evolutionary dynamics of prokaryotic transcriptional ... · uncorrected proof evolutionary dynamics...

doi:10.1016/j.jmb.2006.02.019 J. Mol. Biol. (xxxx) xx, 1–20

ARTICLE IN PRESS

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

72

73

74

75

Evolutionary Dynamics of Prokaryotic TranscriptionalRegulatory Networks

M. Madan Babu1,2*, Sarah A. Teichmann1 and L. Aravind2*
76
77

78

79

80

81

82

83

84

1National Center for Biotech-nology Information, NationalInstitutes of Health, MD 20894USA

2MRC Laboratory of MolecularBiology, Hills Road, CambridgeCB22QH, UK

U0022-2836/$ - see front matter q 2006 P

Abbreviations used: LSI, lifestyleE-mail addresses of the correspon

[email protected]; aravi

YJMBI 58077—25/2/2006—18:46—SATHYA—

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

100

101

102

103

104

105
TED PROOF
The structure of complex transcriptional regulatory networks has beenstudied extensively in certain model organisms. However, the evolutionarydynamics of these networks across organisms, which would revealimportant principles of adaptive regulatory changes, are poorly under-stood. We use the known transcriptional regulatory network of Escherichiacoli to analyse the conservation patterns of this network across 175prokaryotic genomes, and predict components of the regulatory networksfor these organisms. We observe that transcription factors are typically lessconserved than their target genes and evolve independently of them, withdifferent organisms evolving distinct repertoires of transcription factorsresponding to specific signals. We show that prokaryotic transcriptionalregulatory networks have evolved principally through widespreadtinkering of transcriptional interactions at the local level by embeddingorthologous genes in different types of regulatory motifs. Differenttranscription factors have emerged independently as dominant regulatoryhubs in various organisms, suggesting that they have convergentlyacquired similar network structures approximating a scale-free topology.We note that organisms with similar lifestyles across a wide phylogeneticrange tend to conserve equivalent interactions and network motifs. Thus,organism-specific optimal network designs appear to have evolved due toselection for specific transcription factors and transcriptional interactions,allowing responses to prevalent environmental stimuli. The methods forbiological network analysis introduced here can be applied generally tostudy other networks, and these predictions can be used to guide specificexperiments.

q 2006 Published by Elsevier Ltd.

Keywords: transcriptional regulatory network; evolution; network; networkmotif; regulation

106

*Corresponding authors
C 107
108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

NCORREIntroduction

Of the several steps at which the flow ofinformation from a gene to its protein product iscontrolled, regulation at the transcriptional level is afundamental mechanism observed in all organisms.This form of regulation is typically mediated by aDNA-binding protein (transcription factor) thatbinds to target sites in the genome and, eithersingly or in combination with other factors,regulates the expression of one or more targetgenes. The sum total of such transcriptionalinteractions in an organism can be conceptualisedas a network, and is termed the transcriptional

ublished by Elsevier Ltd.

similarity index.ding authors:[email protected]

202231—XML – pp. 1–20 / GH

123

124

125

126

regulatory network.1–11 In such a network, nodesrepresent genes and edges represent regulatoryinteractions. Studies on the transcriptional regula-tory network at an abstract level have shown thatthey have architectures resembling scale-free net-works, with striking structural and topologicalsimilarity to other networks from biological andnon-biological systems. They are characterized bythe recurrence of small patterns of interconnections,called network motifs,3–5,12–16 which were firstdefined in Escherichia coli,3 and were subsequentlyfound in yeast and other organisms.2,4,12

Even though the general structural properties oftranscriptional networks are well understood, thereare several fundamental questions regarding theprovenance and evolution of transcriptional regu-latory networks that remain unanswered: What arethe trends of conservation of transcription factors,

T

2 Prokaryotic Transcriptional Regulatory Networks

ARTICLE IN PRESS

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

206

207

208

209

210

211

212

213

214

215

216

217

218

219

220

221

222

223

224

225

226

227

228

229

230

231

232

233

234

235

236

237

238

239

240

241

242

243

244

245

246

247

248

249

250

251

252

UNCORREC

target genes and regulatory interactions in thenetwork? How do interactions that specify topolo-gically equivalent motifs within the networkevolve? How does the global structure of thenetwork evolve? We addressed these questions byusing the experimentally determined transcrip-tional regulatory network of E. coli as a referencenetwork,3,17 and performing a comparativegenomic analysis to predict components of theregulatory network for 175 prokaryotes withcompletely sequenced genomes, from diverselineages of the bacterial and archaeal kingdoms(a list of the genomes is provided as SupplementaryData).

Reconstruction of transcriptional regulatorynetworks

While there has been considerable progress inunravelling the regulatory networks of variousmodel organisms such as E. coli, the extrapolationof this information to poorly studied organisms,whose complete genome sequences are now avail-able, remains a major challenge. Target genes for atranscription factor can be identified by usingsequence profiles of known binding sites acrossdifferent organisms and by arriving at a set of geneswith conserved regulatory sequences. However,this method requires prior knowledge aboutbinding sites, and is applicable only to closelyrelated genomes, since orthologous transcriptionfactors may regulate orthologous target genesthrough very divergent binding sites in distantlyrelated organisms.18–21

Alternatively, using an experimentally characte-rized transcriptional network as template, one caninfer transcriptional targets of a regulator in agenome of interest by identifying orthologues oftranscription factors and their target genes. It is nowgenerally accepted that in the majority of cases,orthologous transcription factors regulate ortho-logous target genes. This procedure of transferringinformation about transcriptional regulation from agenome with known regulatory interactions toanother genome by identifying orthologous pro-teins was assessed recently by Yu et al.,22 and wasfound to be a fairly robust method for predictingsuch interactions in eukaryotes. In fact similarapproaches, based on orthologue detection using abi-directional best-hit procedure,23–28 have beendeveloped successfully to transfer information oninteractions to other organisms and have proved tobe useful in predicting new interactions.

Detecting orthologues is a non-trivial process.After testing various orthologue detection pro-cedures (e.g. bi-directional best-hit and best hitswith defined e-value cut-offs), we arrived at ahybrid procedure, which was used to identifyorthologous proteins in a genome. Using thisapproach, we predicted components of the tran-scriptional networks. With the best-characterizedtranscriptional regulatory network currently avail-able, that of E. coli with 755 genes (112 transcription

YJMBI 58077—25/2/2006—18:46—SATHYA—202231—XML – pp. 1–20 / G

ED PROOF

factors) and 1295 transcriptional interactions, as atemplate, we used our orthologue detection pro-cedure and predicted transcriptional interactionnetworks, for the first time, for 175 prokaryoticgenomes (Figure 1; Supplementary Data M1, M2and S3). Our method is based on identifying theorthologues of E. coli transcription factors andthe orthologues of E. coli target genes in each ofthe prokaryote genomes using protein sequencecomparisons, without considering conservation ofthe transcription factor-binding sites in the DNA.We chose the orthology-based method rather thanusing binding site information because usingbinding sites (i) would place an additional con-straint and would drastically reduce the size of theE. coli network that we considered (only a fewtranscription factors in the network have enoughexperimentally characterized binding site infor-mation to build a reliable profile) and (ii) wouldlimit the number of genomes that can be compared,as reliable detection of these short binding sites indistantly related organisms is not possible, and mayhence bias our analysis.20 It should be noted that themethod we develop can, in principle, be applied toany reference network, and we chose the E. colinetwork because it is the most comprehensive whencompared to networks available for other prokar-yotes.

The transcriptional regulatory network for E. coliis shown in Figure 1, along with the networks for aGram-positive mammalian pathogen, Bacillusanthracis and a free-living organism, Streptomycescoelicolor, which were reconstructed using the E. colinetwork as the reference network. We wish toemphasize that we do not predict new regulatoryinteractions that have been gained in the otherorganisms, and no current computational methodsallow us to do it in the absence of other externalinformation such as gene expression data, etc.However, we can still predict potentially conservedinteractions, apart from conserved regulatory com-ponents and genes, which can shed light onnetwork evolution (see Supplementary Data S17).

Orthologue detection, which is the foundation forour network reconstruction procedure can beconfounded by rapid duplication, divergence andloss of genes. Hence, in this study, we assessed ourprocedure using the expression data available forVibrio cholerae and the known regulatory network ofBacillus subtilis. We studied the extent to whichtarget genes with the same set of known andpredicted transcription factors have similarexpression profile.29 We found that co-regulatedgenes in E. coli, for which the transcriptionalnetwork is known, and in V. cholerae, for whichpredictions were based on the reconstructednetwork, tend to be strongly co-expressed(Supplementary Data S2). Ideally, one would wantto carry out such an assessment for as manygenomes as possible; however, the availability ofmeaningful gene expression data for other organ-isms limits us to restrict this analysis to V. cholerae.This result supports the validity of reconstructing

H

UNCORRECTED PROOFFigure 1. Known transcriptional regulatory network for E. coli and reconstructed transcriptional regulatory networks for a pathogen, Bacillus anthracis and a free-living

organism, Streptomyces coelicolor. Transcription factors and target genes are represented as red and blue circles, and transcriptional interactions are represented as black lines. Thetranscription factors are ordered according to their connectivity to emphasize the scale-free-like structure of the network at the global level, where there are few dominantregulatory hubs that control many target genes and many transcription factors that regulate few target genes. Information about the number of transcription factors, targetgenes, regulatory interactions and regulatory network motifs is provided at the right.2,4 This Figure illustrates how we can reconstruct transcriptional networks for genomes,including organisms, that are poorly characterised but are important. The table below shows the number of transcription factors that have a DNA-binding domain belonging toa particular family. It is clear by comparing the numbers that each genome has evolved its own set of transcriptional regulators, and hence regulatory interactions, by using thesame DNA-binding domains to different extents.

