network motifs are regarded as the basic building blocks of complex networks [16, 17]. Feed forward loop (FF loop) and Bi-fan motif were found to be the two most important network motifs in TRN . In a recent study, Dobrin et al.  reported that the motifs in E. coli TRN aggregated into homologous motif clusters that largely overlapped with known biological functions and further formed a giant motif supercluster which comprised about half of the nodes in the giant component of the whole network. This study provided interesting information for understanding the organization principle of regulatory networks. A different approach for studying network organization is the so called "top-down view". It starts from the whole network structure and identifies subsystems or modules by network decomposition. It is generally recognized that most cellular functions are coordinately carried out by groups of genes forming functional modules [19–25]. The identification of modules is thus an essential step for obtaining any testable biological hypotheses from the network structure. Several methods have been proposed to detect modules in metabolic networks and protein-protein interaction networks based on the topology of the network [21, 26–30]. As shown in our recent work  the global connectivity structure of metabolic network was useful for a more reasonable decomposition of it into functional modules. However, the global structure of TRN has been so far not taken into account in its decomposition. In fact, little is known about the global connectivity structure of TRN.
In this work we demonstrated the applicability of a top-down approach for the identification of functional modules in TRN with the well established transcriptional regulatory network of E. coli as an example. For this purpose we first showed an uncovered global hierarchical structure. Global regulators and modules with clearly defined functions were then identified by a new network decomposition method based on the hierarchical structure. We further investigated the distribution of the two basic network motifs, feed forward loop and bi-fan motif, in the network hierarchical structure and examined their relationship to functional modules.
Results and discussion
The hierarchical structure of regulatory network
The multi-layer hierarchical structure of the E. coli TRN implies that no feed back regulation exists at transcription level. We noticed that Shen-Orr et al have also reported that there was no feed back loop in the E. coli regulatory network . We further examined the yeast regulatory network proposed by Guelzim et al  and found it also has a similar hierarchical structure (result not shown). This gives rise to the question why the transcriptional regulatory networks of these organisms possess such an acyclic hierarchical structure. A possible explanation is that the interactions in TRN are interactions between proteins and DNAs. Therefore, a regulated gene must has been transcribed and translated into its protein product (which is eventually further modified by cofactor binding) to make a feedback interaction between it and its regulator gene possible. The well studied lac operon may be used as an example to further illustrate this point. Lac operon is not expressed unless lactose is available for the cell because it is repressed by the lac repressor. Lac repressor (the protein but not the gene) is the control element of the system. Its existence (expression) is necessary for the cell to properly response to environmental changes (i.e. the presence and absence of lactose). Therefore, for cells to quickly and properly response to changes of environmental conditions it is of advantage to keep a set of proteins expressed in all conditions and through them to regulate the expression of other genes in a hierarchical way. Feedback control of gene expression may be mainly through other interactions (e.g. metabolite and protein interaction) rather than through transcriptional interactions between proteins and genes. In fact, many transcription factors can bind small molecules to gain or loss their ability to bind DNA.
The five-layer hierarchy shown in Fig. 1B does not necessarily mean that TFs at the top layer require 4 steps to regulate operons at the bottom layer. In fact, many operons at the bottom layer are directly regulated by top layer TFs. Among the 717 linked pairs of operons, 516 are directly connected. The average path length of the network is only 1.36, suggesting a fast and efficient response of cells to environment perturbations in general. The longest regulation path in the network is IHF → OmpR → FlhDC → FliA and further to seven operons (marked yellow in Fig. 1B) related to flagella motility. The finding that there is no short-cut between these regulators and the regulated operons is unexpected. Regulatory relationships may exist between them but are not yet identified. Actually five operons that are regulated by FliA are also directly regulated by FlhDC, resulting in a shorter path between the upper layer regulators (FlhDC) and these operons.
Network decomposition, global regulators and modules
Based on the uncovered hierarchical organization structure we propose a new method to identify functional modules in TRN. As discussed above, there is a giant weakly connected component in the whole TRN of E. coli. We find that the giant component preserves the five layer hierarchical structure of the whole network. It also includes the single large motif super cluster found by Dorbin et al.  and thus preserves most of the network motifs in the whole network. Therefore, in the subsequent steps we focus on this giant component to present a new method to identify global regulators and modules in the network.
