Transcription Networks Ildefonso Cases (CNB-CSIC)

Transcription Networks

Ildefonso Cases (CNB-CSIC)

SummarySummary

Concepts in TranscriptionConcepts in Transcription

Transcription NetworksTranscription Networks Definition, properties and evolutionDefinition, properties and evolution

Transcription Networks vs Functional NetworksTranscription Networks vs Functional Networks

Evolution of Regulatory StructuresEvolution of Regulatory Structures

Transcription and AdaptationTranscription and Adaptation

Regulación de la TranscripciónRegulación de la Transcripción

• Resultado de la interacción entre proteínas y Resultado de la interacción entre proteínas y DNA. DNA.

• El conjunto de proteínas que se unan a su región El conjunto de proteínas que se unan a su región promotora (directa o indirectamente) va a promotora (directa o indirectamente) va a determinar la expresión de un gen:determinar la expresión de un gen:

En que tejidos En que tejidos

En que momento del desarrolloEn que momento del desarrollo

Bajo que condiciones ambientalesBajo que condiciones ambientales

etc.etc.

Transcripción en BacteriasTranscripción en Bacterias

Transcripción en Bacterias: Transcripción en Bacterias: Factores SigmaFactores Sigma

Escherichia coliEscherichia coli

sigma70/Dsigma70/D

sigma32/H: heat shocksigma32/H: heat shock

sigma24/E: ECFsigma24/E: ECF

sigma28: flagelosigma28: flagelo

sigma38/S:fase estacionaria,stresssigma38/S:fase estacionaria,stress

sigma54/N: nitrógeno y otrossigma54/N: nitrógeno y otros

fecI: hierrofecI: hierro

Pseudomonas putida: > 15Pseudomonas putida: > 15

Streptomyces: > 30Streptomyces: > 30

Transcripción en BacteriasTranscripción en Bacterias

Transcripción en Bacterias: Transcripción en Bacterias: OperonesOperones

Transcripción en EukariotasTranscripción en Eukariotas

Transcripción en EukariotasTranscripción en Eukariotas

Transcripción en ArcheasTranscripción en Archeas

•Maquinaria basal EukariotaMaquinaria basal Eukariota

•Reguladores eukariotas y bacterianosReguladores eukariotas y bacterianos

Otras fuentes de RegulaciónOtras fuentes de Regulación

ElongaciónElongación

Estabilidad del mRNAEstabilidad del mRNA

etc.etc.

Transcription NetworksTranscription Networks

Transcription Networks

0,01

0,1

1

1 10 100 1000

0,01

0,1

1

1 10 100 1000

Regulators regulates:

genes

p(k)=akp(k)=ak-b-b

Scale-free Scale-free NetworksNetworks

Resistant to ErrorResistant to Error

Sensitive to Sensitive to AttackAttack

Network Properties

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.YeastYeast

Guelzim et al. 2002 Nature Genet. 31:60-63

Preferential AttachmentPreferential Attachment

1

2

3

Network evolutionNetwork evolution

Duplicated Duplicated Genes are often Genes are often co-expressedco-expressed

and share and share regulator binding regulator binding sitessites

van Noort et al., 2004 EMBO Rep 5(3):280-4

Binding sites EvolutionBinding sites Evolution

Papp et al,2003. Trends Genet 19:417

Milo et al,2002. Science 298:824

MotivesMotives

MotivesMotives

Milo et al,2002. Science 298:824

Motives ProfilingMotives Profiling

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are needed to see this picture.

Milo et al. 2004 Science 303:1538-1542

Overlapping MotivesOverlapping Motives

Bi-fan y FFL often share nodes and Bi-fan y FFL often share nodes and edgesedges

Dobrin et al,2004. BMC Bioiformatics 5:10

Motives EvolutionMotives Evolution

Conant & Wagner,2003. Nat Genet. 34:264

Motives PropertiesMotives Properties

Shen-Orr et al.,2002. Nat Genet. 31:64

Coregulation Network Coregulation Network

gamma≈-1gamma≈-1

c=0.6c=0.6

scale-freescale-free

small worldsmall world

van Noort et al., 2004 EMBO Rep 5(3):280-4

Network Evolution SimulationNetwork Evolution Simulation

van Noort et al., 2004 EMBO Rep 5(3):280-4

In the absence In the absence of selection of selection we can we can reproduce a reproduce a network with network with similar similar propertiesproperties

van Noort et al., 2004 EMBO Rep 5(3):280-4

Network Evolution SimulationNetwork Evolution Simulation

Trancription Networks DynamicsTrancription Networks Dynamics

Luscombe et al., 2004 Nature 431:308

Trancription Networks DynamicsTrancription Networks Dynamics

Luscombe et al., 2004 Nature 431:308

Luscombe et al., 2004 Nature 431:308

Trancription Networks DynamicsTrancription Networks Dynamics

Endogenous Exogenous

Combining NetworksCombining Networks

Regulatory Networks vs.Regulatory Networks vs.