YJM

BI58077—

25/2/

2006—18:46—

SATHYA—202231—

XML–pp.1–20

/GH

Prokaryotic

Transcrip

tionalRegulatory

Networks

3

ARTICLEIN

PRESS

253

254

255

256

257

258

259

260

261

262

263

264

265

266

267

268

269

270

271

272

273

274

275

276

277

278

279

280

281

282

283

284

285

286

287

288

289

290

291

292

293

294

295

296

297

298

299

300

301

302

303

304

305

306

307

308

309

310

311

312

313

314

315

316

317

318

319

320

321

322

323

324

325

326

327

328

329

330

331

332

333

334

335

336

337

338

339

340

341

342

343

344

345

346

347

348

349

350

351

352

353

354

355

356

357

358

359

360

361

362

363

364

365

366

367

368

369

370

371

372

373

374

375

376

377

378


ARTICLE IN PRESS

379

380

381

382

383

384

385

386

387

388

389

390

391

392

393

394

395

396

397

398

399

400

401

402

403

404

405

406

407

408

409

410

411

412

413

414

415

416

417

418

419

420

421

422

423

424

425

426

427

428

429

430

431

432

433

434

435

436

437

438

439

440

441

442

443

444

445

446

447

448

449

450

451

452

453

454

455

456

457

transcriptional networks by inferring regulatoryinteractions between orthologous transcriptionfactors and orthologous target genes in prokaryotes.Additionally, the experimentally determined tran-scriptional regulatory network of B. subtilis (abacterium very distantly related to our referenceorganism E. coli) shows a good degree of con-gruence with the interactions predicted by ouranalysis, thereby lending support to the validity ofour reconstruction procedure (Supplementary DataS11; note that the experimentally determinedB. subtilis network is far from complete and ismuch smaller than the E. coli network and, hence,this comparison should not be treated as a goldstandard to get false positive and false negativeestimates).

458

459

460

461

462

463

Results and Discussion

Evolution of genes and their regulatoryinteractions

T

464

465

466

467

468

469

470

471

472

473

474

475

476

477

478

479

480

481

482

483

484

485

486

487

488

489

490

491

492

493

494

495

496

497

498

499

500

501

502

503

504

UNCORREC

Transcription factors evolve rapidly andindependently of their target genes

To assess the evolutionary trends in the networkconservation, we asked if transcriptional regulatorsand their targets are conserved differentially inevolution. When we quantified the extent ofconservation of the 755 genes in the 175 genomes,we found that transcription factors are less con-served across genomes during evolution than theirtarget genes (Figure 2(a)). We assessed the signifi-cance of this observed bias in the conservationpatterns by simulating network evolution (Sup-plementary Data M3), and found it to be statisticallysignificant (p!10K4). This suggests that the evol-ution of major phenotypic differences betweenorganisms is a consequence of replacements oftranscription factors, which provide regulatoryinputs, rather than changes in the gene repertoireitself. The above observation is consistent with thefact that metabolic enzymes, which constitute amajor set of the target genes and thus the metabolicnetwork, are well conserved across the differentorganisms.30

Because a regulatory interaction involves atranscription factor and its target gene, one mightexpect them to be present or absent as pairs. Indeed,studies on the conservation of physically interactingproteins across the different proteomes suggest thatproteins forming a complex, especially interactingpairs, tend to be conserved or lost in a concertedmanner.30,31 To test this, we first created two sets ofgene pairs using the transcriptional regulatorynetwork: (i) the interacting set, which consisted ofall pairs of genes that are known to interact in thetranscriptional network and (ii) the non-interactingset, which consisted of all possible pairs of genes inthe network that are known not to interact in theoriginal network. We then analysed the conser-vation pattern across the 175 organisms for all the


pairs of genes in both sets. If interacting proteins arepreferentially conserved or lost, one would expectthe trends obtained for the two sets to be vastlydifferent. On the other hand, if interacting proteinsare conserved to the same extent as any pair of non-interacting proteins, one would expect the trendsobtained for the two sets to be similar. Interestingly,in the transcriptional regulatory network studiedhere, the relative conservation of pairs from the twosets is close to 1 across all genomes, suggesting thatthere is no strong preference for pairs of interactingtranscription factors and target genes to be con-served in the network (Supplementary Data M6d;and Figure 2(b)). This observation suggests that theforces of natural selection act relatively indepen-dently to retain or discard transcription factors andtheir targets, as opposed to protein pairs that areinvolved in physical interactions.

ED PROOF

Organisms have evolved their own setof transcription factors

Several bacteria, such as B. anthracis and S. coeli-color (Figure 1), have few transcription factors withorthologues in the E. coli reference network. How-ever, these genomes are relatively large and mustcontain a complex transcriptional regulatory net-work. Hence, we searched these proteomes withsensitive sequence profile methods to identifyproteins with DNA-binding domains typicallyobserved in known transcription factors.33 In mostgenomes, we could identify other transcriptionfactors belonging to the same repertoire of DNA-binding domain families as in E. coli (Figure 1), butnot orthologous to the E. coli proteins. This obser-vation indicates that new lineage-specific transcrip-tion factors, and thereby new interactions, aregained constantly during evolution. However, ourcurrent level of understanding prevents a syste-matic prediction of the newly gained interactions(Supplementary Data S14). For small genomes,typically those of obligate parasites with lowfractions of transcription factors, no additionaltranscription factors were detectable (Supplemen-tary Data S4). Consistent with recent observationsby van Nimwegen, Ranea et al. and by us,34–36 theincrease in the number of regulatory proteins withgenome size is non-linear (Figure 2(c)). This impliesthat as genome size increases, a greater thanproportional increase in the numbers of transcrip-tion factors is required for controlling the newlyadded genes. This tendency might correlate withthe need to regulate specialized groups of genesindividually, or with the need for integration ofdistinct inputs and introducing more layers in theregulatory hierarchy of the metabolically or organi-zationally complex bacteria with large genomes.36

These observations, taken together with the higherdegree of conservation of target genes, suggestcertain general principles that operate in theevolution of transcriptional networks. (1) In smallparasitic genomes, transcription factors have beenlost due to the absence of selective pressures for

H

UNCORRECTED PROOF

Figure 2. Conservation of the transcription regulatory network and regulatory interactions. (a) For each of the 175genomes, the fraction of target genes conserved (x-axis) is plotted against the fraction of transcription factors conserved(y-axis). The diagonal, shown in green, represents equal conservation of transcription factors and target genes, and everypoint represents a genome. The graph shows that more target genes are conserved than transcription factors in thegenomes considered (yZ1.1173xK16.609, R2Z0.9223, p!10K4). The significance of this trend was assessed bysimulating network evolution, which involved neutral removal of different sets of genes 10,000 times (Supplementary

YJMBI 58077—25/2/2006—18:46—SATHYA—202231—XML – pp. 1–20 / GH

Prokaryotic Transcriptional Regulatory Networks 5

ARTICLE IN PRESS

505

506

507

508

509

510

511

512

513

514

515

516

517

518

519

520

521

522

523

524

525

526

527

528

529

530

531

532

533

534

535

536

537

538

539

540

541

542

543

544

545

546

547

548

549

550

551

552

553

554

555

556

557

558

559

560

561

562

563

564

565

566

567

568

569

570

571

572

573

574

575

576

577

578

579

580

581

582

583

584

585

586

587

588

589

590

591

592

593

594

595

596

597

598

599

600

601

602

603

604

605

606

607

608

609

610

611

612

613

614

615

616

617

618

619

620

621

622

623

624

625

626

627

628

629

630


ARTICLE IN PRESS

631

632

633

634

635

636

637

638

639

640

641

642

643

644

645

646

647

648

649

650

651

652

653

654

655

656

657

658

659

660

661

662

663

664

665

666

667

668

669

670

671

672

673

674

675

676

677

678

679

680

681

682

683

684

685

686

687

688

689

690

691

692

693

694

695

696

697

698

699

700

701

702

703

704

705

706

707

708

709

710

711

regulation. (2) In larger genomes, target genes areoften controlled by additional regulators or reg-ulators that are non-orthologous, so that genes areresponsive to a variety of different inputs that aretypically dependent on the environmental niche ofthe organism. For example, in a number ofphylogenetically distant free-living bacteria, inclu-ding proteobacteria, B. subtilis and Streptomyces,there is an expansion of so-called one-componenttranscription factors of the LysR family, which sensea wide range of small-molecule ligands. However,there are no homologues of such transcriptionfactors in any of their close relatives, which areobligate pathogens. This is consistent with the needin all of these evolutionarily weakly related, free-living, physiologically complex bacteria to be ableto sense the same set of metabolites in theirenvironment.

T

712

713

714

715

716

717

718

719

720

721

722

723

724

725

726

727

728

729

730

731

732

733

734

735

736

737

738

739

740

741

742

743

744

745

746
ORREC
Organisms with similar lifestyles preservehomologous regulatory interactions

Given that the conservation of transcriptionfactors and target genes is poorly correlated, wesought to address the extent to which transcrip-tional interactions are conserved across differentorganisms. To do this, we developed an algorithmto deduce the relationship between differentgenomes based on conservation of specific sets oftranscriptional interactions (Supplementary DataM5). We first represented the presence or theabsence of genes and interactions of the referenceE. coli transcriptional network in each of the 175prokaryotic genomes as a binary interaction con-servation profile. The organisms were then hier-archically clustered on the basis of their interactionconservation profiles (Figure 3(a)).