Global regulators and their regulated operons and functions in the regulatory network of E. coli.
directly regulated Operons
Total regulated operons
integration host factor
Cold shock protein
cAMP receptor protein
anaerobic regulator, regulatory gene for nitrite and nitrate reductases, fumarate reductase
DNA-binding global regulator; involved in chromosome organization; preferentially binds bent DNA
Response regulator for osmoregulation; regulates production of membrane proteins
RNA polymerase sigma 54 subunit
stationary phase sigma factor
Response regulator protein represses aerobic genes under anaerobic growth conditions and activates some anaerobic genes
Two-component regulator protein for nitrate/nitrite response
Functional investigation of modules identified.
Biological function description
aceBAK, acs, adhE, fruBKA, fruR, icdA, iclMR, mlc, ppsA, ptsG, ptsHI_crr, pykF
Hexose PTS transport system, PEP generation, Acetate usage, glyoxylate shunt
acnA, fpr, fumC, marRAB, nfo, sodA, soxR, soxS, zwf
Oxidative stress response
ada_alkB, aidB, alkA, ahpCF, dps, gorA, katG, oxyR
Oxidative stress response, Alkylation
alaWX, aldB, argU, argW, argX_hisR_leuT_proM, aspV, dnaA, leuQPV, leuX, lysT_valT_lysW, metT_leuW_glnUW_metU_glnVX, metY_yhbC_nusA_infB, nrdAB, pdhR_aceEF_lpdA, pheU, pheV, proK, proL, proP, sdhCDAB_b0725_sucABCD, serT, serX, thrU_tyrU_glyT_thrT, thrW, tyrTV, valUXY_lysV, yhdG_fis
rRNA, tRNA genes, DNA synthesis system, pyruvate dehydrogenase and ketoglutarate dehydrogenase system
araBAD, araC, araE, araFGH, araJ
Arabinose uptake and usage
argCBH, argD, argE, argF, argI, argR, carAB
Arginine usage, urea cycle
caiF, caiTABCDE, fixABCX
clpP, dnaKJ, grpE, hflB, htpG, htpY, ibpAB, lon, mopA, mopB, rpoH
Heat shock response
codBA, cvpA_purF_ubiX, glnB, glyA, guaBA, metA, metH, metR, prsA, purC, purEK, purHD, purL, purMN, purR, pyrC, pyrD, speA, ycfC_purB, metC, metF, metJ
Purine synthesis, purine and pyrimidine salvage pathway, methionine synthesis
cpxAR, cpxP, dsbA, ecfI, htrA, motABcheAW, ppiA, skp_lpxDA_fabZ, tsr, xprB_dsbC_recJ
Stress response, Conjugative plasmid expression, cell motility and Chemotaxis
dctA, dcuB_fumB, frdABCD, yjdHG
C4 dicarboxylate uptake
edd_eda, gntKU, gntR, gntT
Gluconate usage, ED pathway
csgBA, csgDEFG, envY_ompT, evgA, gcvA, gcvR, gcvTHP, gltBDF, ilvIH, kbl_tdh, livJ, livKHMGF, lrp, lysU, ompC, ompF, oppABCDF, osmC, sdaA, serA, stpA
Amino acid uptake and usage
fdhF, fhlA, hycABCDEFGH, hypABCDE
Formate hydrogenlyase system
flgAMN, flgBCDEFGHIJ, flgKL, flgMN, flhBAE, flhDC, fliAZY, fliC, fliDST, fliE, fliFGHIJK, fliLMNOPQR, tarTapcheRBYZ
Flagella motility system
ftsQAZ, rcsAB, wza_wzb_b2060_wcaA_wcaB
Capsule synthesis, cell division
gdhA, glnALG, glnHPQ, nac, putAP
Glutamine and proline utilization
glmUS, manXYZ, nagBACD, nagE
Glucosamine, mannose utilization
glpACB, glpD, glpFK, glpR, glpTQ
Glycerol phosphate utilization
lysA, lysR, tdcABCDEFG, tdcR
Serine, threonine usage
malEFG, malK_lamB_malM, malPQ, malS, malT, malZ
rhaBAD, rhaSR, rhaT
appCBA, appY, betIBA, betT, cydAB, cyoABCDE, fadBA, focA_pflB, fumA, glcC, glcDEFGB, gltA, lctPRD, mdh, nuoABCEFGHIJKLMN, fabA, fadL, fadR, uspA
Oxidative phosphorylation, Glycolate, lactose utilization, fatty acid degradation
cytR, deoCABD, deoR, nupC, nupG, tsx, udp
Nucleosides uptake and usage
cirA, entCEBA, fecABCDE, fecIR, fepA_entD, fepB, fepDGC, fhuACDB, fur, tonB
Iron uptake system
galETKM, galR, galS, mglBAC
Galactose uptake and usage
dmsABC, fdnGHI, narGHJI, narK, nirBDC_cysG, nrfABCDEFG, torCAD, torR
Nitrogen metabolism, Nitrate and nitrite reductase,
narZYWV, nhaA, nhaR, osmY
intracellular pH regulation
aslB, inaA, mdlA, rob, ybaO, ybiS, yfhD
cutC, dapA_nlpB_purA, ecfABC, ecfD, ecfF, ecfG, ecfH, ecfJ, ecfK, ecfLM, fkpA, ksgA_epaG_epaH, lpxDA_fabZ, mdoGH, nlpB_purA, ostA_surA_pdxA, rfaDFCL, rfbO, rpoE_rseABC, uppS_cdsA_ecfE
RpoE regulated stress response, lipopolysaccharide synthesis
ansB, cpdB, cyaA, dadAX, epd_pgk, glgCAP, glgS, ivbL_ilvBN, ompA, speC, srlAEBD_gutM_srlR_gutQ, tnaLAB, ubiG, yhfA
