Structure, evolution and dynamics of transcriptional regulatory networks M. Madan Babu, PhD National...

Structure, evolution and dynamics of transcriptional regulatory networks

M. Madan Babu, PhD

National 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 transcriptional regulatory network

Dynamic nature of the transcriptional regulatory network

Evolution of local network structure

Evolution of global network structure

Dynamics of local network structure

Dynamics of global network structure

Structure of the transcriptional regulatory network

Local network structure: network motifs

Global network structure: scale-free structure

Outline

Evolution of the transcriptional regulatory network

Dynamic nature of the transcriptional regulatory network

Evolution of local network structure

Evolution of global network structure

Dynamics of local network structure

Dynamics of global network structure

Structure of the transcriptional regulatory network

Local network structure: network motifs

Global network structure: scale-free structure

Outline

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 transcriptional regulatory network

Dynamic nature of the transcriptional regulatory network

Evolution of local network structure

Evolution of global network structure

Dynamics of local network structure

Dynamics of global network structure

Structure of the transcriptional regulatory network

Local network structure: network motifs

Global network structure: scale-free structure

Outline

Evolution of the transcriptional regulatory network in yeast

How did the regulatory network evolve?

What are the underlying molecular mechanisms?

Organismal evolution

Growth of the regulatory networkE. coli

Yeast

?

?

?

?

What are the mechanisms for creating new genes?

Network Growth

??

?Transcription factor

Creation of a new Creation of a new

target gene

Mechanisms for the creation of new genes

OR Recombination/Innovation

Duplication & Divergence

Duplication

Divergence

A

A’ A’’

Dataset used in the analysis

Transcriptional regulatory network in Yeast

477 proteins (109 TFs + 368 TGs)901 interactions

Guelzim et.al. Nat. Gen. (2002)

Duplication of the basic unit

Basic unit

Duplication of

target gene transcription factor

Duplication of

Duplication of transcription factor and target gene

Divergence Scenario

Duplicat

ion

of TF

Duplication

of TG

Duplication of TF+TG

InheritanceLoss & gain Inheritance Loss & gain

Loss & inheritance

GainGain

Inheritance, loss and gain of interaction

Divergence Scenario I - Inheritance

Duplicat

ion

of TF

Duplication

of TG

Duplication of TF+TG

InheritanceLoss & gain Inheritance Loss & gain

Loss & inheritance

GainGain

Inheritance of interaction

Consequence of duplicating a target gene

Basic unit

Duplication of

target gene

same TF regulates homologous TGs(inheritance of interaction)

Divergence of TG

20% of all interactions in Yeast (166 interactions)

Eno1 Eno2

Gcr1

Consequence of duplicating a transcription factor

Basic unit

Duplication of

transcription factor

Same gene regulated by homologous TFs(inheritance of interaction)

Divergence of TF

Pdr1 Pdr3

Flr1

22% of all interactions in Yeast (188 interactions)

Consequences of duplicating a TF and TG

Basic unit

Duplication of

transcription factorand target gene

Homologous TFs regulate homologous TGs(loss/inheritance of interaction)

Divergence of TF and TG

Mal11 Mal31

Mal13 Mal33

4% of all interactions in Yeast (31 interactions)

Contribution of duplication with inheritance to network growth

188duplication of TF (21%)

166duplication of TG (20%)

31duplication

of TG + TF (4%)

385 interactions in Yeast = 45%

~1/2 of the regulatory network has evolved by duplication followedby inheritance of interaction after the duplication event

Divergence Scenario II – Gain of new interaction

Duplicat

ion

of TF

Duplication

of TG

Duplication of TF+TG

InheritanceLoss & gain Inheritance Loss & gain

Loss & inheritance

GainGain

Gain of interaction

Duplication followed by innovation of new interaction

Basic unit

Basic unit

Duplication of

transcription factor

Duplication of

target geneDivergence of TG + BS

Different transcription factors regulateHomologous genes

(loss/gain of interaction)

Divergence of TF to recognisea different BS

Homologous TFs regulating different genes

(loss/gain of interaction)

365

duplication & gain (43%)

Duplication with gain of new interactions

“~ 1/2 of the regulatory network has evolved by duplication and gain of new interactions after the duplication event”

Network growth by Recombination

Recombination

Innovation Innovation

“~1/10 of the regulatory network has evolved by innovation”

101

innovation (12%)

Innovation of new interactions

385duplication &

inheritance (45%)

365duplication &

gain (43%)

101innovation (12%)

Evolution of the transcriptional regulatory network

~90% of the network has evolved by duplication followed byInheritance, loss and gain of interaction

How can such events affect local and global network structure?

Questions

- are the motifs and scale free structure products of duplication events ?

- are the motifs and scale free structure selected for in evolution ?

Duplication of TG Duplication of TF Duplication of TF & TG

Motifs

Growthmodels

Single Input Module Feed-Forward Motif Multiple input motif

Duplication models and evolution of network motifs

Very rarely we find instances where duplication events have resulted in the formation of network motifs

Evolution of local network structure

Network motifs have evolved independently (convergent evolution) multiple times

because they confer specific properties to the network

Duplication and evolution of scale free structure

Growth by gene duplication and inheritance of interaction can explain evolution of scale-free structure

Regulatory hubs should control more duplicate genes

(4)(2)

Total = 6

“Rich gets richer”

(5)(2)

Duplication& inheritance

Total = 7

(6)(2)

Duplication& inheritance

Total = 8

Regulatory hubs do not regulate duplicate genes more often than any other normal transcription factor

Scale free structure has been selected for in evolutionand is not a product of duplication events

Evolution of global network structure

Summary II - Evolution

385duplication &

inheritance (45%)

365duplication &

gain (43%)

101innovation (12%)

Gene duplication followed by inheritance of interaction andgain of new interactions have

contributed to 90% of the network

Teichmann SA & Madan Babu MNature Genetics (2004)

Network motifs and the scale free structure are not products of

duplication events, but have been selected for in evolution

Evolution of the transcriptional regulatory network

Dynamic nature of the transcriptional regulatory network

Evolution of local network structure

Evolution of global network structure

Dynamics of local network structure

Dynamics of global network structure

Structure of the transcriptional regulatory network

Local network structure: network motifs

Global network structure: scale-free structure

Outline

Are there differences in the sub-networks under different conditions?