The clustering of organisms based on theinteractions present and depicted in Figure 3(b)reveals that transcriptional interactions appear to beshaped by a set of disparate forces. At low tomoderate evolutionary distance from the referenceorganism E. coli, namely proteobacteria, the cluster-ing generally mirrors organism-based relationshipsconstructed using highly conserved genesequences,37 suggesting that the regulatory networkretains a noticeable phylogenetic signal. Beyond theproteobacteria, a number of other effects appear todominate upon the background of the weakphylogenetic signal. These include the similaritiesin genome size (Supplementary Data S15)38,39 andecological adaptations:40,41 for example, bacteriawith comparable genome size, such as several species

UNCData). (b) For each of the 175 genomes, the fraction of genes cgenes conserved (y-axis) in the interacting set (black) and the nmore conserved, one would expect significant differences betthat the trends are very similar (the average relative conservat0.95), suggesting that pairs of interacting genes are conserveinteracting genes. (c) The number of predicted transcriptiongenome size (x-axis), indicating that new transcription factoryZ9e(K0.5x1.75) fits the data best (R2Z0.83).


ED PROOF

of Bacillus, Corynebacterium and Mycobacterium,whose principal habitat is the soil, form a cluster(Figure 3(b)). Likewise, the obligate or intracellularparasites from diverse bacterial clades, namelyMycoplasma, Rickettsiae and Chlamydiae, grouptogether in this analysis.

This observation motivated us to investigatewhether our finding that organisms with similarlifestyles conserve similar interactions was ageneral principle, or was limited to anecdotalexamples. First, we systematically defined thelifestyle of each organism as a combination of fourproperties: oxygen requirement, optimal growthtemperature, environmental condition, andwhether it is a pathogen. For example, E. coli K12would belong to the lifestyle class called facul-tative:mesophilic:host-associated:no. Then, wecompared the similarity between organisms belon-ging to different lifestyle classes as a function of theinteractions they have in common (SupplementaryData S12 andM10). Each element in thematrix shownin Figure 4(a) corresponds to the normalized averagesimilarity in the interaction content across all pairs oforganisms belonging to the lifestyle classes con-sidered. The values along the diagonal, which reflectthe average similarity among organisms belonging tothe same lifestyle class, tend to be much greater thanthe off-diagonal elements. This lends support to ourfinding that organisms with similar lifestyles doindeed tend to conserve similar interactions.

We defined an index, called the lifestyle similarityindex (LSI), to measure the strength of this trend.The LSI is calculated as the ratio of the averagesimilarity among the diagonal elements to theaverage similarity of the off-diagonal elements.LSI values greater than 1 mean that organismswithin the same lifestyle class have more similarityof interactions compared to organisms belonging toa different lifestyle class. Amongst the organismsincluded in our study, we obtained an LSI value thatis far above 1 and is statistically significant (LSIZ1.42; p!10K3; Z-scoreZ2.96). This shows thatorganisms belonging to the same lifestyle have asignificantly higher number of interactions incommon in comparison to organisms from otherlifestyle classes. The enrichment in similarity insome off-diagonal elements, corresponding toorganisms belonging to different lifestyle classes,is associated primarily with certain mesophilicorganisms. This arises due to the fact that theselife-style classes contain organisms with largegenomes that are phylogenetically related to otherspecialized organisms spanning the entire spectrum

onserved (x-axis) is plotted against the fraction of pairs ofon-interacting set (green). If interacting pairs of genes are

ween the trends obtained for the two sets. The plot showsion of gene pairs across 175 organisms from the two sets isd to an extent comparable with that of any pair of non-factors (y-axis) increase non-linearly with the increase ins evolve as genome size increases. A power-law equation

H

747

748

749

750

751

752

753

754

755

756


ARTICLE IN PRESS

757

758

759

760

761

762

763

764

765

766

767

768

769

770

771

772

773

774

775

776

777

778

779

780

781

782

783

784

785

786

787

788

789

790

791

792

793

794

795

796

797

798

799

800

801

802

803

804

805

806

807

808

809

810

811

812

813

814

815

816

817

818

819

820

821

822

823

824

825

826

827

828

829

830

831

832

833

834

835

836

837

838

839

840

841

842

843

844

845

846

847

of lifestyle classes. For example, B. subtilis, a meso-phile, may be related to a thermophile like Bacillusstearothermophilus, a pathogen like B. anthracis, anae-robe like Thermoanaerobacter tengcongensis and micro-aerophiles such as Lactobacillus. We emphasize herethat each phylogenetic group does have organismswith different lifestyles and, for each lifestyle classthat we define, there are organisms belonging todifferent phylogenetic groups. Thus, the LSI valuesare not overly biased by evolutionary relatedness fora lifestyle class or by the enrichment of a particularlifestyle class within a phylogenetic group (Sup-plementary Data S13). This proposition applies alsoto a subsequent analysis of lifestyle classes andmotifconservation, described later.

Thus, on the basis of the information aboutconservation of specific transcription factors andtheir interactions, we were able to potentiallyreconstruct the presence of transcriptional responsepathways similar to those in the reference network invarious other genomes, and understand their evol-ution (Supplementary Data S5, S10, M4, M5). Incombination with other context-based methods,42,43

these reconstructions could aid in probing poorlycharacterized or experimentally intractable organ-isms, including key human pathogens. For example,important pathogens like Yersinia pestis, Pseudomonassyringae and a nitrogen-fixing symbiont of the

UNCORRECT

Figure 3 (legen


OF

soybean plant, Bradyrhizobium japonicum, have con-served the GntR, KdgR, ExuR and UxuR transcrip-tional regulators. These regulators can sense differenthexuronates and hexuronides, suggesting the con-servation of the pathway that can catabolize thesecompounds. Examinationof target gene conservationshows that they are indeed conserved, thus allowingus to predict the presence of these response pathwaysand their transcriptional regulators. Extending ourapproach with other reference networks in futurecould identify potential targets for therapeuticintervention in unrelated pathogens that share asimilar ecological niche.

Evolution of local network structure

At the local level, the transcription regulatorynetwork shows recurrent topological patterns ofinterconnections called network motifs, which canbe viewed as building blocks of the network.2–4

Such network motifs have been suggested to carryout specific information processing tasks and hencedictate specific patterns of gene expression.6,44 Forexample, the function of feed-forward motifs is torespond only to persistent signals, and that ofsingle-input motifs is to bring about a quick andcoordinated change in gene expression. We inves-tigated whether natural selection acts at the level of

ED PRO

d next page)

848

849

850

851

852

853

854

855

856

857

858

859

860

861

862

863

864

865

866

867

868

869

870

871

872

873

874

875

876

877

878

879

880

881

882

CTED PROOF

Figure 3. Regulatory interaction conservation in genomes. (a) A method to analyse conservation of regulatoryinteractions in the different genomes. (b) Genomes were clustered according to the interactions they conserve. The Figureshows that: (i) genomes in the same phylogenetic group generally cluster together; (ii) parasitic genomes; and (iii)genomes with similar lifestyle but belonging to different phylogenetic groups cluster together. This suggests thatinteractions are gained or lost depending on the organism’s lifestyle and the environmental conditions in which theylive. Reconstructed transcriptional network for a representative organism from each group is given at the side.


ARTICLE IN PRESS

883

884

885

886

887

888

889

890

891

892

893

894

895

896

897

898

899

900

901

902

903

904

905

906

907

908

909

910

911

912

913

914

915

916

917

918

919

920

921

922

923

924

925

926

927

928

929

930

931

932

933

934

935

936

937

938

939

940

941

942

943

944

945

946

947

948

949

950

951

952

953

954

955

956

957

958

959

960

961

962

963

964

965

966

967

968

969

970

971

972

973

974

975

976

977

978

979

980

981

982

983

984

985

986

987

988

989

990

991

992

993

994

995

996

997

998

999

1000

1001

1002

1003

1004

1005

1006

1007

1008

UNCORREmotifs and whether the interactions, which areconserved across organisms, correspond to networkmotifs in the reference network.

In the E. coli network, there are 277 feed-forwardmotifs, 43 single-input motifs and 70 multiple-inputmotifs. To evaluate motif conservation in thenetwork, we devised a new method (Supplemen-tary Data M8; and Figure 5) that clusters topologi-cally equivalent motifs according to theirconservation profile. First, we generated a motifconservation profile for each genome, and thencarried out a two-way clustering procedure: ak-means clustering of all motifs with similardistribution profile across genomes, followed by ahierarchical clustering of genomes on the basis oftheir motif conservation profile (SupplementaryData S6 and S7).


Interactions within motifs are conserved to the sameextent as other interactions

Contrary to our expectation, the analysis showeda surprising result, that there is no significantconservation of whole motifs when compared torandom networks of similar size. Interestingly,within coherent monophyletic lineages of prokar-yotes with different lifestyles, particular networkmotifs may not be conserved, whereas genomesbelonging to unrelated phylogenetic groups butwith similar lifestyle conserve these motifs. Forexample, Fnr (a global regulator, activated duringlow levels of oxygen), NarL (transcriptionalregulator of a two-component signal transductionsystem) and NuoN (subunit of the NADH dehy-drogenase complex I) form a feed-forward motif,

H

UNCORRECTED PROOFFigure 4. Organisms with similar lifestyles show similarity in interaction and motif content. Each element in the matrix represents (a) the average similarity in the interactioncontent or (b) the average similarity in the motif content among all pairs of organisms belonging to the two lifestyle classes considered. The lifestyle similarity index (LSI) is theratio of average similarity between organisms in the same lifestyle class to the average similarity between organisms belonging to a different lifestyle class, i.e. the ratio of theaverage value of the diagonal elements to the average value of the off-diagonal elements. LSIO1 suggests that organisms with similar lifestyles tend to show similar interactionsor network motif contents. The LSI values are 1.42 and 1.34 for the similarity based on (a) interactions and (b) network motifs. The matrix shown is not symmetric because eachelement has been row-normalized to highlight the trend. This is done for illustrative purposes only, and will not affect the LSI values. (For details, see Supplementary Data S12and M10.)