Functional NetworksFunctional Networks

Functional AssociationsFunctional Associations

• Protein ComplexesProtein Complexes• Enzymes …. RibosomesEnzymes …. Ribosomes

• Information/Biochemical PathwaysInformation/Biochemical Pathways• Metabolic ProgramsMetabolic Programs

• Anaerobic… Aerobic MetabolismAnaerobic… Aerobic Metabolism

• Biological ProcessesBiological Processes• Transcription … RecombinationTranscription … Recombination

Relation between functional Relation between functional associations and co-regulation?associations and co-regulation?

““co-regulated genes are co-regulated genes are functionally associated”functionally associated”

PrecedentsPrecedents

• Pairs of interacting proteins are more frequent Pairs of interacting proteins are more frequent

among co-expressed genes in among co-expressed genes in S. cerevisiaeS. cerevisiae

• 50% of the pairs of co-expressed genes belong 50% of the pairs of co-expressed genes belong

to the same biochemical pathway in to the same biochemical pathway in S. S.

cerevisiaecerevisiae and more than 30% in and more than 30% in C. elegansC. elegans

• In In E. coliE. coli and and B. subtilisB. subtilis genes in operons (and genes in operons (and

thus presumably co-expressed) tend to belong thus presumably co-expressed) tend to belong

to the same general class of cellular functionto the same general class of cellular function

EcocycEcocyc

• Protein Complexes and sub-complexesProtein Complexes and sub-complexes• Biochemical Pathways Biochemical Pathways

• Pathways and Super-pathwaysPathways and Super-pathways

• Regulatory informationRegulatory information• Transcription Units Transcription Units • Regulatory ProteinsRegulatory Proteins

• Regulons: Genes directly regulated by the same protein in Regulons: Genes directly regulated by the same protein in the same waythe same way

• Super-regulons: also include indirect interactionsSuper-regulons: also include indirect interactions

Correlated?Correlated?

• Functional Functional AssociationsAssociations

• ComplexesComplexes• PathwaysPathways• SuperpathwaysSuperpathways

• Regulatory Regulatory AssociationsAssociations

• Transcription UnitsTranscription Units• RegulonsRegulons• Supe-regulonsSupe-regulons

Coding functional associationsCoding functional associations

A

B

CA

B

C

A B

E

G

F

CA

B

C

E F

G

A B C D

A 0 1 1 0

B 1 0 1 0

C 1 1 0 0

D 0 0 0 0

Coding Regulatory associationsCoding Regulatory associations

C

DBA

A B C

D

A B A

B

C

A

C D

B

A B

C D

A B C D

A 0 1 0 0

B 1 0 1 0

C 0 1 0 0

D 0 0 0 0C

A C D E

A 0 1 0 1

C 1 0 1 0

D 0 1 0 1

E 1 0 1 0

A B C D

A 0 0 1 1

B 0 0 1 0

C 1 1 0 1

D 1 0 1 0

A C D

A 0 1 0

C 1 0 1

D 0 1 0

A C D

A 0 1 1

C 1 0 1

D 1 1 0

A C D

A 0 1 0

C 1 0 1

D 0 1 0

OriginalOriginalMatricesMatrices

ReducedReducedMatricesMatrices

Ia=2

Ib=3

Iab/Ia=2/2=100%

Iab/Ib=2/3=66%

GeneGene NetworkNetwork

Functional Functional Assoc.Assoc.

Ice = Ia*Ib/(N*(N-1)/2)

Complexes vs. Transcription UnitsComplexes vs. Transcription Units282 genes, 87% and 85%, 80 times more than expected

ExceptionsExceptions

MtlAMtlA

GatAGatA

GatBGatB

GatCGatC

PtsHPtsH

PtsIPtsI

ExceptionsExceptions

Evolutionary Implications?

