adaptive evolution in prokaryotic transcriptional regulatory networks

Adaptive evolution in prokaryotic transcriptionalregulatory networks

M. Madan Babu, PhD

NCBI, NLMNational Institutes of Health

Networks in Biology

Nodes

Links

Interaction

A

B

Network

Proteins

Physical Interaction

Protein-Protein

A

B

Protein Interaction

Metabolites

Enzymatic conversion

Protein-Metabolite

A

B

Metabolic

Transcription factorTarget genes

TranscriptionalInteraction

Protein-DNA

A

B

Transcriptional

Evolution of the regulatory network across organisms

Evolution of local network structure (motifs)

Structure of the transcriptional regulatory networkComponents, local & global structure

Outline

Evolution of components in the network (genes and interactions)

Evolution of global network structure (scale-free structure)

Structure of the transcriptional regulatory network

Scale free network(Global level)

all transcriptionalinteractions in a cellAlbert & Barabasi

Madan Babu M, Luscombe N, Aravind L, Gerstein M & Teichmann SACurrent Opinion in Structural Biology (2004)

Motifs(Local level)

patterns ofInterconnections

Uri Alon & Rick Young

Basic unit(Components)transcriptional

interaction

Transcriptionfactor

Target gene

Properties of transcriptional networks

Local level: Transcriptional networks are made up of motifswhich perform information processing task

Global level: Transcriptional networks are scale-free conferring robustness to the system

Transcriptional networks are made up of motifs

Single inputMotif

- Co-ordinates expression- Enforces order in expression- Quicker response

ArgR

Arg

D

Arg

E

Arg

F

Multiple inputMotif

- Integrates different signals- Quicker response

TrpR TyrR

AroM AroL

Network Motif

“Patterns ofinterconnections

that recur at different parts and

with specificinformation

processing task”

Feed ForwardMotif

- Responds to persistent signal - Filters noise

Crp

AraC AraBAD

Function

Shen-Orr et. al. Nature Genetics (2002) & Lee et. al. Science (2002)

N (k) k

1

Scale-free structure

Presence of few nodes with many links and many

nodes with few links

Transcriptional networks are scale-free

Scale free structure provides robustness to the system

Albert & Barabasi, Rev Mod Phys (2002)

Scale-free networks exhibit robustness

Robustness – The ability of complex systems to maintain their function even when the structure of the system changes significantly

Tolerant to random removal of nodes (mutations)

Vulnerable to targeted attack of hubs (mutations) – Drug targets

Hubs are crucial components in such networksHaiyuan Yu et. al.

Trends in Genetics (2004)

Summary I - Structure

Transcriptional networks are made up of motifs that havespecific information processing task

Transcriptional networks are scale-free which confers robustnessto such systems, with hubs assuming importance

Madan Babu M, Luscombe N et. alCurrent Opinion in Structural Biology (2004)

Evolution of the regulatory network across organisms

Evolution of local network structure (motifs)

Structure of the transcriptional regulatory networkComponents, local & global structure

Outline

Evolution of components in the network (genes and interactions)

Evolution of global network structure (scale-free structure)

Evolution of networks across organisms

How does the regulatory network change during the course of organismal evolution ?

Evolving interactions

Change inenvironment

Evolvinginteractions


Ancestral networkIn a particular environment

Dataset

112 TFs

711 TGs

1295 Interactions

E. coli transcriptional regulatory network

Shen-orr et al (2002) Nature Genetics

Madan Babu & Teichmann (2003) Nucleic acids Research

Salgado et al (2002) Nucleic Acids Research

Step 1

E. coli

Procedure to reconstruct regulatory network

Define TFs and TGs

Step 2

Genome of interest

Identify orthologs in thegenome of interest

Step 3

Reconstruct interactionsif orthologous TFs and TGs

exist in the genome of interest andare known to interact in E. coli

Genome of interest

Similar to Yu H et. al, Genome Research (2004)Verified with COGS, Tatusov, Koonin, Lipman, Science (1998)

12

171

78

38

250

314

41

251

326

Bacillus anthracis A2012 (5544 genes) Streptomyces coelicolor (7769 genes)

49

156

100

Reconstructed transcriptional networks

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

175 completely sequenced prokaryotic genomes20 Archaeal, 156 Bacterial Genomes

175 completely sequenced prokaryotic genomes

20 Archaeal

156 Bacterial Genomes

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

Evolution of networks across organisms

How do regulatory interactions change during the course of organismal evolution ?