YJM

BI58077—

25/2/

2006—18:46—

SATHYA—202231—

XML–pp.1–20

/GH

ARTICLEIN

PRESS

1009

1010

1011

1012

1013

1014

1015

1016

1017

1018

1019

1020

1021

1022

1023

1024

1025

1026

1027

1028

1029

1030

1031

1032

1033

1034

1035

1036

1037

1038

1039

1040

1041

1042

1043

1044

1045

1046

1047

1048

1049

1050

1051

1052

1053

1054

1055

1056

1057

1058

1059

1060

1061

1062

1063

1064

1065

1066

1067

1068

1069

1070

1071

1072

1073

1074

1075

1076

1077

1078

1079

1080

1081

1082

1083

1084

1085

1086

1087

1088

1089

1090

1091

1092

1093

1094

1095

1096

1097

1098

1099

1100

1101

1102

1103

1104

1105

1106

1107

1108

1109

1110

1111

1112

1113

1114

1115

1116

1117

1118

1119

1120

1121

1122

1123

1124

1125

1126

1127

1128

1129

1130

1131

1132

1133

1134

UNCORRECTED PROOF

Figure 5.Network motif conservation in genomes. (a) A procedure to build and cluster motif conservation profiles toidentify topologically equivalent motifs that are conserved in the different genomes. (b) Cluster diagram of the 277 feed-



ARTICLE IN PRESS

1135

1136

1137

1138

1139

1140

1141

1142

1143

1144

1145

1146

1147

1148

1149

1150

1151

1152

1153

1154

1155

1156

1157

1158

1159

1160

1161

1162

1163

1164

1165

1166

1167

1168

1169

1170

1171

1172

1173

1174

1175

1176

1177

1178

1179

1180

1181

1182

1183

1184

1185

1186

1187

1188

1189

1190

1191

1192

1193

1194

1195

1196

1197

1198

1199

1200

1201

1202

1203

1204

1205

1206

1207

1208

1209

1210

1211

1212

1213

1214

1215

1216

1217

1218

1219

1220

1221

1222

1223

1224

1225

1226

1227

1228

1229

1230

1231

1232

1233

1234

1235

1236

1237

1238

1239

1240

1241

1242

1243

1244

1245

1246

1247

1248

1249

1250

1251

1252

1253

1254

1255

1256

1257

1258

1259

1260

T


ARTICLE IN PRESS

1261

1262

1263

1264

1265

1266

1267

1268

1269

1270

1271

1272

1273

1274

1275

1276

1277

1278

1279

1280

1281

1282

1283

1284

1285

1286

1287

1288

1289

1290

1291

1292

1293

1294

1295

1296

1297

1298

1299

1300

1301

1302

1303

1304

1305

1306

1307

1308

1309

1310

1311

1312

1313

1314

1315

1316

1317

1318

1319

1320

1321

1322

1323

1324

1325

1326

1327

1328

1329

1330

1331

1332

1333

1334

1335

1336

1337

1338

1339

1340

1341

1342

1343

1344

1345

1346

1347

1348

1349

1350

1351

1352

1353

1354

1355

1356

1357

1358

1359

1360

1361

1362

1363

1364

1365

1366

1367

1368

1369

1370

1371

1372

1373

REC

which is not conserved in other g proteobacteria,whereas it is found in several distantly relatedgenomes (Figure 6(a) and (b)). These resultssuggested that organisms belonging to the samelifestyle have conserved similar network motifs. Totest the generality of this observation, we computedthe similarity in motif conservation among orga-nisms belonging to different lifestyles (Supplemen-tary Data S12). Figure 4(b), which represents thesimilarity in network motif conservation amongorganisms belonging to different lifestyles, againshows that the diagonal elements (average simi-larity in motif content among organisms within thesame lifestyle class) are much greater than the off-diagonal elements (average lifestyle similarityamong organisms belonging to different lifestyleclass). In this case, the LSI value is 1.34 (p 3!10K3;Z-scoreZ2.83), showing that organisms with thesame lifestyle share common motifs whencompared to organisms of different lifestyles. Thissuggests that, apart from the phylogenetic com-ponent in retaining interactions, organisms withsimilar lifestyles tend to conserve network motifs asa general principle.

Though motifs tend to be conserved withinlifestyle classes, we sought to know whetherinteractions in motifs are conserved preferentiallycompared to other interactions in the networkregardless of lifestyle class. To determine this, wecomputed a motif conservation index (C.I) for eachorganism (Supplementary Data M9). C.I is definedas the logarithm of the ratio of the fraction ofconserved interactions that forms a motif in E. colito the fraction of all interactions conserved. We thencarried out network simulation experiments wherewe: (a) selected for interactions in motifs; (b)neutrally selected interactions in the network; and(c) selected against interactions in motifs andobtained the trends for C.I. As shown inFigure 6(c), the observed trend of conservationindex for the 175 genomes is closest to a model ofunbiased removal of interactions, rather thanpreferential conservation or loss of interactions inmotifs. Thus, we find that there is no preferentialconservation of whole motifs or parts thereof(Supplementary Data S8). These results indicatethat interactions in motifs and whole motifs are notconserved preferentially when compared to otherinteractions in the network, which was unexpected.All these findings suggest that motif formation is

UNCORforward motif conservation profile for the 175 genomes. Theshown as columns. If all constituents of a motif are present inred, or in shades of red to blue reflecting the amount of conserthat different feed-forward motifs have been conserved to vardiagram of 43 single-input motifs and 70 multiple-input motifforward motifs. These parts of the Figure illustrate that netwovarious extents in the different genomes. A representation likehave similar conservation profiles. Two-way clustering, as dohave conserved similar sets of network motifs. Such informpatterns in poorly characterised organisms. (High-resolutiongenomes/madanm/evdy/.)


dynamic during evolution, and can easily bereshaped during adaptation to changes in lifestyle.

ED PROOF

Orthologous genes can come under the controlof different motifs in different organisms

Careful examination of partially conservedmotifs provided a possible answer as to whyinteractions in motifs are not specifically conserved.Our analysis revealed that orthologous genes canbecome part of different types of motifs in theregulatory network of different organisms by losingor gaining specific transcription factors (Figure 6(d);and Supplementary Data S9). This observationimplies that an orthologous gene in a differentorganism may acquire a different pattern of geneexpression in order to adapt better to changingenvironments. In this context, it is interesting tonote that a more recent study by Dekel and Alon45

has demonstrated that E. coli strains grown indifferent lactose environments optimize levels ofLacZ expression to increase its growth-rate bykeeping the cost of production low.46 Thus, ourresults lend strong support (at a genomic levelacross many organisms) that different organismsarrive at different solutions by tinkering withspecific regulatory interactions to optimizeexpression levels rather than porting whole blocksof pre-existing transcriptional interactions. Forexample, a gene that may need to be regulatedtightly (as a feed-forward motif) in one organism,might need to be regulated directly and quickly (asin a single-input motif) in a different genome due tochanges in lifestyle, development or environment.Specific cases are shown in Figure 6(d) (a completelist for every genome can be obtained from thesupplementary website; see Supplementary Data).An example of this is E. coli, which is adapted to alife with fixed aerobic and anaerobic phases, andwill not express the fumarate reductase genes (FrdBand FrdC, which converts fumarate to succinateunder anaerobic conditions to derive energy) unlessthere is persistent signal for lack of oxygen (througha feed-forward motif involving both Fnr andNarL). In contrast, Haemophilus influenzae, whichencounters rapid redox fluctuations during hostinfection and needs to regulate the fumaratereductase genes more quickly than E. coli, appearsto depend solely on Fnr for the response(by employing a single-input motif).

genomes are shown as rows and the different motifs area genome of interest, then the particular cell is coloured

vation (blue for completely absent). This Figure illustratesious extents in the different genomes. (c) and (d)) Clusters in the 175 genomes with the same representation as feed-rk motifs are not conserved as blocks but are conserved tothis can be extremely helpful in finding sets of motifs thatne here, also allows us to identify different genomes thatation can be helpful in understanding gene expressionimages are available at http://www.mrc-lmb.cam.ac.uk/

1374

1375

1376

1377

1378

1379

1380

1381

1382

1383

1384

1385

1386

http://www.mrc-lmb.cam.ac.uk/genomes/madanm/evdy/


UNCORRECTED PROOF

Figure 6. Evolution of local network structure. (a) A feed-forward motif formed by Fnr, NarL and NuoN genes inE. coli is completely conserved in a closely related genome, Salmonella typhi, but not in other g-proteobacterial genomes.Fnr is a regulatory hub and is not always conserved in genomes within the same phylogenetic group. This suggests thatcondition-specific regulatory hubs can either be lost or displaced by a non-orthologous protein in closely relatedgenomes. (b) Distantly related organisms that have conserved all interactions in the regulatory motif and that haveconserved the regulatory hub, Fnr. (c) This is a plot of fraction of genes conserved (x-axis) against conservation index(y-axis), which is a measure of the extent to which interactions in a motif are conserved. The conservation index (C.I.) iscalculated as the logarithm of the ratio of the fraction of conserved interactions that forms a motif in E. coli to the fractionof all interactions conserved. C.I.O0 means that interactions in motifs are selected for; C.I. close to 0 suggests total



