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Adaptive evolution in prokaryotic transcriptional regulatory networks. M. Madan Babu, PhD. NCBI, NLM National Institutes of Health. Network. Metabolic. Protein Interaction. Transcriptional. Proteins. Nodes Links. Metabolites. Transcription factor Target genes. Enzymatic

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Adaptive evolution in prokaryotic transcriptional regulatory networks

Adaptive evolution in prokaryotic transcriptional

regulatory networks

M. Madan Babu, PhD

NCBI, NLM

National Institutes of Health


Adaptive evolution in prokaryotic transcriptional regulatory networks

Network

Metabolic

Protein Interaction

Transcriptional

Proteins

Nodes

Links

Metabolites

Transcription factor

Target genes

Enzymatic

conversion

Physical Interaction

Transcriptional

Interaction

Protein-Protein

Protein-Metabolite

Interaction

Protein-DNA

A

A

A

A

B

B

B

B

Networks in Biology


Adaptive evolution in prokaryotic transcriptional regulatory networks

Outline

Structure of the transcriptional regulatory network

Components, local & global structure

Evolution of the regulatory network across organisms

Evolution of components in the network (genes and interactions)

Evolution of local network structure (motifs)

Evolution of global network structure (scale-free structure)


Adaptive evolution in prokaryotic transcriptional regulatory networks

Outline

Structure of the transcriptional regulatory network

Components, local & global structure

Evolution of the regulatory network across organisms

Evolution of components in the network (genes and interactions)

Evolution of local network structure (motifs)

Evolution of global network structure (scale-free structure)


Adaptive evolution in prokaryotic transcriptional regulatory networks

Transcription

factor

Target gene

Motifs

(Local level)

patterns of

Interconnections

Uri Alon & Rick Young

Basic unit

(Components)

transcriptional

interaction

Scale free network

(Global level)

all transcriptional

interactions in a cell

Albert & Barabasi

Structure of the transcriptional regulatory network

Madan Babu M, Luscombe N, Aravind L, Gerstein M & Teichmann SA

Current Opinion in Structural Biology (2004)


Adaptive evolution in prokaryotic transcriptional regulatory networks

Local level: Transcriptional networks are made up of motifs

which perform information processing task

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

Properties of transcriptional networks


Adaptive evolution in prokaryotic transcriptional regulatory networks

Single input

Motif

Multiple input

Motif

Feed Forward

Motif

ArgR

TrpR

TyrR

Crp

AroL

AroM

AraBAD

ArgD

AraC

ArgE

ArgF

- Co-ordinates expression

- Enforces order in expression

- Quicker response

- Integrates different signals

- Quicker response

Function

- Responds to persistent signal

- Filters noise

Transcriptional networks are made up of motifs

Network Motif

“Patterns of

interconnections

that recur at different

parts and

with specific

information

processing task”

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


Adaptive evolution in prokaryotic transcriptional regulatory networks

1

N (k) a

g

k

Transcriptional networks are scale-free

Scale-free structure

Presence of few

nodes with many links and many nodes with few links

Scale free structure provides robustness to the system

Albert & Barabasi, Rev Mod Phys (2002)


Adaptive evolution in prokaryotic transcriptional regulatory networks

Tolerant to random removal of nodes (mutations)

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

Hubs are crucial components in such networks

Scale-free networks exhibit robustness

Robustness – The ability of complex systems to maintain their function even

when the structure of the system changes significantly

Haiyuan Yu et. al.

Trends in Genetics (2004)


Adaptive evolution in prokaryotic transcriptional regulatory networks

Summary I - Structure

Transcriptional networks are made up of motifs that have

specific information processing task

Transcriptional networks are scale-free which confers robustness

to such systems, with hubs assuming importance

Madan Babu M, Luscombe N et. al

Current Opinion in Structural Biology (2004)


Adaptive evolution in prokaryotic transcriptional regulatory networks

Outline

Structure of the transcriptional regulatory network

Components, local & global structure

Evolution of the regulatory network across organisms

Evolution of components in the network (genes and interactions)

Evolution of local network structure (motifs)

Evolution of global network structure (scale-free structure)


Adaptive evolution in prokaryotic transcriptional regulatory networks

Evolving

interactions

Evolving

interactions

Change in

environment

Change in

environment

Evolution of networks across organisms

Ancestral network

In a particular environment

How does the regulatory network change during the

course of organismal evolution ?