Evolving interactions


Evolvinginteractions


Ancestral networkIn a particular environment

Selection can operate at three levels of organization

Network(all transcriptional

interactions in a cell)

Motifs(patterns of

interconnections)

Interactions(transcriptional

interaction)

Transcriptionfactor

Target gene


Evolution of the basic unit



Motifs(patterns of

interconnections)


interaction)

Transcriptionfactor

Target gene


Transcription factors and target genes may co-evolve or evolve independently of each other

Co-evolutionIndependent

evolution

Work on protein interaction network has shownthat interacting proteins tend to co-evolve

0

10

20

30

40

50

60

70

80

90

100

0 20 40 60 80 100

Target genes present (%)

Tra

nscr

iptio

n fa

ctor

s pr

esen

t (%

)

Transcription factors evolve rapidly and independentlyof their target genes

Does not mean they lose transcription factorsInstead they evolves their own set of regulators

Predicted Transcription Factors from the different genomes

Nimwegen, TIGS (2003); Renea et. al, JMB (2004); Aravind et. al, FEMs letters (2005)

0

100

200

300

400

500

600

700

0 1000 2000 3000 4000 5000 6000 7000 8000 9000

Tra

nscr

iptio

n fa

cto

rs

Proteome size

B. pertussis

B. parapertussisB. bronchiseptica

M. magnetotacticum

Pirellula_sp

D. hafniense

N. punctiforme

B. japonicum

Nostoc SpL. interrogans

S. coelicolor

Winged HTH

Classical prokaryotic HTH

C-terminal effector domain

Cro/C1 type HTH

FIS like

96

42

39

25

13

111

39

45

47

6

226

200

142

87

1

Winged HTH



Cro/C1 type HTH

FIS like

Winged HTH



Cro/C1 type HTH

FIS like

Escherichia coli K12 (4311 genes)

Bacillus anthracis A2012 (5544 genes)

Streptomyces coelicolor (7769 genes)

Transcription Factor conservation profile

Can be used to predict presence/absence of specific response regulatory pathways

organism Ainteraction 0001: yesinteraction 0002: yesinteraction 0003: yesinteraction 0004: nointeraction 0005: yesinteraction 0006: no..interaction 1295: yes

organism Binteraction 0001: yesinteraction 0002: nointeraction 0003: yesinteraction 0004: yesinteraction 0005: yesinteraction 0006: no..interaction 1295: yes

.....

organism Zinteraction 0001: nointeraction 0002: nointeraction 0003: nointeraction 0004: yesinteraction 0005: yesinteraction 0006: no..interaction 1295: no

Interaction conservation profile

interaction 1 2 3 4 5 6 . . 1295organism A 1 1 1 0 1 0 . . 1 organism B 1 0 1 1 1 0 . . 1.organism Z 0 0 0 1 1 0 . . 0

A

B

CDE

F

G H

Do organisms with similar lifestyle conserve similar interactions ?

Procedure to construct tree based on similarity of conserved networks

2.010

81

Bor A in conserved nsinteractio

B andA in conserved nsinteractio1

D

Lp

ng

am

ma

Bfl

gam

ma

Hsp

eur

Wb

ega

mm

aW

brga

mm

aR

pa

lph

aR

coa

lph

aS

pnT

firm

Sp

nfir

mi

Tw

hT

Wac

tTw

hT

acti

Ct c

h lap

irC

cach

lap i

Cm

uch

lap i

Cpn

Tch

laC

pnJ

chla

Cpn

Ach

laC

pnC

chla

Hp

Jep

sil

Hp

2eps

il

Bbsp

iroM

pnfir

mi

Bapgam

ma

Mge

f irm

i

U ufir

mi m

Mga firmi

Mpe firmi

Meq nan

M theu r

M taeur

P fueur

M pu firmi

Kpn gam m a

Gm e delta

M de gamm a

Plu gam m a

Ype K gam m

Ype C g am m

Sen gam m a

St gam m a

S ty g am m a

Sty T gam m

Ec O gam m a

Ec O E g am m

Ec C gam m a

Ec K gam m a

Sfl 2 gam m

Sfl g a m m a

Bfu b etaC vi b etaR m e be ta

Avi g a m m aPpu gam m aPsy ga m m aPae gam m aPf l gam m aSon gam m aVch gam m aVpa gam m aVvu C gam m