ARTICLE IN PRESS

1387

1388

1389

1390

1391

1392

1393

1394

1395

1396

1397

1398

1399

1400

1401

1402

1403

1404

1405

1406

1407

1408

1409

1410

1411

1412

1413

1414

1415

1416

1417

1418

1419

1420

1421

1422

1423

1424

1425

1426

1427

1428

1429

1430

1431

1432

1433

1434

1435

1436

1437

1438

1439

1440

1441

1442

1443

1444

1445

1446

1447

1448

1449

1450

1451

1452

1453

1454

1455

1456

1457

1458

1459

1460

1461

1462

1463

1464

1465

1466

1467

1468

1469

1470

1471

1472

1473

1474

1475

1476

1477

1478

1479

1480

1481

1482

1483

1484

1485

1486

1487

1488

1489

1490

1491

1492

1493

1494

1495

1496

1497

1498

1499

1500

1501

1502

1503

1504

1505

1506

1507

1508

1509

1510

1511

1512


ARTICLE IN PRESS

1513

1514

1515

1516

1517

1518

1519

1520

1521

1522

1523

1524

1525

1526

1527

1528

1529

1530

1531

1532

1533

1534

1535

1536

1537

1538

1539

1540

1541

1542

1543

1544

1545

1546

1547

1548

1549

1550

1551

1552

1553

1554

1555

1556

1557

1558

1559

1560

1561

1562

1563

1564

1565

1566

1567

1568

1569

1570

1571

1572

1573

1574

1575

1576

1577

1578

1579

1580

1581

1582

1583

1584

1585

1586

1587

1588

1589

1590

1591

1592

In this context, we note that studies of duplicatedgenes within the transcriptional regulatory networkof E. coli and yeast have shown that network motifshave not evolved by duplication of whole ancestralmodules.47,48 These findings hint that the sameinteraction could have existed in different regulatorycontexts in ancestral genomes, which is in agreementwith our findings. Our conclusions are consistentwith the results of a recent study showing the lack ofconservation of higher order network modules,which are semi-independent units larger than net-workmotifs.49 This typeofflexibility in the regulatorycontext of a gene in aparticularprokaryotic cellmightbe viewed as analogous to the presence of multipledistinct pathways for context specific regulation oftarget genes across different cell typeswithin amulti-cellular organism.

1593

1594

1595

1596

1597

1598

1599

1600

1601

1602

1603

1604

1605

1606

1607

1608

Evolution of global network structure

We next sought to address how the fine-scale,independent evolution of interactions in networkmotifs affects the global structure of the network.Transcriptional regulatory networks have ahierarchical structure best approximated by thescale-free network model, in which the outgoingconnectivity of transcription factors follows a powerlaw yZaxKg, where y is the number of transcriptionfactors, x is the number of target genes and g is thescale-free exponent. In other words, there are fewtranscription factors that regulate many target genes.These influential transcription factors play the role ofregulatory hubs in the network.

T

1609

1610

1611

1612

1613

1614

1615

1616

1617

1618

1619
C
Transcriptional regulatory hubs evolve like othertranscription factors in the network

It was postulated that scale-free behaviour shouldhold good for randomly selected parts of networks ofthis form.14 In fact, the reconstructed transcriptionalnetworks for a majority of the genomes follow apower lawdistribution in their outgoing connectivity(Figure 7(a); and Supplementary Data S16). We then

UNCORREneutrality towards maintenance of motifs and C.I.!0 suggobtained by carrying out network simulation where interacselected against are shown in blue, green and red. The plot ofthat form the network motif are not selected for or against in einteraction in the network. This implies that evolution actinteraction in different genomes. (d) Analysis of partially contranscription factors, orthologous genes in different genomesrequirements. Genes in a feed-forwardmotif (FFM) in E. coli calosing a transcription factor (shown in grey). Feed-forward msensitive to fluctuations in input signals. Whereas a SIM regulsome input signal, like small molecules. Genes in a multiple-input motif (SIM) by losing a transcription factor. Genes regtranscribed only when two input signals cross particular thfactor, genes that are tightly regulated can be regulated in a siregulated as a part of an MIM by the loss of a transcriptioninteractions to create different motifs in genomes when ortholthe Figure illustrates that by bringing about temporal exprescan follow different patterns of gene expression, be it in a dif


ED PROOF

asked if this is because transcription factors occupy-ing hubs in the network are preferentially conserved.Earlier studies on protein–protein interaction net-works have suggested that the proteinswith themostinteractions tend to be more conserved acrossorganisms.50,51 We explored this possible linkbetween connectedness of a hub and its conservationby comparing the observed retention of regulatoryinteractions to simulated models of network evol-ution where transcription factors are lost in anunbiased manner, and models where hubs arepreferentially conservedor lost. Theobservedpatternis closest to the unbiased removal of transcriptionfactors and is independent of the number of theirtarget genes in the E. coli network. This implies thatthere is no correlation between the degree ofconnectedness of a particular transcription factorand its conservation across genomes, as shown inFigure 7(b). Table 1 provides specific examples oftranscription factors with many target genes that arepoorly conserved across genomes and transcriptionfactors with low connectivity that are conserved inmany genomes.A possible explanation for the absence of any bias

for conservation of influential transcription factorsis suggested by our recent investigation of regula-tory hubs in yeast. Most of these regulatory hubs arecondition-specific, i.e. they regulate many genesonly in particular conditions, but remain silent inother conditions.52,53 Most transcription factors inprokaryotes are condition-specific, in that theyrespond to a specific environmental signal.54 If thelifestyle of the organism does not involve aparticular condition, then that regulatory proteincan be lost. For example, in the opportunisticpathogen P. aeruginosa, which actively utilizesphenolic compounds, the transcriptional regulatorsMhpR, HcaR and FeaR can sense the compoundsand activate target genes that that encode enzymesinvolved in their catabolism. However, moreobligate pathogens like Staphylococcus aureus andCampylobacter jejuni, which do not typically facephenolic compounds in their natural niches, lackboth the regulators and their target genes for the

ests selection against such interactions. The C.I. trendstions in motifs were selected for, neutrally selected andC.I. for the 175 genomes (in black) shows that interactionsvolution and are conserved to the same extent as any others by tinkering to form different motifs using the sameserved motifs revealed that by losing (or gaining) specificcould be expressed in different ways according to specificn be regulated as a part of a single-input module (SIM) byotif regulation ensures that target gene expression is notation ensures expression of target genes as long as there isinput motif (MIM) in E. coli can be regulated as a single-ulated as a part of a MIM ensures that target genes areresholds independently. Thus, by losing a transcriptionmple manner. Genes regulated as a part of an FFM can befactor. Thus, evolution tinkers with specific regulatory

ogous genes need to be expressed differently. Generically,sion of specific transcription factors, the same target geneferent cell type or in different organisms.

1620

1621

1622

1623

1624

1625

1626

1627

1628

1629

1630

1631

1632

1633

1634

1635

1636

1637

1638

NCORRECTED PROOF

Figure 7. Evolution of global network structure. (a) The fraction of genes conserved (x-axis) is plotted against the scale-free exponent value (g) of the conserved networks (y-axis) for the different genomes, in black. g is the scale-free exponentin the power law equation yZ axKg, where y is the number of transcription factors and x is the number of target genes.The average value of g obtained after simulating a neutral model of network evolution 10,000 times is shown in green.This plot shows that for genomes that conserve about 30–60% of the genes, the exponent value (g) is higher than inrandom networks, suggesting a more pronounced hub-like behaviour. (b) The fraction of transcriptional regulatoryinteractions conserved in the network (x-axis) is plotted against the fraction of the genes conserved (y-axis) for each ofthe 175 genomes (yZ0.007x2K1.7843xC100, R2Z0.95). To understand what the observed trendmeans, we simulated theprocess of network evolution by incorporating different rules: (i) remove genes with low connectivity first, implyingselection to conserve highly connected transcription factors, shown in blue; (ii) remove genes neutrally with no specialemphasis on connectivity, implying neutral evolution, shown in green; and (iii) remove nodes with high connectivityfirst, implying selection. against highly connected regulatory proteins, shown in red. We find that the observed trend ismore similar to the trend that we obtain while simulating the neutral condition. (c) Reconstructed transcriptionalregulatory networks for H. influenzae and B. pertussis is shown to illustrate that the scale-free-like structure is stillconserved, even though regulatory hubs can be lost or replaced. For example, the distribution for the outgoingconnection for the conserved network in H. influenzae is yZ214.8xK0.49 even though a regulatory hub NarL is lost.


ARTICLE IN PRESS

1639

1640

1641

1642

1643

1644

1645

1646

1647

1648

1649

1650

1651

1652

1653

1654

1655

1656

1657

1658

1659

1660

1661

1662

1663

1664

1665

1666

1667

1668

1669

1670

1671

1672

1673

1674

1675

1676

1677

1678

1679

1680

1681

1682

1683

1684

1685

1686

1687

1688

1689

1690

1691

1692

1693

1694

1695

1696

1697

1698

1699

1700

1701

1702

1703

1704

1705

1706

1707

1708

1709

1710

1711

1712

1713

1714

1715

1716

1717

1718

1719

1720

1721

1722

1723

1724

1725

1726

1727

1728

1729

1730

1731

1732

1733

1734

1735

1736

1737

1738

1739

1740

1741

1742

1743

1744

1745

1746

1747

1748

1749

1750

1751

1752

1753

1754

1755

1756

1757

1758

1759

1760

1761

1762

1763

1764

Uutilization of these aromatic compounds. Theseobservations suggest an important principle ofadaptation at the regulatory level: the adaptivevalue of an orthologous transcription factor might


be different across different organisms, dependingon the environment, thereby affecting the number ofgenes regulated by it and the overall networkstructure. These findings support an interesting

H

T

OOF

Table 1. Conservation and connectivity profile for transcription factors

Transcriptionfactor GI number

Low connectivity(k%15)

High conservation(R50%) Function

A. Proteins with few target genes (connectivity, k%15) but conserved in more than 50% of the genomes studied

Ada 16130150 5 74.86 Transcriptional regulator of DNA repairBirA 16131807 5 89.71 Biotin operon repressorMarR 33347561 7 54.29 Repressor of multiple antibiotic resistance

(Mar) operonDnaA 16131570 10 87.43 Transcriptional regulator of house-keeping

genesCpxR 16131752 14 55.43 Regulator of a 2CSTa

OmpR 16131282 14 57.14 Regulator of adaptive response (2CSTa)NarP 16130130 15 60.57 Regulator of aerobic respiration (2CSTa)

Transcrip-tion factor GI number

High connectivity(kO15)

Low conservation(!50%) Function

B. Proteins with many target genes (connectivity, kO15) but conserved in less than 50% of the genomes studied

FhlA 16130638 16 34.29 Formate hydrogen lyase activatorRob 16132213 17 24.00 Transcriptional activator for antibiotic resistanceCysB 16129236 18 25.14 Regulator of cysteine biosynthesisFruR 16128073 25 21.14 Fructose operon repressorHns 16129198 26 14.29 General regulatorPurR 16129616 29 33.14 Purine biosynthesis repressorFis 16131149 34 28.00 Regulator of rRNA and tRNA operonsLrp 16128856 53 44.57 Leucine responsive regulatory proteinNarL 16129184 66 36.00 Regulator of anaerobic respirationArcA 16132218 70 40.00 Regulator of aerobic respirationIhf 16129668 96 37.71 General regulatorFnr 16129295 110 49.14 Tanscriptional regulator of aerobic, anaerobic

respiration and osmotic balance

a 2CST, two-component signal transduction system.


