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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