Vvu Y gam mDha firm iSpa r a lpha

R so l be taBp be taBpp beta

Bbr be taR hsp alpha

Mlo alpha

Bme l alpha

Bs 1a lpha

Sme

a lpha

Atuc

alph

A tuw

a lph

Rpa

alpha

Rru

a lpha

Brja

a lpha

Mm

agalpha

Ccr a lpha

Xca

gamm

a

Xax

gamm

a

Xci gam

ma

Hdu

3gam

m

Hso

gamm

a

Hi g

amm

a

Pm

ug

amm

a

Ne

ube

ta

Nm

Zb

eta

Nm

Mb

eta

Cbu

ga

mm

a

Xfa

gam

ma

Xfa

Tg

amm

Fn

ufu

so

Ca

uch

loro

Se

pA

firmS

aM

Wfirm

Sa

Nfirm

iS

aM

ufirm

Bc

Afirm

iB

an

Am

firB

anA

2fir

Mle

actin

Cdiactin

Sav

actinS

coeactin

Tmaqte

Tfusactin

Lmo

firmi

Linfirm

iTte

firmi

Cte

Efirm

Cpefirm

iBlo

actinM

tuH

acti

Mtu

Cacti

Mbo

actin

Bhafirm

i

Bsfirmib

Cglactin

Cefactin

Anacyan

Sspcyan

Npucyan

Gvicyan

Lgafirmi

Lmefirmi

Spyfirmi

SpyM8fir

SpyM3fir

SpySfirm

Llafirmi

Sag2firm

SagNfirm

Pgibachlo

Ctepbachl

Hheepsilo

Wsuepsilo

Cjepsilon

Tercyan

Thelcyan

AaeaqteChubachloPspchlapiDdedelta

SstcrenDrdr

BthVbach PmarCcya Lintspiro PmarMcya Scspcyan PmarMEcyCthfirm

i Ooefirmi Lplfirm

i EfaVfirm SsocrenApcren

FaceurSmufirm

iCacfirm

i

Oihfirm

iAfeur

Mm

aeur

Mba

eur

Pyae

cren

Mac

eur

Pheu

r

Paeu

r

Tpsp

iro

Mka

eur

Mje

ur

Bap

Sga

mm

Bsp

gam

ma

Tac

eur

Tvo

eur

Distance tree based on interactions present

Tree based on network similarity

Closely related organismswith similar lifestyle clustertogether

Organisms with similarlifestyle but belonging todifferent phylogeneticgroups cluster together

0.00.30.60.7O4

0.30.00.50.6O3LS2

0.60.50.00.2O2

0.70.60.20.0O1LS1

O4O3O2O1

LS2LS1

Each element in the matrixrepresents the normalized distance Between motif profiles for a given

pair of organisms

0.150.6LS2

0.60.1LS1

LS2LS1

0.15

Max (LS2=0.6)

=0.250

0.6

Max (LS2=0.6)

=1.000

LS2

0.6

Max (LS1=0.6)

=1.000

0.1

Max (LS1=0.6)

= 0.166

LS1

LS2LS1

Average distance between organisms having different

lifestyles

Row normalized distance between organisms having

different lifestyles

1.00.70.40.3O4

0.71.00.50.4O3LS2

0.40.51.00.8O2

0.30.40.81.0O1LS1

O4O3O2O1

LS2LS1

Each element in the matrixrepresents the normalized similarity between interaction or motif profiles

for a given pair of organisms

0.850.4LS2

0.40.9LS1

LS2LS1

0.85

Max (LS2=0.85)

=1.000

0.4

Max (LS2=0.85)

=0.470

LS2

0.4

Max (LS1=0.9)

=0.444

0.9

Max (LS1=0.9)

= 1.000

LS1

LS2LS1

Average similarity between organisms having different

lifestyles

Row normalized similarity between organisms having


Lifestyle similarity index

Define a lifestyle class for each of the 176 organism based on 4 attributes

Oxygen requirement Optimal growth temperature Habitat Pathogen or not

e.g: E. coli would belong to the class: Facultative:Mesophilic:Host-associated:No

elements diagonal off ofNumber

elements diagonal Off

elements diagonal ofNumber

elements Diagonal

lifestylesdifferent tobelonging

organismsbetween similarity Average

lifestyle same the tobelonging


LSI

Each cell representsAverage similarity

in interaction content between organisms


LSI = 1.42 p-value < 10-3

Organisms with similar lifestyle conserve similar interactions

Transcription factors tend to evolve rapidly than their target genes. This coupled with the observation that different genomes evolve

their own transcription factors means that they sense and respond to different signals in their environment.

Summary I - Evolution of the basic unit

At the level of regulatory interactions, organisms with similar lifestyle conserve similar regulatory interactions indicating

the influence of environment on gene regulation.

Evolution of network motifs across organisms



Motifs(patterns of

interconnections)


interaction)

Transcriptionfactor

Target gene


Interactions in motifs may be conserved as a unit or may evolve like any other interaction in the network

Complete conservationor absence

Partial conservation

Work on protein interaction network has shownthat motifs tend to be completely conserved

F1 Fn S1 Sn M1 MnE. coli ... ... ...