ARTICLE IN PRESS

1765

1766

1767

1768

1769

1770

1771

1772

1773

1774

1775

1776

1777

1778

1779

1780

1781

1782

1783

1784

1785

1786

1787

1788

1789

1790

1791

1792

1793

1794

1795

1796

1797

1798

1799

1800

1801

1802

1803

1804

1805

1806

1807

1808

1809

1810

1811

1812

1813

1814

1815

1816

1817

1818

1819

1820

1821

1822

1823

1824

1825

1826

1827

1828

1829

1830

1831

1832

1833

1834

1835

1836

1837

1838

1839

1840

1841

1842

1843

1844

1845

1846

1847

1848

1849

1850

1851

1852

1853

1854

1855

1856

1857

1858

1859

1860

1861

1862

1863

1864

1865

1866

1867

1868

1869

1870

1871

1872

1873

1874

1875

1876

1877

1878

1879

1880

1881

1882
CORREC
hypothesis proposed by Wagner on adaptation,evolvability and robustness and in living systems55

and an elegant work by Hittinger et al., where theyreport gene inactivation, and loss as being associ-ated with adaptation to new ecological niches inyeast.56

The binding affinity and specificity of a transcrip-tion factor and its target site can be affected byrelatively small changes in the DNA-binding inter-face of the transcription factor, or in the bindingsite.57,58 As a result, DNA-binding domains couldevolve new target sites relatively easily, resulting inrapid de novo emergence of new transcriptionalinteractions. This could be another reason for thehigh level of variability of transcriptional regulatoryinteractions in evolution. Protein–protein inter-actions, on the other hand, involve larger interfacesand hence more mutations are needed to alter them.Indeed, Maslov et al.59 have shown that paralogousproteins in yeast that participate in transcriptionalregulatory interactions evolve new interactionsrapidly when compared to paralogous proteinsthat participate in protein–protein interactions.

1883

1884

1885

1886

1887

1888

1889

1890

UNScale-free structure emerges independentlyduring evolution

Our analysis of the reconstructed networksreveals that even though particular regulatoryhubs may be lost or possibly replaced, as shownin Figure 7(c) for Haemophilus influenzae and


ED PRBordetella pertussis, the distribution of outgoing

connectivity is still best approximated by a power-law function. It is evident From Figure 7(a) that theexponent (g in the fit yZaxKg) increases in genomeswhere the fraction of conserved nodes is less than60%, and this is statistically significant (Supplemen-tary Data M7). This implies that a hierarchicalstructure, with a connectivity distribution approxi-mated by a power law, is still retained in poorlyconserved networks (Figure 6(c)). The fact that thereconstructed networks are still power-law-like intheir distribution, but with a different exponentvalue when compared to random networks of asimilar size, suggests selection for different proteinsto be regulatory hubs. This may result in theindependent emergence of new network structuresthat resemble scale-free networks. However, forphylogenetically close genomes that have con-served more than 60% of the genes, the values ofthe exponent are similar to that of the ancestralnetwork (Figure 7(a)).To further investigate the prevalence of network

topology resembling a scale-free structure in otherbacteria, we analysed the extent to which thestructure of the regulatory network changes inB. subtilis. For this, we compiled information aboutthe known transcriptional regulatory network ofB. subtilis from DBTBS.60 By comparing it with theE. coli network, we found that even though theyshare a similar set of target genes, the individualtranscriptional factors have been replaced to

UNCORRECTED PROOFFigure 8.Comparisonof theknownB. subtilisand theE. coli transcriptional regulatorynetwork.Regulatoryhubs foreachnetworkare shownseparately.Thenumber inparenthesesrepresents the number of target genes for the transcription factor. Relevant information and the outgoing connectivity distribution are given below each network. (a) The B. subtilisnetwork has a scale-free structure with CcpA as its major regulatory hub. The closest hit in E. coli is the CytR protein (cytidine repressor belonging to the LacI family of repressors),which has only ten target genes. CcpA is the major carbon catabolite repressor protein that interacts with its co-repressor Hpr-P protein and controls genes involved in carbonmetabolism. (b) TheE. colinetworkhasCrp as itsmajor regulatory hub and it has a scale-free structure. Crp is a functional analogue of theCcpAprotein inB. subtilis. It regulates genesinvolved in carbonmetabolismbut is not evolutionarily related toCcpA.There is noCrp orthologue inB. subtilis and this organismdoesnot sense cAMP likeE. coli. The closestmatchtoCrp fromE. coli inB. subtilis is Fnr,which is known to regulate ten target genes inB. subtilis. Thus,we see how twoorganismswith the ability to carry out specific carbonmetabolismhave evolved their own set of regulatory hubs andmaintained the scale-free structure. This supports the argument that the scale-free-like structure evolved independently in the twogenomes.

YJM

BI58077—

25/2/

2006—18:47—

SATHYA—202231—

XML–pp.1–20

/GH

ARTICLEIN

PRESS

1891

1892

1893

1894

1895

1896

1897

1898

1899

1900

1901

1902

1903

1904

1905

1906

1907

1908

1909

1910

1911

1912

1913

1914

1915

1916

1917

1918

1919

1920

1921

1922

1923

1924

1925

1926

1927

1928

1929

1930

1931

1932

1933

1934

1935

1936

1937

1938

1939

1940

1941

1942

1943

1944

1945

1946

1947

1948

1949

1950

1951

1952

1953

1954

1955

1956

1957

1958

1959

1960

1961

1962

1963

1964

1965

1966

1967

1968

1969

1970

1971

1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

UNCORRECTED PROOFFigure 9. Evolution of network structure at three levels of organization. Three levels of organization of network structure. (a) At the level of genes and interactions,transcription factors tend to evolve more rapidly than their target genes. This, coupled with the observation that different genomes evolve their own transcription factors, maymean that they sense and respond to different signals in their changing environments. At the level of regulatory interactions, organisms with similar lifestyles conserve similarregulatory interactions, indicating the strong influence of environment on gene regulation. (b) At the local level, interactions in motifs evolve like any other interaction in thenetwork. By losing or gaining individual transcription factors, orthologous genes can come under the influence of different motifs and may hence code for a different pattern ofgene expression. Even though motifs are not conserved as whole units, organisms with similar lifestyles do retain similar motifs. (c) At the global level, regulatory hubs that arelifestyle-specific are lost as rapidly as other transcription factors in the network. Even though hubs can be lost or replaced, organisms with different lifestyles evolve a similarscale-free structure where different proteins emerge as hubs, as dictated by their lifestyle. All observations suggest that transcriptional regulatory networks in prokaryotes arevery flexible and adapt rapidly to changes in environment by tinkering individual interactions to arrive at an optimal design.

YJM

BI58077—

25/2/

2006—18:47—

SATHYA—202231—

XML–pp.1–20

/GH

ARTICLEIN

PRESS

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

2031

2032

2033

2034

2035

2036

2037

2038

2039

2040

2041

2042

2043

2044

2045

2046

2047

2048

2049

2050

2051

2052

2053

2054

2055

2056

2057

2058

2059

2060

2061

2062

2063

2064

2065

2066

2067

2068

2069

2070

2071

2072

2073

2074

2075

2076

2077

2078

2079

2080

2081

2082

2083

2084

2085

2086

2087

2088

2089

2090

2091

2092

2093

2094

2095

2096

2097

2098

2099

2100

2101

2102

2103

2104

2105

2106

2107

2108

2109

2110

2111

2112

2113

2114

2115

2116

2117

2118

2119

2120

2121

2122

2123

2124

2125

2126

2127

2128

2129

2130

2131

2132

2133

2134

2135

2136

2137

2138

2139

2140

2141

2142


ARTICLE IN PRESS

2143

2144

2145

2146

2147

2148

2149

2150

2151

2152

2153

2154

2155

2156

2157

2158

2159

2160

2161

2162

2163

2164

2165

2166

2167

2168

2169

2170

2171

2172

2173

2174

2175

2176

2177

2178

2179

2180

2181

2182

2183

2184

2185

2186

2187

2188

2189

2190

2191

2192

2193

2194

2195

2196

2197

2198

2199

2200

2201

2202

2203

2204

2205

2206

2207

2208

2209

2210

2211

2212

2213

2214

2215

2216

2217

2218

2219

2220

2221

2222

2223

2224

2225

a considerable extent. However, the overall powerlaw-like distribution in the outgoing connectivity ofthe network has been maintained in the B. subtilisnetwork. This comparison revealed that differentproteins act as regulatory hubs in the two genomes.For example, CcpA and Crp are the two regulatoryhubs in B. subtilis and E. coli, respectively control-ling many genes involved in carbon metabolism.Both have very different modes of regulation andare not evolutionarily related, suggesting indepen-dent innovation of regulatory hubs to regulateorthologous target genes. We find also that proteinsthat are regulatory hubs in the E. coli referencenetwork are either absent or regulate very fewtarget genes in other organisms. These observationsprovide additional support for the suggestion thatthe hierarchical structure of these networks hasconverged to an architecture similar to scale-freenetworks, albeit with independently recruitedregulatory hubs (Figure 8).

