A computational study of bacterial gene regulation and adaptation ...

More documents

Recommendations

Info

Downloaded from genome.cshlp.org on November 29, 2008 - Published by Cold Spring Harbor Laboratory PressSeshasayee et al.tions by integrating regulatory information into metabolic fluxbalance simulations (Covert and Palsson 2002; Herrgard et al.2003; Covert et al. 2004; Barrett et al. 2005; Shlomi et al. 2007a).It has also been used to characterize recurring patterns of TF–target gene interactions termed “network motifs” that confer differentkinetic properties to metabolic circuits (Shen-Orr et al.2002; Mangan et al. 2003, 2006; Zaslaver et al. 2004; Alon 2007).In parallel, three genome-scale investigations have madeimportant contributions to our understanding of general principlesthat underlie transcriptional regulation of the metabolicsystem (Ihmels et al. 2004; Kharchenko et al. 2005; Notebaart etal. 2008). In Saccharomyces cerevisiae, Ihmels et al. (2004) combinedlarge-scale gene expression data with the metabolic network todemonstrate that transcriptional regulation ensures coherent metaboliteflow between sequential enzymes. Kharchenko et al. (2005)reported, again in yeast, that enzymes show more similar expressionprofiles if they are closer together in the metabolic network.Very recently in both S. cerevisiae and E. coli, Notebaart et al. (2008)argued that strong correlations in metabolite flow calculatedfrom flux balance simulations are better predictors of coexpressionthan simple distance separation in the metabolic network.The above studies are largely based on the metabolic andregulatory apparatus of S. cerevisiae. Although both S. cerevisiae andE. coli are unicellular organisms, their regulatory machineries arevastly different (Fink 1987). For example, whereas more than halfof all E. coli metabolic regulators are activated by small-moleculebinding (Anantharaman et al. 2001; Madan Babu and Teichmann2003), most eukaryotic TFs respond to complex signalingcascades (Reece et al. 2006). It is unclear if the findings are applicableto prokaryotes, and it is important to perform an independentstudy focused on a bacterial system.Here we study, on a genomic scale, how the transcriptionalregulatory system controls small-molecule metabolism in E. coli.First, we investigate the regulation of different types of metabolicpathways on a global scale, and also introduce a functional hierarchyof TFs. Next, we examine regulatory patterns at a localscale, by assessing how gene expression is mediated for neighboringmetabolic reactions, with special attention on the controlof pathway junctions. Finally, we study the evolution of metabolicpathways and evaluate whether there is a relationship betweenthe coordinated regulation and the conservation of enzymes.In doing so, we establish rules of transcriptional regulatorysystem that are generally applicable to bacterial metabolicsystems.Results and DiscussionOur study uses five distinct data sets:1. A metabolic network comprising 788 reactions mapped onto781 enzyme genes and 628 small molecules from the EcoCycdatabase (Keseler et al. 2005);2. a transcriptional regulatory network involving 111 TFs regulating388 enzyme genes (49.7% of all enzymes) via 913 regulatoryinteractions, sourced from RegulonDB (Salgado et al.2006);3. 43 binding interactions between 40 TFs and 39 small moleculesfrom EcoCyc representing post-translational regulationof TF activity;4. Affymetrix microarray data covering 221 mRNA hybridizationsacross diverse cellular conditions from the M3D database(Faith et al. 2007); and5. protein sequences for E. coli K12 MG1655 and 380 other prokaryoticorganisms with completely sequenced genomes obtainedfrom the KEGG database (Kanehisa et al. 2006).The first four data sets contain information for E. coli K12 only.Global regulation of metabolic enzymesHierarchy of general and specific TFsThe E. coli transcriptional regulatory network was previouslyshown to have a pyramid-shaped hierarchical topology, with afew master TFs at the top level regulating lower-level TFs (Ma etal. 2004; Balazsi et al. 2005; Yu and Gerstein 2006). Recent studieshave further shown that metabolic pathways are regulated byshorter cascades of TFs than functions such as motility and biofilmformation (Shen-Orr et al. 2002; Martinez-Antonio et al.2008). Here we show that the regulation of the metabolic systemis also hierarchical in terms of the functionality of the targetedenzymes (Fig. 1). All the results presented in this paper are robustagainst