F1 Fn S1 Sn M1 Mnorganism A ... ... ...

F1 Fn S1 Sn M1 Mnorganism B ... ... ...

....Z

.

.

.

.

....

(1/3).(3/3)B

(1/3).(3/3)A

FnF2F1Org

....Z

.

.

.

.

....

(3/3).(2/3)B

(3/3).(2/3)A

SnS2S1Org

....Z

.

.

.

.

....

(4/4).(1/4)B

(4/4).(2/4)A

MnM2M1Org

motif conservationprofile

feed-forward motifs single input modules multi input modules

clustering of motifs (e.g. K-means) clustering of motifs (e.g. K-means)

Generation of motif conservation profiles

Motifs are only partially conserved in many genomes

0% 100%Motifs

Gen

om

es

E. coli

Partially conserved motifs

Are interactions in motifs more conserved thanother interactions in the network?

Simulation of network evolution

Negative selection for interactions

in motifs

Interactions in motifsare selected against

Positive selection for interactions

in motifs

Interactions in motifsare selectively conserved

Neutral selection for interactions

in motifs

Interactions in motifsare neutrally conserved

Interactions in motifs evolve like any other interaction in the network

-1

-0.5

0

0.5

1

30 40 50 60 70 80 90 100

% genes conserved

Cm

Selection for motifs

Observed trend in genomes

Neutral conservation of motifs

Selection against motifs

Evolutionarily closely related organisms that havedissimilar lifestyle do not conserve network motifs

Salmonella typhi( proteobacteria)

Fnr

NarL NuoN

Vibrio cholerae( proteobacteria)

Haemophilus somnus( proteobacteria)

Xylella fastidiosa( proteobacteria)

Blochmannia floridanus( proteobacteria)

Evolutionarily distantly related organisms that havesimilar lifestyle conserve network motifs

R. palustris ( proteobacteria)B. pertussis ( proteobacteria)N. punctiforme (Cyanobacteria)S. avermitilis (Actinobacteria)D. hafniense (Firmicute)

Fnr

NarL

Orthologous genes can be embedded in different motifsaccording to requirements dictated by lifestyle

Interactions in motifs evolve like any other interaction in the network

Feed forward motif Single input motif

Responds to persistent signal Quick response

E. colistable environment – requires persistent signal

H. influenzaeunstable environment – requires quick response

0.00.30.60.7O4

0.30.00.50.6O3LS2

0.60.50.00.2O2

0.70.60.20.0O1LS1

O4O3O2O1

LS2LS1

Each element in the matrixrepresents the normalized distance Between motif profiles for a given

pair of organisms

0.150.6LS2

0.60.1LS1

LS2LS1

0.15

Max (LS2=0.6)

=0.250

0.6

Max (LS2=0.6)

=1.000

LS2

0.6

Max (LS1=0.6)

=1.000

0.1

Max (LS1=0.6)

= 0.166

LS1

LS2LS1

Average distance between organisms having different

lifestyles

Row normalized distance between organisms having


1.00.70.40.3O4

0.71.00.50.4O3LS2

0.40.51.00.8O2

0.30.40.81.0O1LS1

O4O3O2O1

LS2LS1

Each element in the matrixrepresents the normalized similarity between interaction or motif profiles

for a given pair of organisms

0.850.4LS2

0.40.9LS1

LS2LS1

0.85

Max (LS2=0.85)

=1.000

0.4

Max (LS2=0.85)

=0.470

LS2

0.4

Max (LS1=0.9)

=0.444

0.9

Max (LS1=0.9)

= 1.000

LS1

LS2LS1

Average similarity between organisms having different

lifestyles

Row normalized similarity between organisms having



Define a lifestyle class for each of the 176 organism based on 4 attributes

Oxygen requirement Optimal growth temperature Habitat Pathogen or not

e.g: E. coli would belong to the class: Facultative:Mesophilic:Host-associated:No

elements diagonal off ofNumber

elements diagonal Off

elements diagonal ofNumber

elements Diagonal

lifestylesdifferent tobelonging


lifestyle same the tobelonging


LSI

Each cell representsAverage similarity in motif content

between organisms

Organisms with similar lifestyle conserve network motifsand hence may regulate target genes in a similar manner


LSI = 1.34 p-value < 3x10-3

Organisms with similar lifestyle conserve network motifs and hence may regulate target genes in a similar manner

Even though motifs are not conserved as wholeunits, organisms with similar lifestyle tend

to conserve similar motifs

Summary II - Evolution of network motifs

Evolution of global structure



Motifs(patterns of

interconnections)


interaction)

Transcriptionfactor

Target gene


Regulatory hubs may be conserved or lost and replaced

Conservation of hubs Replacement of hubs

Work on protein interaction network has shownthat hubs tend to be conserved

Are hubs more conserved than other nodes in the network?