Cell cycle

Sporulation

Stress

Static network

Across all cellular conditions

Dynamic nature of the regulatory network in yeast

How are the networks used under different conditions?

Dataset - gene regulatory network in Yeast

3,962 genes (142 TFs +3,820 TGs)7,074 Regulatory interactions

Individual experimentsTRANSFAC DB + Kepes dataset

288 genes + 477 genes356 interactions + 906 interactions

ChIp-chip experimentsSnyder lab + Young lab1560 genes + 2416 gene

2124 interactions + 4358 interaction

Integrating gene regulatory network with expression data

142 TFs3,820 TGs7,074 Interactions

Transcription Factors

1 condition

2 conditions

3 conditions

4 conditions

5 conditions

142 TFs1,808 TGs4,066 Interactions

Target Genes

Gene expression data

for 5 cellular conditions

Cell-cycleSporulation

DNA damageDiauxic shift

Stress

Back-tracking method to find active sub-networks

Gene regulatory network

Identify differentially regulated genes

Find TFs that regulate the genes Find TFs that regulate these TFs

Active sub-network

DNA damage

Cell cycleSporulation

Diauxic shift

Stress

Active sub-networks: How different are they ?

Multi-stageprocesses

BinaryProcesses

Network Motifs

Milo et.al (2002), Lee et.al (2002)

Single Input Motif (SIM) – 23%

Feed-forward Motif (FF) – 27%

Multi-Input Motifs (MIM) – 50%

Sub-networks : Network motifs

Network motifs are used preferentially in the different cellular conditions

- Do different proteins become hubs under different conditions?

- Is it the same protein that acts as a regulatory hub?

Cell cycle Sporulation Diauxic shift DNA damage Stress

Condition specific networks are scale-free

Regulatory hubs change with conditions

Cluster TFs according tothe number of target genes

active in each condition

Different TFs become key regulators in

different conditions

CC SP DS DD SR TF

250 45 20 30 15 Swi6

Hubs regulate other hubs to initiate cellular events

Suggests a structure which transfers weight between hubsto trigger cellular events

Network Parameters

Connectivity

Path length

Clustering coefficient

Network Parameters - Connectivity

Outgoing connections = 49.8

on average, each TF regulates ~50 genes

Changes

Incoming connections = 2.1

on average, each gene is regulated by ~2 TFs

Remains constant

Network parameters : Connectivity

Binary:Quick, large-scale turnover of genes

Multi-stage:Controlled, ticking

over of genes at different stages

• “Binary conditions” greater connectivity

• “Multi-stage conditions” lower connectivity

Number of intermediate TFs until final target

Path length

1 intermediate TF

= 1

Indication of how immediatea regulatory response is

Average path length = 4.7

Network Parameters – Path length

Starting TF

Final target

• “Binary conditions” shorter path-length “faster”, direct action

• “Multi-stage” conditions longer path-length “slower”, indirect action intermediate TFs regulate different stagesBinaryMulti-stage

Network parameters : Path length

Clustering coefficient

= existing links/possible links= 1/6 = 0.17

Measure of inter-connectedness of the network

Average coefficient = 0.11

6 possible links

1 existing link

4 neighbours

Network Parameters – Clustering coefficient

Ratio of existing links to maximum number of links for neighboring nodes

• “Binary conditions” smaller coefficientsless TF-TF inter-regulation

• “Multi-stage conditions” larger coefficients more TF-TF inter-regulation

BinaryMulti-stage

Network parameters : Clustering coeff

Sub-networks have evolved both their local structure and global structure to respond to cellular conditions efficiently

multi-stage conditions

• fewer target genes• longer path lengths• more inter-regulation between TFs

binary conditions

• more target genes• shorter path lengths• less inter-regulation between TFs

Summary III - Dynamics

Sub-networks have evolved both their local structure and global structure to respond to cellular conditions efficiently

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

Network motifs are preferentially used under the different cellularconditions and different proteins act as regulatory hubs in different

cellular conditions

Conclusions

“This has resulted in a network structure that can efficiently re-wire interactions to meet the biological demand placed by the process”

“Transcriptional networks are made up of network motifs at the local level and have a scale-free structure at the global level”

“Even though close to 90% of the regulatory network in yeast has evolved by duplication, network motifs and scale free

structure are not products of duplication events – instead they have been selected for in evolution”

Implications

First overview of the evolution and dynamics of thetranscriptional regulatory network of a eukaryote

Identification of key regulatory hubs under different conditions can serve asgood drug targets

Provides insights into engineering regulatory interactions

Methods developed to reconstruct and compare active networksare generically applicable

Acknowledgements

Sarah TeichmannMRC-LMB, Cambridge, U.K

MRC - Laboratory of Molecular BiologyCambridge Commonwealth TrustOverseas Research Studentship

Trinity College, Cambridge

Nicholas LuscombeHaiyuan Yu

Michael SnyderMark Gerstein

Yale University, NH, U.S.A

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

L AravindNCBI, NLM

National Institutes of Health, USA