Pathways vs. Transcription UnitsPathways vs. Transcription Units330 genes, 94% and 26%, 35 times more than expected

Transcription Units per PathwayTranscription Units per Pathway

0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

0,4

0,45

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

A B

E

G

F

C

A

B

C

E F

G

A

B

C

E F

G

66%66%

26%26%

Complexes vs. RegulonsComplexes vs. Regulons209 genes, 10% and 97%, 7 times more than expected

Pathways vs. RegulonPathways vs. Regulon258 genes, 18% and 77%, 4 times more than expected

16%3.1

15%3.5

7%4.7

SR

20%3.8

18%4.2

10%6.9

RE

94%28.0

94%35.4

87%79.2

TU

SPPC

78%86%97%SR

71%77%97%RE

20%26%85%TU

SPPC

0

20

40

60

80

100

Functional associatio

n

Gene N

etw

ork

DBA

A B C

DC

15%2.8

13%3.2

6%4.1

GRGR

20%3.8

18%4.2

10%6.9

RERE

80%87%97%GRGR

71%77%97%RERE

SPSPPPCC SPSPPPCC

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

NO 0 705 460 1165

Super-path. 0 20 9 29

Pathway 24 422 58 504

COMPLEX 164 7 0 171

TU Regulon Super-regulon ALL

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

NO 3 40 12 55

Super-reg. 0 67 0 67

Regulon 7 422 20 449

TU 164 24 0 188

Complex PathwaySuper-

pathway ALL

ConclusionsConclusions

• Subunits of protein complexes are often in the Subunits of protein complexes are often in the same transcription unitsame transcription unit

• Pathways are spread in several transcription Pathways are spread in several transcription units, which contains linear sub-pathways and units, which contains linear sub-pathways and are often co-regulatedare often co-regulated

• Expression of pathway branches is often Expression of pathway branches is often coordinatedcoordinated

• The tighter the functional association The tighter the functional association the tighter the mechanism of co-the tighter the mechanism of co-regulationregulation

Evolution of regulonsEvolution of regulons

Regulatory Structures has functional senseRegulatory Structures has functional sense

How regulons are assemble during evolution?How regulons are assemble during evolution?

Genome AGenome B Genome C Genome D Genome F Genome G Genome H

Sigma54Sigma54

Sigma54 regulon: Sigma54 regulon: ““relatively easy” to predict relatively easy” to predict

well distributed in the bacterial treewell distributed in the bacterial tree

good number : 10-100 per genomegood number : 10-100 per genome

Distribution of sigma54Distribution of sigma54

Aquifex aeolicus D. radiodurans T. maritima

E. coliS. typhiY. pestisV. choleraeP. aeurginosaBuchnera sp.H. influenzaeP. multocida

N. meningiditisR. solanacearum

H. pyloriC. jejuni

S. melilotiM. lotiA. tumafaciensB. melitensisC. crescentus

R. prowazekiiR. conorii

T. pallidumB. burgdorferiC. trachomatis

Actinobacteria

B. subtilisL. inocua

S. aureusS. pyogenesM. neumoniae

conserved sigma54-regulationconserved sigma54-regulation

COG0174 Glutamine synthase

COG0347 Nitrogen regulatory protein PII

COG0642 Signal transduction histidine kinase

COG0683 ABC-type branched-chain amino acid transport systems,

periplasmic component

COG0834 ABC-type amino acid transport system,

periplasmic component

COG1301 Na+/H+-dicarboxylate symporters

COG1815 Flagellar basal body protein

COG2513 PEP phosphonomutase and related enzymes

COG4992 Ornithine/acetylornithine aminotransferase

phylogenetic profilesphylogenetic profiles

GlnAGlnA

GlnKGlnK

His-KiHis-Ki

LivKLivK

HisJHisJ

GltPGltP

FlgBFlgB

PrpBPrpB

ArgDArgD

alp

ha b

et

a gam

ma gra

m

+

aquifex

delt

a-

epsi

lon

Evolution Sigma54 regulonEvolution Sigma54 regulon

Sigma54 regulon is very dynamicSigma54 regulon is very dynamic

Expression of genes transcribed from sigma54 Expression of genes transcribed from sigma54 promoters is couple to physiological conditionspromoters is couple to physiological conditions

Are Genes required to be coupled to Are Genes required to be coupled to physiological conditions different in different physiological conditions different in different bacterial species?bacterial species?

How regulation reflects life-style?How regulation reflects life-style?