Sorbitol and Glycogen metabolism
atoC, atoB, hydHG, hypA, pspABCDE, pspF, rtcAB, rtcR, zraP
Phage shock protein, Zn-resistence system, Acetoacetate metabolism
dsdC, dsdXA, ebgAC, ebgR, fucAO, fucPIKUR, lacI, lacZYA, malI, malXY, melAB, melR, uhpA, uhpT, yiaJ, yiaKLMNOPQRS
Lactose, maltose, fucose, dehydroascorbate, xylulose, melibiose transport and metabolism
aroF_tyrA, aroG, aroH, aroL_yaiA_aroM, aroP, mtr, trpLEDCBA, trpR, tyrP, tyrR
Aromatic amino acid synthesis
bioA, bioBFCD, birA_murA
cbl, cysB, cysDNC, cysJIH, cysK, cysPUWAM, ssuEADCB, tauABCD
Sulfur metabolism, cysteine synthesis, Taurine utilization
exuR, exuT, uidR, uidABC, uxaCA, uxuABR
Utilization of hexUronide
lexA_dinF, polB, recA, recN, rpsU_dnaG_rpoD, ssb, sulA, umuDC, uvrA, uvrB, uvrC, uvrD
DNA recombination and repair, UV resistent
phnCDE_f73_phnFGHIJKLMNOP, phoA, phoBR, phoE, pstSCAB_phoU
To investigate if the modules identified from anaylisis of the network structure are really functionally related, we checked the functions of the genes in the individual modules by using database EcoGene . Most genes in the same module turned out to have closely related biological function. Thus, we can assign clearly defined functions for most of the modules. However, there are also several modules which include operons that are seemingly functionally not closely related. For example, there are also several operons for acetate usage in module 1 (Table 2) besides the operons for the PTS sugar transport system. One of the acetate usage operons aceBAK is also repressed by FruR, the regulator for fructose uptake. This makes the cell not to use acetate as a substrate in the presence of fructose. Therefore, the two different pathways are actually functionally related from a regulation viewpoint. The other three modules (module 4, 23 and 32) which include operons with different functions are actually linked by certain global regulators (fis, arcA and rpoN respectively). They are not connected by any local regulators with specific functions. Thus, it is not strange that they are not closely functionally related. One reason for this problem is probably the information incompleteness of the network. The regulatory network considered in this work contains only about twenty percent of the genes in the E. coli genome. With more and more information available we can include more interactions and genes in the network to obtain more reasonable modules by structural analysis. Identifying these functional modules can help us to gain a general view of the function (or ability) of organisms. Furthermore, we can compare these structure based modules with modules from hierarchical classification results of microarray experiments to find unknown regulatory relationships.
We compared the ten global regulators with those found in three previous studies by considering the number of directly or indirectly regulated genes (operons) and their structure and function diversity [7, 10, 11]. Five of them (CRP, IHF, FNR, HNS, ArcA) have been identified in all the three studies. The other five regulators have also been recognized as global in either one or two of the three studies. Our definition of global regulators is directly linked to the identification of functional modules. Modules are sets of genes with closely related function. An important criterion for a regulator to be regarded as global is that it regulates genes with diverse but concerted functions. Therefore, determination of global regulators by the number of regulated modules is more reasonable than that solely by the number of genes or operons. From Fig. 3 we can see that the number of links among the modules is far less than that between the global regulators and the modules. This indicates that the global regulators introduce the major cross-talks between modules and link them together to form the whole network. Therefore, breaking the links through the global regulators can help to identify the true modules as shown in this work.