Adaptive evolution in prokaryotic transcriptional regulatory networks

Dataset

E. coli transcriptional regulatory network

112 TFs

711 TGs

1295 Interactions

Salgado et al (2002) Nucleic Acids Research

Shen-orr et al (2002) Nature Genetics

Madan Babu & Teichmann (2003) Nucleic acids Research


Adaptive evolution in prokaryotic transcriptional regulatory networks

Genome of interest

Genome of interest

Step 2

Step 3

Reconstruct interactions

if orthologous TFs and TGs

exist in the genome of interest and

are known to interact in E. coli

Identify orthologs in the

genome of interest

Procedure to reconstruct regulatory network

E. coli

Step 1

Define TFs and TGs

Similar to Yu H et. al, Genome Research (2004)

Verified with COGS, Tatusov, Koonin, Lipman,Science (1998)


Adaptive evolution in prokaryotic transcriptional regulatory networks

Bacillus anthracis A2012 (5544 genes)

Streptomyces coelicolor (7769 genes)

38

41

250

251

314

326

12

49

171

156

78

100

Reconstructed transcriptional networks

175 completely sequenced prokaryotic genomes

20 Archaeal, 156 Bacterial Genomes

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


Adaptive evolution in prokaryotic transcriptional regulatory networks

175 completely sequenced prokaryotic genomes

20 Archaeal

156 Bacterial Genomes

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


Adaptive evolution in prokaryotic transcriptional regulatory networks

Evolving

interactions

Evolving

interactions

Change in

environment

Change in

environment

Evolution of networks across organisms

Ancestral network

In a particular environment

How do regulatory interactions change during the

course of organismal evolution ?


Adaptive evolution in prokaryotic transcriptional regulatory networks

Network

(all transcriptional

interactions in a cell)

Selection can operate at three levels of organization

Transcription

factor

Target gene

Motifs

(patterns of

interconnections)

Interactions

(transcriptional

interaction)

Madan Babu M, Luscombe N et. al

Current Opinion in Structural Biology (2004)


Adaptive evolution in prokaryotic transcriptional regulatory networks

Network

(all transcriptional

interactions in a cell)

Evolution of the basic unit

Transcription

factor

Target gene

Motifs

(patterns of

interconnections)

Interactions

(transcriptional

interaction)

Madan Babu M, Luscombe N et. al

Current Opinion in Structural Biology (2004)


Adaptive evolution in prokaryotic transcriptional regulatory networks

Independent

evolution

Co-evolution

Transcription factors and target genes may

co-evolve or evolve independently of each other

Work on protein interaction network has shown

that interacting proteins tend to co-evolve


Adaptive evolution in prokaryotic transcriptional regulatory networks

Transcription factors present(%)

Target genes present (%)

Transcription factors evolve rapidly and independently

of their target genes

Does not mean they lose transcription factors

Instead they evolves their own set of regulators


Adaptive evolution in prokaryotic transcriptional regulatory networks

S. coelicolor

B. japonicum

B. pertussis

B. bronchiseptica

B. parapertussis

Transcription factors

D. hafniense

M. magnetotacticum

N. punctiforme

Nostoc Sp

Pirellula_sp

Escherichia coli K12

(4311 genes)

Bacillus anthracis A2012

(5544 genes)

Streptomyces coelicolor

(7769 genes)

L. interrogans

Winged HTH

111

Winged HTH

226

Winged HTH

96

39

200

Classical prokaryotic HTH

Classical prokaryotic HTH

Classical prokaryotic HTH

42

C-terminal effector domain

C-terminal effector domain

45

C-terminal effector domain

142

39

Proteome size

Cro/C1 type HTH

Cro/C1 type HTH

Cro/C1 type HTH

47

87

25

FIS like

FIS like

FIS like

1

6

13

Predicted Transcription Factors from the different genomes

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


Adaptive evolution in prokaryotic transcriptional regulatory networks

Transcription Factor conservation profile

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


Adaptive evolution in prokaryotic transcriptional regulatory networks

Interaction conservation

profile

organism A

interaction 0001: yes

interaction 0002: yes

interaction 0003: yes

interaction 0004: no

interaction 0005: yes

interaction 0006: no

.