perturbations in the underlying data set (i.e., introductionof errors and deletion of data points), indicating that ourfindings are likely to remain valid as data sets are updated in thefuture (Methods).At the top of the hierarchy, general TFs regulate genes belongingto multiple functional categories as described by theCOG classification system (Tatusov et al. 2003). Ten of the 111TFs in our data set belong to this group, and it includes six of theseven global TFs described in an earlier publication (Martínez-Antonio and Collado-Vides 2003). Despite the breadth of theirregulation, all these TFs display a statistical enrichment for asingle functional category that reflects the nature of the inputsignal sensed by the TF concerned. For example, Lrp binds leucineand preferentially targets amino acid metabolism. Even Fisand IHF—which do not explicitly contain signal-sensing domains,but whose activities are growth-phase-dependent (AliAzam et al. 1999)—favor regulation of energy metabolism (Blotet al. 2006).On the other hand, specific TFs restrict their target genes tothose in the same EcoCyc pathway or the same functional category:54 TFs are pathway specific, and 18 others are functionspecific (i.e., regulate more than one pathway but only those ofsingle functional category). Again, the activities of many specificTFs are post-translationally controlled through small-moleculebinding (Anantharaman et al. 2001; Madan Babu and Teichmann2003); however, here, the regulatory metabolite tends tooriginate from the pathway that is targeted by the relevant TF,effectively forming a local feedback loop. A classic example is theLacI repressor of the lactose utilization operon: On binding allolactose,this TF immediately affects the corresponding catabolicpathway. We could not classify the remaining 29 TFs, as therewere too few targets with metabolic COG or pathway annotationsin the current data set.In direct relation to the diversity of target gene functions,we find that general TFs regulate more genes than specific TFs;thus the topological and functional hierarchies of the regulatorynetwork are closely linked. The two classes of TFs also differ inseveral other ways (Supplemental Fig. 1):1. General TFs display higher mRNA expression levels than specificTFs when we examine data from Affymetrix GeneChips(P Mann-Whitney = 5.6 10 5 ). This reflects the fact that cellsrequire larger absolute quantities of general TFs since they2 Genome Researchwww.genome.org
Downloaded from genome.cshlp.org on November 29, 2008 - Published by Cold Spring Harbor Laboratory PressRegulation and evolution of E. coli metabolismhave to bind many more targets(Lozada-Chavez et al. 2008). For example,it is well known that CRP,which targets the entire carbohydratemetabolism system, is present in1400–6600 copies per cell (Ishizuka etal. 1993), compared with LacI, whichcontrols only lactose degradation andis present in only 10–20 copies percell (Gilbert and Muller-Hill 1966).2. In line with previous observations(Menchaca-Mendez et al. 2005), wealso observe that the genes of specificTFs tend to be encoded much closerto their target binding sites, in contrastto general TFs (P Mann-Whitney
Page 3 and 4:
Statement of lengthThis thesis does
Page 5 and 6:
molecules also bind to enzymes dire
Page 7 and 8:
Table of ContentsTitlePagesIntroduc
Page 9 and 10:
TitlePageFigure 4.5: Catalytic and
Page 11 and 12:
IntroductionFunctional and comparat
Page 13 and 14:
egulation owing to the large amount
Page 15 and 16:
(A) Closed complex formationRNA pol
Page 17 and 18:
Intuitively negative supercoiling,
Page 19 and 20:
I.3. Transcription factors: domain
Page 21 and 22:
I.4. Transcriptional regulatory net
Page 23 and 24:
(Balazsi et al. 2005). Gene express
Page 25 and 26:
metabolism / carbon nutrition with
Page 27 and 28:
I.4.5. Signal sensing in transcript
Page 29 and 30:
I.5. Non-transcriptional output: cy
Page 31 and 32:
transcriptional initiation by c-di-
Page 33 and 34:
phosphatase activity of the kinases
Page 35 and 36:
I.7. ConclusionIn this chapter, we
Page 37 and 38:
Chapter 1: Global regulators of bac
Page 39 and 40:
Page 41 and 42:
Page 43 and 44:
Page 45 and 46:
Page 47 and 48:
Page 49 and 50:
Page 51 and 52:
Page 53 and 54:
Page 55 and 56:
Page 57 and 58:
Page 59 and 60:
Chapter 2: Transcriptional control
Page 61 and 62:
Page 63 and 64:
Page 65 and 66:
Page 67 and 68:
Page 69 and 70:
Page 71 and 72:
Page 73 and 74:
Page 75 and 76:
Page 77 and 78:
Page 79 and 80:
Page 81 and 82:
Page 83 and 84:
Page 85 and 86:
Page 87 and 88:
Chapter 3: Transcriptional and post
Page 89 and 90:
Page 91 and 92:
Page 93 and 94:
Page 95 and 96:
Page 97 and 98:
Page 99 and 100:
Page 101 and 102:
Page 103 and 104:
Page 105 and 106:
Chapter 4: Comparative genomics of
Page 107 and 108:
Page 109 and 110:
Page 111 and 112:
Page 113 and 114:
Page 115 and 116:
Page 117 and 118:
Page 119 and 120:
Page 121 and 122:
Page 123 and 124:
Page 125 and 126:
Page 127 and 128:
Page 129 and 130:
Page 131 and 132:
Page 133 and 134:
Page 135 and 136:
two-domain architectures wherethese
Page 137 and 138:
Page 139 and 140:
Page 141 and 142:
Page 143 and 144:
Page 145 and 146:
Page 147 and 148:
Page 149 and 150:
(A)60% of genomes from class4530150
Page 151 and 152:
Page 153 and 154:
Discussion and conclusionsDiscussio
Page 155 and 156:
Discussion and conclusionsD1.1.3. C
Page 157 and 158:
Discussion and conclusionsD2. Futur
Page 159 and 160:
BibliographyAmikam, D., Steinberger
Page 161 and 162:
BibliographyBlattner, F.R., Plunket
Page 163 and 164:
BibliographyChen, S.L., Hung, C.S.,
Page 165 and 166:
BibliographyEchols, N., Harrison, P
Page 167 and 168:
Bibliographysystem level analysis o
Page 169 and 170:
BibliographyHerring, C.D., Raghunat
Page 171 and 172:
BibliographyJenal, U., and Malone,
Page 173 and 174:
BibliographyKeseler, I.M., Collado-
Page 175 and 176:
BibliographyLee, S.H., Angelichio,
Page 177 and 178:
BibliographyMcClelland, M., Sanders
Page 179 and 180:
BibliographyO Croinin, T., Carroll,
Page 181 and 182:
BibliographyPrice, M.N., Dehal, P.S
Page 183 and 184:
BibliographySatriano, J., and Schlo
Page 185 and 186:
BibliographySteinberger, O., Lapido
Page 187 and 188:
BibliographyUssery, D.W., Wassenaar
Page 189 and 190:
BibliographyYanofsky, C. 1981. Atte
Page 191 and 192:
Appendix 1Appendix 1.1: Differences
Page 193 and 194:
!!!!!!!!!!!! !!!!!!!!!!!!!!!!!!!!!!
Page 195 and 196:
Appendix 1Appendix 1.5: Gene expres
Page 197 and 198:
Appendix 2Appendix 2.1: Differences
Page 199 and 200:
Appendix 2Appendix 2.3: Co-regulati
Page 201 and 202:
Appendix 2Appendix 2.5: Flux coupli
Page 203 and 204:
Appendix 2Appendix 2.7: Co-regulati
Page 205 and 206:
Appendix 2Appendix 2.8B: Connectivi
Page 207 and 208:
Appendix 2Appendix 2.10: Correlatio
Page 209 and 210:
Appendix 2Appendix 2.12A: Robustnes
Page 212 and 213:
Appendix 3Appendix 3Supplementary m
Page 214 and 215:
Reaction ID Regulatory metabolite I
Page 216 and 217:
Appendix 3Appendix 3.3B: Direct reg
Page 218 and 219:
Appendix 3Appendix 3.4B: Direct reg
Page 220 and 221:
Appendix 4Appendix 4Supplementary m
Page 222 and 223: Appendix 4Motile organisms are rich
Page 224: Appendix 4We can expand the analysi
Page 227 and 228: Appendix 5Appendix 5.1A: PFAMs for
Page 229 and 230: Appendix 5Appendix 5.1C: PFAMs for
Page 231 and 232: Appendix 5Appendix 5.2B: Small-mole
Page 233 and 234: Appendix 5Appendix 5.3: Organisms s
Page 235 and 236: Appendix 5Appendix 5.5: Partner dom
Page 237 and 238: Appendix 5fraction of SDRRs would a
Page 239 and 240: Appendix 5Appendix 5.6C: Non-hybrid
Page 241 and 242: Appendix 5Appendix 5.8: List of org
Page 244 and 245: Appendix 6Appendix 6List of genomes
Page 246 and 247: Appendix 648. Mycobacterium tubercu
Page 248 and 249: Appendix 6144. Bacillus cereus ATCC
Page 250 and 251: Appendix 6240. Nitrobacter winograd
Page 252 and 253: Appendix 6336. Cytophaga hutchinson
Page 254 and 255: Appendix 6432. Acinetobacter bauman
Page 256 and 257: Appendix 6528. Enterobacter sakazak
Page 258 and 259: Appendix 6624. Polynucleobacter nec
Page 260 and 261: Appendix 7Appendix 7List of genomes
Page 262 and 263: Appendix 748. Bordetella parapertus
Page 264 and 265: Appendix 7144. Haemophilus somnus14
Page 266 and 267: Appendix 7240. Prochlorococcus mari
Page 268 and 269: Appendix 7336. Synechococcus sp. CC
Page 270 and 271: Downloaded from genome.cshlp.org on
Page 284 and 285: JOURNAL OF BACTERIOLOGY, June 2008,
Page 286 and 287: VOL. 190, 2008 P. MIRABILIS GENOME
Page 294: VOL. 190, 2008 P. MIRABILIS GENOME
Page 297 and 298: 2 Nucleic Acids Research, 2008PCR a
Page 299 and 300: 4 Nucleic Acids Research, 2008Table
Page 301 and 302: 6 Nucleic Acids Research, 2008Table
Page 303 and 304: 8 Nucleic Acids Research, 2008solve
Page 305 and 306: 524 Mol Genet Genomics (2008) 279:5
Page 317 and 318: 4378 J.J. James et al. / FEBS Lette
Page 324 and 325:
An Assessment of the Role of DNA Ad
Page 326 and 327:
DAM and Transcriptionpotential of s
show all

A computational study of bacterial gene regulation and adaptation ...

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?