Network motifs and motif clusters
To investigate if network motifs, which are considered to be the elementary building blocks of the whole network , are basic building blocks of modules and if motif clusters are generally equivalent to functional modules, we calculated the feed forward loops (FF loops) in the TRN of E. coli. In agreement with the results of Dobrin et al , the 42 FF loops in the network aggregate to seven homologous motif clusters (see Additional file Additional file 1). Four of the motif clusters are generally in consistence with the modules identified in this study (Table 2), including the flagellar-motor module (module 15), the osmoregulated porin gene module (13), the oxidative stress response module (2) and the methionine biosynthesis module (9). The third feed forward cluster found by Dorbin et al.  comprises genes of nitrogen regulation and formate regulon. They are found in two separate modules (14 and 17 respectively) in this work. In contrast to the good agreement for the five motif clusters, the other two clusters include genes belonging to many different modules. For example, the CRP cluster (see Additional file 1) consists of genes for usage of different carbon sources such as arabinose (module 5), carnitine (7), fucose (33), maltose (21), galactose (26) and mannose (18). The reason for this discrepancy is that each of the two clusters contains a global regulator (FNR and CRP respectively) which regulates genes with various functions. We further investigated the distribution of the 42 FF loops in the hierarchical structure and find that 32 of them contain one of the ten global regulators. Because modules are defined as subsets of genes with closely related functions, while global regulators tend to regulate functionally far related genes, clusters formed from network motifs which contain global regulators are not proper candidates for modules. For the four consistent motif clusters, three of them are formed from the ten FF loops that do not contain global regulators. Cluster four (osmoregulated porin gene) contains the global regulators IHF and OmpR. As shown in Fig. 1B, these two global regulators also regulate genes with flagellar motility function (module 15) and many other genes with different functions. Therefore, these two regulators cannot be properly placed in one module though most of the other genes in the cluster are functionally related. We also calculated the bi-fan motifs and find that 180 of the 209 bi-fan motifs contain global regulators. Among them 130 bi-fan motifs contain two global regulators. This means that two target operons would be coregulated by two global regulators. The fact that most network motifs contain global regulators which regulate functionally far related operons indicates that motifs cannot be regarded as elementary building blocks of functional modules because global regulators should not belong to any module with specific functions.
The E. coli transcriptional regulatory network presently known possesses a multi-layer hierarchical structure with no feedback regulation at transcription level. Regulators in the top layers of the hierarchical structure can be considered as global regulators that often act together with local regulators to regulate genes in the bottom layer. Based on the hierarchical structure a new decomposition method is proposed which can be used to identify functional modules in the network. Analysis of operon composition of the two well-known network motifs (feed forward loop and bi-fan motif) and their distribution in the hierarchical structure suggests that they are not elementary building blocks of functional modules in the transcriptional regulatory network of E. coli.
Network reconstruction and representation
The original transcriptional regulatory database of E. coli was obtained from the website of Alon's research group. This database is mainly based on the RegulonDB  and complemented by Shen-Orr et al . We removed three operons (gatR_1, rcsA and rotA) because they are either the same with another operon or inside another operon. GatR_1 has been merged with gatR_2 in the updated annotation of E. coli genome in the database EcoGene . RcsA is part of the rcsAB operon, while rotA is the same with ppiA. Another operon, nycA, was not found in any E. coli genome database. We searched the original literature  for this gene from the database obtained from Shen-Orr et al  and could still not find it. Therefore, we removed the nycA operon from the network. There are also six operons (emrRAB, gatYZABCDR, hipBA, idnDOTR, moaABCDE and mtlADR) in the network that are only autoregulated and hence do not connected with other operons. Therefore, we ignored these operons as well when analyzing the network connectivity structure. The resulting network consists of 413 nodes (operons) and 576 directed links (regulatory relationships). The 54 autoregulatory relationships in the network are represented as loops in the graph.
Network structure analysis
Calculations for the network structure analysis were carried out by using the software Pajek . The number of directly regulated operons of a regulator gene equals to its output degree, while the total number of directly and indirectly regulated operons equals to its output domain. The connected components were found by calculating the weakly connected components (the direction ignored because the regulatory network is an acyclic directed graph).
Network motif calculation
From the hierarchical structure, feed forward loops are easily found by searching for all the fully connected triads which are located in different regulatory layers (not necessary to be three nearby layers). Bi-fan motifs are searched by using the subgraph searching algorithm in Pajek .
This work was financially supported through the Braunschweig Bioinformatic Competence Center project "Intergenomics" of the Ministry for Education and Research (BMBF), Germany (Grant No. 031U110A) and by the project B6 in the Sonderforschungsbereich 578 der Deutschen Forschungsgemeinschaft (DFG).
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