Simulation of network evolution

Negative selection for hubs

Hubs in networks areare selected against

Positive selection for hubs

Hubs in networksare selectively conserved

Neutral selection for nodes

Nodes in networksare neutrally conserved

Regulatory hubs are lost as rapidly asother transcription factors in the network

Crp

NarL

Crp

NarL

E. coli H. influenzae B. pertussis

NarL

Crp

Regulatory hubs which are condition specific can beeither lost or replaced

The same protein in organisms living in different lifestyles may conferdifferent adaptive value. Hence it may emerge as a regulatory

hub in the organism to which it confers high adaptive value and not in the others

Different proteins should emerge as hubs in organismswith different lifestyle

CcpA (85)

ComK (48)

AbrB (41)

Fur (37)

PhoP (33)

CodY (30)

Known transcriptional regulatory network of B. subtilis

Crp (188)

Fnr (109)

Ihf (95)

ArcA (69)

NarL (65)

Lrp (52)

Known transcriptional regulatory network of E. coli

Different proteins emerge as regulatory hubs

Scale-free structure emerged independently in evolutionHubs evolve according to requirements dictated by life style

Even though hubs can be lost or replaced, organismswith different lifestyle evolve a scale-free structure where

different proteins emerge as hubs as dictated by their lifestyle

Summary III - Evolution of global structure

Implications

First overview of transcriptional regulatory systems, including predictionof transcription factors, in experimentally intractable organisms and pathogens

Identification of key regulatory hubs can possibly serve asgood drug targets

Good starting point to study how changesin cis-regulatory elements affect gene expression experimentally and in

engineering regulatory interactions

Methods developed are generically applicable

Conclusion

Transcription factors evolve independently of their target genesOrganisms with similar lifestyle conserve similar interactions

Interactions in motifs are not conserved as whole unitsOrganisms with similar lifestyle conserve similar motifs

Hubs are not completely conserved and can be lost or replacedDifferent proteins emerge as hubs in organisms as dictated by lifestyle

Transcriptional networks in prokaryotes are flexible and adapt to their environment by tinkering individual interactions

Sarah TeichmannMRC-LMB, Cambridge, U.K

Acknowledgements

MRC - Laboratory of Molecular BiologyNational Institutes of Health

L AravindNCBI, NIH, Bethesda, USA

Evolutionary dynamics of prokaryotic transcriptional regulatory networksMadan Babu M, Teichmann, SA & Aravind L, submitted

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

Madan Babu M & Teichmann SANucleic Acids Research (2003)

Trends in Genetics (2003)

Evolution of transcription factors

duplication of TF

duplication of TG

duplication of TG + TF

Teichmann SA & Madan Babu MNature Genetics (2004)

Growth oftranscriptional

regulatory networks

Luscombe N, Madan Babu M et. alNature (2004)

Condition specific usageof transcriptional

regulatory networks

Past work from our lab

0

0.2

0.4

0.6

0.8

1

1.2

-1

-0.8

-0.6

-0.4

-0.2 0

0.2

0.4

0.6

0.8 1

Pearson Corre lation Coefficient

Rel

ativ

e d

istr

ibu

tio

n

Co-regulated pairs of TGs

TF – TG pairs

Random pair of genes

E. colia

0

0.2

0.4

0.6

0.8

1

1.2

-1

-0.8

-0.6

-0.4

-0.2 0

0.2

0.4

0.6

0.8 1

Pearson Corre lation Coefficient

Rel

ativ

e d

istr

ibu

tio

n

Co-regulated pairs of TGs

TF – TG pairs

Random pair of genes

V. choleraeb

Experimental network753 interactions, 569 proteins

Reconstructed network414 interactions, 322 proteins

4617 87

Interactions in the reconstructednetwork formed by the

123 proteins: 133

Interactions in the experimentally determined network formed by the

123 proteins: 63

Overlap123 proteins

Interactions seen in bothnetworks: 46

Interactions seen ONLY in experimental network: 46

Interactions seen ONLY in reconstructed network: 87

fnr

adaptive evolution in prokaryotic transcriptional regulatory networks

Documents

regulatory network change

network genes

scalefree networks

scalefree structure

scalefreescale free

regulatory interactions

regulatory networkdefine

linkstranscriptional