T

† http://www.ncbi.nlm.nih.gov/‡ http://genome-www5.stanford.edu/

2226

2227

2228

2229

2230

2231

2232

2233

2234

2235

2236

2237

2238

2239

2240

2241

2242

2243

2244

2245

2246

2247

2248

2249

2250

2251

2252

2253

2254

2255

2256

2257

2258

2259

2260

2261

2262

2263

2264

2265

2266

2267

2268

UNCORREC

Conclusions

We present the first comprehensive analysis ofthe evolution of transcriptional regulatory networksat three distinct levels of organization by comparingthe conservation of an experimentally establishedreference network of 1295 interactions across 175microbial genomes (computationally analyzingw500,000 protein sequences). At the level ofindividual genes, we show that target genes aremore conserved across genomes than transcriptionfactors, and the conservation of a target gene and itstranscription factor are uncoupled, unlike the tightcorrelation of conservation patterns in physicallyinteracting protein pairs. Each organism hasevolved its own set of transcription factors,suggesting that a major factor in adaptation tonew environment is the emergence of distinctrepertoires of transcription factors, which probablyintegrate new inputs (Figure 9(a)). At the local level,there is no preferential conservation of interactionswithin network motifs, or of whole network motifs.However, it appears that by losing or conservingspecific transcriptional regulators, orthologousgenes in different genomes can be incorporatedwithin different regulatory contexts and can therebyeasily exhibit different patterns of gene expression.We find also that organisms with similar lifestylehave similar motif content and interactions in theirconserved networks (Figure 9(b)). Thus, naturalselection appears to tinker with individual inter-actions to arrive at an optimal design for a givenorganism. At the level of global network topology,we see that conservation of transcription factors isindependent of the number of target genes theyregulate, and depends on the lifestyle of theorganism rather than the phylogenetic distancefrom E. coli (Figure 9(c)). Additionally, a compari-son of the known regulatory networks of E. coli andB. subtilis confirms the above observations andreveals that different proteins act as regulatory


hubs in the two genomes. This, coupled with ourobservation that the reconstructed networks havedifferent power law exponents, suggests that theoverall tendency towards a scale-free like behaviouris an emergent property in evolution.

With advancement in large-scale experimentalstudies to identify transcriptional regulatory net-works, we believe that the general methods wepresent here will be useful in studying any set ofnetworks and genomes. These results and predic-tions can serve as a scaffold for experimental studieson transcriptional control in poorly characterizedgenomes, and could be relevant for designing ChIp-chip experiments for pathogens and in engineeringof regulatory interactions for organisms withbiotechnological value.

ED PROOF

Materials and Methods

Detailed descriptions of the methods are given in theSupplementary Data.

Dataset

The E. coli transcriptional regulatory network consis-ting of 112 transcription factors, 711 target genes and 1295regulatory interactions was assembled from the Regu-lonDB,17 and from data in the literature.3,33 Proteinsequences for the 176 completely sequenced organismswere downloaded from the NCBI website†. Theexpression dataset for benchmarking predicted regulat-ory interactions was downloaded from the StanfordMicroarray Database‡. The known transcriptional regu-latory network for B. subtilis was obtained from DBTBS.60

Information about lifestyle for the organisms wasobtained from the NCBI genome information websiteand from the Bergeys manual of Bacteriology.

Identification and analysis of conservedtranscriptional regulatory networks

The E. coli regulatory network was used as thereference network to study network evolution in the 175other organisms. The detailed algorithm to reconstructtranscriptional networks and the procedure to identifyorthologous genes are available as Supplementary Data(methods M1 and M2). Validations for the interactiontransfer procedure are available as Supplementary Data(S2 and S11). Network motifs were identified usingstandard algorithms. The algorithm to compare networksand the procedures of the relevant statistical tests tomeasure significance are available as SupplementaryData (M5 to M10). The procedure to comparelifestyle similarity and motif or interaction conservationfor the 175 organisms is described in Supplementary Data(S12).

Uncited Reference

32.

H

http://www.ncbi.nlm.nih.gov/

http://genome-www5.stanford.edu/


ARTICLE IN PRESS

2269

2270

2271

2272

2273

2274

2275

2276

2277

2278

2279

2280

2281

2282

2283

2284

2285

2286

2287

2288

2289

2290

2291

2292

2293

2294

2295

2296

2297

2298

2299

2300

2301

2302

2303

2304

2305

2306

2307

2308

2309

2310

2311

2312

2313

2314

2315

2316

2317

2318

2319

2320

2321

2322

2323

2324

2325

2326

2327

2328

2329

2330

2331

2332

2333

2334

2335

2336

2337

2338

2339

2340

2341

2342

2343

2344

2345

Acknowledgements

M.M.B. and L.A. gratefully acknowledge theIntramural research program of National Institutesof Health, USA for funding their research. M.M.B.acknowledges the MRC Laboratory of MolecularBiology, Trinity College, Cambridge, CambridgeCommonwealth Trust and the National Institute ofHealth Visitor Program for financial support. Wethank Dr Nakai and Yuko Makita for sending usinformation on the B. subtilis network. We thank DrsN. Luscombe, C. Chothia, P. TenWolde, L. LoConte,L. M. Iyer, V. Pisupati, S. Balaji, S. Maslau andmembers of our groups for reading the manuscript.

2346

2347

2348

2349

2350

2351

2352

2353

2354

2355

Supplementary Data

Supplementary data associated with this articlecan be found, in the online version, at doi:10.1016/j.jmb.2006.02.019

Supplementary methods, information and thepredictions are available at: http://www.mrc-lmb.cam.ac.uk/genomes/madanm/evdy/

T

2356

2357

2358

2359

2360

2361

2362

2363

2364

2365

2366

2367

2368

2369

2370

2371

2372

2373

2374

2375

2376

2377

2378

2379

2380

2381

2382

2383

2384

2385

2386

2387

2388

2389

2390

2391

2392

2393

2394

UNCORREC

References

1. Barabasi, A. L. & Oltvai, Z. N. (2004). Networkbiology: understanding the cell’s functional organiz-ation. Nature Rev. Genet. 5, 101–113.

2. Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N.,Chklovskii, D. & Alon, U. (2002). Network motifs:simple building blocks of complex networks. Science,298, 824–827.

3. Shen-Orr, S. S., Milo, R., Mangan, S. & Alon, U. (2002).Network motifs in the transcriptional regulationnetwork of Escherichia coli. Nature Genet. 31, 64–68.

4. Lee, T. I., Rinaldi, N. J., Robert, F., Odom, D. T., Bar-Joseph, Z., Gerber, G. K. et al. (2002). Transcriptionalregulatory networks in Saccharomyces cerevisiae.Science, 298, 799–804.

5. Guelzim, N., Bottani, S., Bourgine, P. & Kepes, F.(2002). Topological and causal structure of the yeasttranscriptional regulatory network. Nature Genet. 31,60–63.

6. Kalir, S. & Alon, U. (2004). Using a quantitativeblueprint to reprogram the dynamics of the flagellagene network. Cell, 117, 713–720.

7. McAdams, H. H., Srinivasan, B. & Arkin, A. P. (2004).The evolution of genetic regulatory systems inbacteria. Nature Rev. Genet. 5, 169–178.

8. Wall, M. E., Hlavacek, W. S. & Savageau, M. A. (2004).Design of gene circuits: lessons from bacteria. NatureRev. Genet. 5, 34–42.

9. Carroll, S. (2005). Endless Forms Most Beautiful: TheNew Science of Evo Devo and the Making of the AnimalKingdom, W.W. Norton & Co., ???.

10. Harbison, C. T., Gordon, D. B., Lee, T. I., Rinaldi, N. J.,Macisaac, K. D., Danford, T. W. et al. (2004).Transcriptional regulatory code of a eukaryoticgenome. Nature, 431, 99–104.

11. Madan Babu, M., Luscombe, N. M., Aravind, L.,Gerstein, M. & Teichmann, S. A. (2004). Structure andevolution of transcriptional regulatory networks.Curr. Opin. Struct. Biol. 14, 283–291.


ED PROOF

12. Milo, R., Itzkovitz, S., Kashtan, N., Levitt, R., Shen-Orr, S., Ayzenshtat, I. et al. (2004). Superfamilies ofevolved and designed networks. Science, 303,1538–1542.

13. Albert, R., Jeong, H. & Barabasi, A. L. (2000). Errorand attack tolerance of complex networks. Nature,406, 378–382.

14. Barabasi, A. L. & Albert, R. (1999). Emergence ofscaling in random networks. Science, 286, 509–512.

15. Oltvai, Z. N. & Barabasi, A. L. (2002). Systems biology.Life’s complexity pyramid. Science, 298, 763–764.

16. Ravasz, E., Somera, A. L., Mongru, D. A., Oltvai, Z. N.& Barabasi, A. L. (2002). Hierarchical organization ofmodularity in metabolic networks. Science, 297,1551–1555.

17. Salgado, H., Gama-Castro, S., Martinez-Antonio, A.,Diaz-Peredo, E., Sanchez-Solano, F., Peralta-Gil, M.et al. (2004). RegulonDB (version 4.0): transcriptionalregulation, operon organization and growth con-ditions in Escherichia coli K-12. Nucl. Acids Res. 32,D303–D306.

18. Alkema, W. B., Lenhard, B. & Wasserman, W. W.(2004). Regulog analysis: detection of conservedregulatory networks across bacteria: application toStaphylococcus aureus. Genome Res. 14, 1362–1373.