.

interaction 1295: yes

organism B

interaction 0001: yes

interaction 0002: no

interaction 0003: yes

interaction 0004: yes

interaction 0005: yes

interaction 0006: no

.

.

interaction 1295: yes

organism Z

interaction 0001: no

interaction 0002: no

interaction 0003: no

interaction 0004: yes

interaction 0005: yes

interaction 0006: no

.

.

interaction 1295: no

.....

H

G

A

interaction 1 2 3 4 5 6 . . 1295

organism 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

B

F

C

E

D

Do organisms with similar lifestyle conserve

similar interactions ?

Procedure to construct tree based on similarity of conserved networks


Adaptive evolution in prokaryotic transcriptional regulatory networks

Distance tree based on interactions present

Tree based on network similarity

Closely related organisms

with similar lifestyle cluster

together

Organisms with similar

lifestyle but belonging to

different phylogenetic

groups cluster together


Adaptive evolution in prokaryotic transcriptional regulatory networks

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

Each cell represents

Average similarity

in interaction content

between organisms


Adaptive evolution in prokaryotic transcriptional regulatory networks

Lifestyle similarity index

LSI = 1.42

p-value < 10-3

Organisms with similar lifestyle conserve similar interactions


Adaptive evolution in prokaryotic transcriptional regulatory networks

Summary I - Evolution of the basic unit

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.

At the level of regulatory interactions, organisms with similar

lifestyle conserve similar regulatory interactions indicating

the influence of environment on gene regulation.


Adaptive evolution in prokaryotic transcriptional regulatory networks

Network

(all transcriptional

interactions in a cell)

Evolution of network motifs across organisms

Transcription

factor

Target gene

Motifs

(patterns of

interconnections)

Interactions

(transcriptional

interaction)

Madan Babu M, Luscombe N et. al

Current Opinion in Structural Biology (2004)


Adaptive evolution in prokaryotic transcriptional regulatory networks

Complete conservation

or absence

Partial conservation

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

Work on protein interaction network has shown

that motifs tend to be completely conserved


Adaptive evolution in prokaryotic transcriptional regulatory networks

Generation of motif conservation profiles


Adaptive evolution in prokaryotic transcriptional regulatory networks

Motifs

E. coli

0%

100%

Genomes

Partially conserved motifs

Motifs are only partially conserved in many genomes


Adaptive evolution in prokaryotic transcriptional regulatory networks

Are interactions in motifs more conserved than

other interactions in the network?

Simulation of network evolution

Positive selection

for interactions

in motifs

Interactions in motifs

are selectively conserved

Neutral selection

for interactions

in motifs

Interactions in motifs

are neutrally conserved

Negative selection

for interactions

in motifs

Interactions in motifs

are selected against


Adaptive evolution in prokaryotic transcriptional regulatory networks

Selection for motifs

Observed trend in genomes

Neutral conservation of motifs

Selection against motifs

Interactions in motifs evolve like any other interaction in the network


Adaptive evolution in prokaryotic transcriptional regulatory networks

Evolutionarily closely related organisms that have

dissimilar lifestyle do not conserve network motifs

Fnr

NuoN

NarL

Xylella fastidiosa

(g proteobacteria)

Haemophilus somnus

(g proteobacteria)

Vibrio cholerae

(g proteobacteria)

Blochmannia floridanus

(g proteobacteria)

Salmonella typhi

(g proteobacteria)

Evolutionarily distantly related organisms that have

similar lifestyle conserve network motifs

Fnr

NarL

R. palustris (a proteobacteria)

B. pertussis (b proteobacteria)

N. punctiforme (Cyanobacteria)

S. avermitilis (Actinobacteria)

D. hafniense (Firmicute)


Adaptive evolution in prokaryotic transcriptional regulatory networks

Interactions in motifs evolve like any other interaction in the network

Single input motif

Feed forward motif

E. coli

stable environment – requires persistent signal

H. influenzae

unstable environment – requires quick response

Responds to persistent signal

Quick response

Orthologous genes can be embedded in different motifs

according to requirements dictated by lifestyle


Adaptive evolution in prokaryotic transcriptional regulatory networks

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

Each cell represents

Average similarity

in motif content

between organisms


Adaptive evolution in prokaryotic transcriptional regulatory networks

Lifestyle similarity index

Organisms with similar lifestyle conserve network motifs

and 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


Adaptive evolution in prokaryotic transcriptional regulatory networks

Summary II - Evolution of network motifs

Even though motifs are not conserved as whole

units, organisms with similar lifestyle tend

to conserve similar motifs


Adaptive evolution in prokaryotic transcriptional regulatory networks

Network

(all transcriptional

interactions in a cell)