Bacteria LifestylesBacteria Lifestyles

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• Enrichment in Transcriptional Regulators of the Enrichment in Transcriptional Regulators of the

Pseudomonas aeruginosa Pseudomonas aeruginosa Genome Genome

Cellular Processes and Bacterial Cellular Processes and Bacterial LifestyleLifestyle

• Transport, Metabolism and TranscriptionTransport, Metabolism and Transcription

• Three sets of proteins from Three sets of proteins from E. coliE. coli

• 396 Transcription-associated proteins as annotated in Swissprot396 Transcription-associated proteins as annotated in Swissprot

• 548 Small-molecules Metabolism Enzymes from EcoCyc548 Small-molecules Metabolism Enzymes from EcoCyc

• 647 Transporters from EcoCyc647 Transporters from EcoCyc

• Blast against all available sequenced genomes classified by lifestyleBlast against all available sequenced genomes classified by lifestyle

60 genomes60 genomes15 0bligate intracellular pathogens and endosymbionts:Buchnera sp., APS, Chlamydia pneumoniae, AR39, Chlamydia pneumoniae, CWL029, Chlamydia pneumoniae, J138, Chlamydia trachomatis, MoPn, Chlamydia trachomatis, serovar D, Mycoplasma genitalium, G-37, Mycoplasma pulmonis, UAB CTIP, Mycobacterium leprae, TN, Mycobacterium tuberculosis, CDC1551, Mycobacterium tuberculosis, Hv37, Rickettsia conorii, Malish 7, Rickettsia prowazekii, Madrid E, Ureaplasma urealyticum, serovar 3

29 Pathogens ( all organisms reported to produce a disease in plants or animals):Pseudomonas aeruginosa, PAO1, Pasteurella multocida, Pm70, Ralstonia solanacearum, Staphylococcus aureus, Mu50, Staphylococcus aureus, N315 2624, Salmonella enterica serovar Typhi, CT18, Salmonella enterica serovar Typhimurium, LT2, Streptococcus pneumoniae, TIGR4, Streptococcus pneumoniae, R6, Streptococcus pyogenes M18, MGAS8232, Streptococcus pyogenes M1, SF370, Vibrio cholerae, El Tor N16961, Xylella fastidiosa, 9a5c, Yersinia pestis, CO92, Treponema pallidum, Nichols, Agrobacterium tumefaciens, C58, Borrelia burgdorferi, B31, Brucella melitensis, M16, Campylobacter jejuni, NCTC 11168, Clostridium perfringens, str. 13, Escherichia coli O157:H7, EDL933, Escherichia coli 0157:H7, RIMD0509952, Fusobacterium nucleatum, ATCC 25586, Haemophilus influenzae, KW20, Helicobacter pylori, 26695, Helicobacter pylori, J99, Listeria monocytogenes, EGD-e, Neisseria meningitidis, MC58, Neisseria meningitidis, Z2491

12 Free-living organisms: Anabaena sp., strain PCC 7120, Bacillus subtilis, 168, Caulobacter crescentus, CB15, Clostridium acetobutylicum, ATCC 824, Corynebacterium glutamicum, Escherichia coli, MG1655, Lactococcus lactis, IL1403, Listeria innocua, CLIP 11262, Mesorhizobium loti, MAFF303099, Sinorhizobium meliloti, strain 1021, Streptomyces coelicolor, A3(2), Synechocystis sp., PCC6803).

4 Extemophiles:Deinococcus radiodurans, R1, Aquifex aeolicus, VF5 1553,Thermotoga maritima, MSB8, Bacillus halodurans, C-125

The problem of phylogenetic The problem of phylogenetic distancesdistances

• 30 set of randomly selected proteins30 set of randomly selected proteins

S = logS = log22Hits / Hits of Random setHits / Hits of Random set

∑∑Hit / ∑Hits of Random setHit / ∑Hits of Random set

• Negative values = UNDERREPRESENTATIONNegative values = UNDERREPRESENTATION

• Positive values = OVERREPRESENTATIONPositive values = OVERREPRESENTATION

TransportTransport

-1.2 -1.0

-1.0 -0.8

-0.8 -0.6

-0.6 -0.4

-0.4 -0.2

-0.20.0

0.00.2

0.20.4

0.40.6

0.60.8

0.81.0

1.01.2

0

0,1

0,2

0,3

0,4

0,5

0,6

Free living organisms Pathogens Extremophiles Intracellular

Small-molecules MetabolismSmall-molecules Metabolism

-1.2 -1.0

-1.0 -0.8

-0.8 -0.6

-0.6 -0.4

-0.4 -0.2

-0.20.0

0.00.2

0.20.4

0.40.6

0.60.8

0.81.0

1.01.2

0

0,1

0,2

0,3

0,4

0,5

0,6

Free living organisms Pathogens Extremophiles Intracellular

Intracellular Pathogens and Intracellular Pathogens and symbionts enriched in Small symbionts enriched in Small

metabolism enzymes !!metabolism enzymes !!