19. Rajewsky, N., Socci, N. D., Zapotocky, M. & Siggia,E. D. (2002). The evolution of DNA regulatory regionsfor proteo-gamma bacteria by interspecies compari-sons. Genome Res. 12, 298–308.

20. McGuire, A. M., Hughes, J. D. & Church, G. M. (2000).Conservation of DNA regulatory motifs and discov-ery of new motifs in microbial genomes. Genome Res.10, 744–757.

21. Kellis, M., Patterson, N., Endrizzi, M., Birren, B. &Lander, E. S. (2003). Sequencing and comparison ofyeast species to identify genes and regulatoryelements. Nature, 423, 241–254.

22. Yu, H., Luscombe, N. M., Lu, H. X., Zhu, X., Xia, Y.,Han, J. D. et al. (2004). Annotation transfer betweengenomes: protein-protein interologs and protein–DNA regulogs. Genome Res. 14, 1107–1118.

23. Kelley, B. P., Yuan, B., Lewitter, F., Sharan, R.,Stockwell, B. R. & Ideker, T. (2004). PathBLAST: atool for alignment of protein interaction networks.Nucl. Acids Res. 32, W83–W88.

24. Mazurie, A., Bottani, S. & Vergassola, M. (2005). Anevolutionary and functional assessment of regulatorynetwork motifs. Genome Biol. 6, R35.

25. Rodionov, D. A., Dubchak, I., Arkin, A., Alm, E. &Gelfand, M. S. (2004). Reconstruction of regulatoryand metabolic pathways in metal-reducing delta-proteobacteria. Genome Biol. 5, R90.

26. Rodionov, D. A., Vitreschak, A. G., Mironov, A. A. &Gelfand, M. S. (2002). Comparative genomics ofthiamin biosynthesis in procaryotes. New genes andregulatory mechanisms. J. Biol. Chem. 277,48949–48959.

27. Sharan, R., Suthram, S., Kelley, R. M., Kuhn, T.,McCuine, S., Uetz, P. et al. (2005). Conserved patternsof protein interaction in multiple species. Proc. NatlAcad. Sci. USA, 102, 1974–1979.

28. Espinosa, V., Gonzalez, A. D., Vasconcelos, A. T.,Huerta, A.M. &Collado-Vides, J. (2005). Comparativestudies of transcriptional regulation mechanisms in agroup of eight gamma-proteobacterial genomes.J. Mol. Biol. 354, 184–199.

29. Herrgard, M. J., Covert, M. W. & Palsson, B. O. (2004).Reconstruction of microbial transcriptional regulat-ory networks. Curr. Opin. Biotechnol. 15, 70–77.

http://dx.doi.org/doi:10.1016/j.jmb.2006.02.019

http://dx.doi.org/doi:10.1016/j.jmb.2006.02.019



T


ARTICLE IN PRESS

2395

2396

2397

2398

2399

2400

2401

2402

2403

2404

2405

2406

2407

2408

2409

2410

2411

2412

2413

2414

2415

2416

2417

2418

2419

2420

2421

2422

2423

2424

2425

2426

2427

2428

2429

2430

2431

2432

2433

2434

2435

2436

2437

2438

2439

2440

2441

2442

2443

2444

2445

2446

2447

2448

2449

2450

2451

2452

2453

2454

2455

2456

2457

2458

2459

2460

2461

2462

2463

2464

2465

2466

2467

2468

2469

2470

2471

2472

2473

2474

2475

2476

2477

2478

2479

2480

2481

2482

2483

2484

2485

2486

2487

2488

2489

2490

2491

2492

2493

2494

2495

2496

2497

2498

2499

2500

2501

2502

2503

2504

2505
REC
30. Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N. &Barabasi, A. L. (2000). The large-scale organization ofmetabolic networks. Nature, 407, 65164–65165.

31. Pagel, P., Mewes, H. W. & Frishman, D. (2004).Conservation of protein-protein interactions—lessonsfrom ascomycota. Trends Genet. 20, 72–76.

32. Aravind, L., Watanabe, H., Lipman, D. J. & Koonin,E. V. (2000). Lineage-specific loss and divergence offunctionally linked genes in eukaryotes. Proc. NatlAcad. Sci. USA, 97, 11319–11324.

33. Madan Babu, M. & Teichmann, S. A. (2003). Evolutionof transcription factors and the gene regulatorynetwork in Escherichia coli. Nucl. Acids Res. 31,1234–1244.

34. van Nimwegen, E. (2003). Scaling laws in thefunctional content of genomes. Trends Genet. 19,479–484.

35. Ranea, J. A., Buchan, D. W., Thornton, J. M. & Orengo,C. A. (2004). Evolution of protein superfamilies andbacterial genome size. J. Mol. Biol. 336, 871–887.

36. Aravind, L., Anantharaman, V., Balaji, S., Babu, M. M.& Iyer, L. M. (2005). The many faces of the helix-turn-helix domain: transcription regulation and beyond.FEMS Microbiol. Rev. 29, 231–262.

37. Hedges, S. B. (2002). The origin and evolution ofmodel organisms. Nature Rev. Genet. 3, 838–849.

38. Andersson, J. O. & Andersson, S. G. (1999). Insightsinto the evolutionary process of genome degradation.Curr. Opin. Genet. Dev. 9, 664–671.

39. Cerdeno-Tarraga, A., Thomson, N. & Parkhill, J.(2004). Pathogens in decay. Nature Rev. Microbiol. 2,774–775.

40. Sallstrom, B. & Andersson, S. G. (2005). Genomereduction in the alpha-Proteobacteria. Curr. Opin.Microbiol. 8, 579–585.

41. Thomson, N., Bentley, S., Holden, M. & Parkhill, J.(2003). Fitting the niche by genomic adaptation.Nature Rev. Microbiol. 1, 92–93.

42. Morett, E., Korbel, J. O., Rajan, E., Saab-Rincon, G.,Olvera, L., Olvera, M. et al. (2003). Systematicdiscovery of analogous enzymes in thiamin biosyn-thesis. Nature Biotechnol. 21, 790–795.

43. Eisenberg, D., Marcotte, E. M., Xenarios, I. & Yeates,T. O. (2000). Protein function in the post-genomic era.Nature, 405, 823–826.

44. Mangan, S. & Alon, U. (2003). Structure and functionof the feed-forward loop network motif. Proc. NatlAcad. Sci. USA, 100, 11980–11985.

45. Dekel, E. & Alon, U. (2005). Optimality and evol-utionary tuning of the expression level of a protein.Nature, 436, 588–592.

UNCO


ED PROOF

46. Elena, S. F. & Lenski, R. E. (2003). Evolutionexperiments with microorganisms: the dynamicsand genetic bases of adaptation. Nature Rev. Genet. 4,457–469.

47. Teichmann, S. A. & Madan Babu, M. (2004). Generegulatory network growth by duplication. NatureGenet. 36, 492–496.

48. Conant, G. C. & Wagner, A. (2003). Convergentevolution of gene circuits. Nature Genet. 34, 264–266.

49. Snel, B. & Huynen, M. A. (2004). Quantifyingmodularity in the evolution of biomolecular systems.Genome Res. 14, 391–397.

50. Yu, H., Greenbaum, D., Xin Lu, H., Zhu, X. &Gerstein, M. (2004). Genomic analysis of essentialitywithin protein networks. Trends Genet. 20, 227–231.

51. Jeong, H., Mason, S. P., Barabasi, A. L. & Oltvai, Z. N.(2001). Lethality and centrality in protein networks.Nature, 411, 41–42.

52. Luscombe, N. M., Babu, M. M., Yu, H., Snyder, M.,Teichmann, S. A. & Gerstein, M. (2004). Genomicanalysis of regulatory network dynamics reveals largetopological changes. Nature, 431, 308–312.

53. Balazsi, G., Barabasi, A. L. & Oltvai, Z. N. (2005).Topological units of environmental signal processingin the transcriptional regulatory network of Escher-ichia coli. Proc. Natl Acad. Sci. USA, 102, 7841–7846.

54. Martinez-Antonio, A., Salgado, H., Gama-Castro, S.,Gutierrez-Rios, R. M., Jimenez-Jacinto, V. & Collado-Vides, J. (2003). Environmental conditions andtranscriptional regulation in Escherichia coli: a physio-logical integrative approach. Biotechnol. Bioeng. 84,743–749.

55. Wagner, A. (2005). Robustness and Evolvability inLiving Systems.

56. Hittinger, C. T., Rokas, A. & Carroll, S. B. (2004).Parallel inactivation of multiple GAL pathway genesand ecological diversification in yeasts. Proc. NatlAcad. Sci. USA, 101, 14144–14149.

57. Luscombe, N. M. & Thornton, J. M. (2002). Protein–DNA interactions: amino acid conservation and theeffects of mutations on binding specificity. J. Mol. Biol.320, 991–1009.

58. Mirny, L. A. & Gelfand, M. S. (2002). Structuralanalysis of conserved base pairs in protein-DNAcomplexes. Nucl. Acids Res. 30, 1704–1711.

59. Maslov, S., Sneppen, K., Eriksen, K. A. & Yan, K. K.(2004). Upstream plasticity and downstream robust-ness in evolution of molecular networks. BMC Evol.Biol. 4, 9.

60. Makita, Y., Nakao, M., Ogasawara, N. & Nakai, K.(2004). DBTBS: database of transcriptional regulationin Bacillus subtilis and its contribution to comparativegenomics. Nucl. Acids Re.s, 32, D75–D77.

2506

2507

2508
R Edited by R. Ebright
2509

2510

2511
(Received 1 December 2005; received in revised form 6 February 2006; accepted 7 February 2006)
H

2512

2513

2514

2515

2516

2517

2518

2519

2520

evolutionary dynamics of prokaryotic transcriptional ... · uncorrected proof evolutionary dynamics...

Documents