Evolution of global structure

Transcription

factor

Target gene

Motifs

(patterns of

interconnections)

Interactions

(transcriptional

interaction)

Madan Babu M, Luscombe N et. al

Current Opinion in Structural Biology (2004)


Adaptive evolution in prokaryotic transcriptional regulatory networks

Conservation of hubs

Replacement of hubs

Regulatory hubs may be conserved or lost and replaced

Work on protein interaction network has shown

that hubs tend to be conserved


Adaptive evolution in prokaryotic transcriptional regulatory networks

Are hubs more conserved than other nodes

in the network?

Simulation of network evolution

Positive selection

for hubs

Hubs in networks

are selectively conserved

Neutral selection

for nodes

Nodes in networks

are neutrally conserved

Negative selection

for hubs

Hubs in networks are

are selected against


Adaptive evolution in prokaryotic transcriptional regulatory networks

Regulatory hubs are lost as rapidly as

other transcription factors in the network


Adaptive evolution in prokaryotic transcriptional regulatory networks

Regulatory hubs which are condition specific can be

either lost or replaced

Crp

Crp

Crp

NarL

NarL

NarL

B. pertussis

H. influenzae

E. coli

The same protein in organisms living in different lifestyles may confer

different 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 organisms

with different lifestyle


Adaptive evolution in prokaryotic transcriptional regulatory networks

Different proteins emerge as regulatory hubs

Known transcriptional regulatory network of B. subtilis

Known transcriptional regulatory network of E. coli

CcpA (85)

Crp (188)

ComK (48)

Fnr (109)

AbrB (41)

Ihf (95)

Fur (37)

ArcA (69)

PhoP (33)

NarL (65)

CodY (30)

Lrp (52)

Scale-free structure emerged independently in evolution

Hubs evolve according to requirements dictated by life style


Adaptive evolution in prokaryotic transcriptional regulatory networks

Summary III - Evolution of global structure

Even though hubs can be lost or replaced, organisms

with different lifestyle evolve a scale-free structure where

different proteins emerge as hubs as dictated by their lifestyle


Adaptive evolution in prokaryotic transcriptional regulatory networks

Implications

First overview of transcriptional regulatory systems, including prediction

of transcription factors, in experimentally intractable organisms and pathogens

Good starting point to study how changes

in cis-regulatory elements affect gene expression experimentally and in

engineering regulatory interactions

Methods developed are generically applicable

Identification of key regulatory hubs can possibly serve as

good drug targets


Adaptive evolution in prokaryotic transcriptional regulatory networks

Conclusion

Transcription factors evolve independently of their target genes

Organisms with similar lifestyle conserve similar interactions

Interactions in motifs are not conserved as whole units

Organisms with similar lifestyle conserve similar motifs

Hubs are not completely conserved and can be lost or replaced

Different 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


Adaptive evolution in prokaryotic transcriptional regulatory networks

Acknowledgements

Sarah Teichmann

MRC-LMB, Cambridge, U.K

L Aravind

NCBI, NIH, Bethesda, USA

MRC - Laboratory of Molecular Biology

National Institutes of Health

Evolutionary dynamics of prokaryotic transcriptional regulatory networks

Madan Babu M, Teichmann, SA & Aravind L, submitted

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


Adaptive evolution in prokaryotic transcriptional regulatory networks

Growth of

transcriptional

regulatory networks

Condition specific usage

of transcriptional

regulatory networks

Evolution of

transcription factors

duplication of TG

duplication of TF

duplication of TG + TF

Teichmann SA & Madan Babu M

Nature Genetics (2004)

Luscombe N, Madan Babu M et. al

Nature (2004)

Madan Babu M & Teichmann SA

Nucleic Acids Research (2003)

Trends in Genetics (2003)

Past work from our lab


Adaptive evolution in prokaryotic transcriptional regulatory networks

a

b

V. cholerae

E. coli

Co-regulated pairs of TGs

Co-regulated pairs of TGs

TF – TG pairs

TF – TG pairs

Random pair of genes

Random pair of genes


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