TranscriptionTranscription

-1.2 -1

-1.0 -0.8

-0.8 -0.6

-0.6 -0.4

-0.4 -0.2

-0.20.0

0.00.2

0.20.4

0.40.6

0.60.8

0.81.0

1.01.2

0

0,1

0,2

0,3

0,4

0,5

0,6

Free living Organisms Pathogens Extremophiles Intracellular

Free-living bacteria require more Free-living bacteria require more regulators since they face more regulators since they face more

diverse conditionsdiverse conditions

Predictive power?Predictive power?

• Can we use these parameter to classify bacterial species?Can we use these parameter to classify bacterial species?

Combining TRANSC & SMMB Combining TRANSC & SMMB ScoresScores

-1

-0,8

-0,6

-0,4

-0,2

0

0,2

0,4

0,6

0,8

1

-1 -0,8 -0,6 -0,4 -0,2 0 0,2 0,4 0,6 0,8 1

TRANSC Score

SM

MB

Score

Intracellular Free living Organisms Pathogens

ECOL

SENT

PAER

HPYL

NMEN

SAUR

ConclusionsConclusions

• Effects of Bacterial lifestyle can be Effects of Bacterial lifestyle can be

observed even at low resolutionobserved even at low resolution

• Metabolism and Transcription-related Metabolism and Transcription-related

protein content can be use as lifestyle protein content can be use as lifestyle

descriptors to differentiate SPECIALIST descriptors to differentiate SPECIALIST

and GENERALIST Bacteriaand GENERALIST Bacteria

Convergence between Convergence between Extremophiles and EndosymbiontsExtremophiles and Endosymbionts

-1

-0,8

-0,6

-0,4

-0,2

0

0,2

0,4

0,6

0,8

1

-1 -0,8 -0,6 -0,4 -0,2 0 0,2 0,4 0,6 0,8 1

TRANSC Score

SMMB Score

Extremophyles Intracellular

Does it hold with 114 Does it hold with 114 Genomes?Genomes?

PathogensIntracellularExtremophilesFree living

June 2002:60June 2002:60 June 2003:114June 2003:114

•Broader Phylogenetic Broader Phylogenetic distribution distribution •Broader ecological Broader ecological distributiondistribution

TranscriptionTranscription

-1.2 -1

-1.0 -0.8

-0.8 -0.6

-0.6 -0.4

-0.4 -0.2

-0.20.0

0.00.2

0.20.4

0.40.6

0.60.8

0.81.0

1.01.2

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Free living Organisms Pathogens Extremophiles Intracellular

Small Molecule MetabolismSmall Molecule Metabolism

-1.2 -1.0

-1.0 -0.8

-0.8 -0.6

-0.6 -0.4

-0.4 -0.2

-0.20.0

0.00.2

0.20.4

0.40.6

0.60.8

0.81.0

1.01.2

0

0.1

0.2

0.3

0.4

Free living organisms Pathogens Extremophiles Intracellular

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1

Taps Score

Enz Score

Pathogens Extremophyles Intracellular Free living Organisms

Sargasso Sea MetagenomeSargasso Sea MetagenomeVenter Venter et al.et al.,2004. Science,2004. Science Apr 2;304(5667):66-74Apr 2;304(5667):66-74

1.045 Mb1.045 Mb

1.2 Millions new 1.2 Millions new ORFsORFs

from ~1400 from ~1400 different speciesdifferent species

~140 new~140 new

metabolismmetabolism 184850184850 15%15%

informationinformation 2596525965 2%2%

Venter et al.,2004. Science Apr 2;304(5667):66-74

ThanksThanks

AdriAdrià Garrigaà Garriga Guillermo CarbajosaGuillermo Carbajosa

Victor de Lorenzo (CNB)Victor de Lorenzo (CNB) Christos Ouzounis (EBI-EMBL, UK)Christos Ouzounis (EBI-EMBL, UK)