Dissecting the dynamics of transcriptional regulatory networks

MRC Laboratory of Molecular BiologyCambridge, UK

MRC Laboratory of Molecular BiologyCambridge, UK

M. Madan BabuGroup leader

M. Madan BabuGroup leader

Overview of research

Discovery of novel DNA binding proteins

Data integration and function prediction

Nuc. Acids. Res (2005) J Bacteriol (2008)

C

C

H

H

Discovery of transcription factors in pathogens

Evolution of a global regulatory hubs

Nuc. Acids. Res (2006)

Evolution of biological systems

Evolution of transcriptional networks Evolution of networks within and across genomes

Nature Genetics (2004) J Mol Biol (2006a)

Uncovering a distributed architecture in networks

PNAS (2008)

Structure and dynamics of biological networks

Structure, function and regulation of biological systems

Principles of network dynamics

Science (in press) Nature (2004)

Methods to investigate network dynamics

Submitted

Global dynamics (network dynamics) of regulatory networks

Local dynamics (node dynamics) of regulatory networks

Structure of the transcriptional regulatory network

Outline

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

Organization of the transcriptional regulatory networkanalogy to the organization of the protein structures

Basic unitTranscriptional interaction

Transcriptionfactor

Target gene

Basic unitPeptide link between amino acids

Local structure - MotifsPatterns of interconnections

Local structureSecondary structure

Global structure - Scale free networkAll interactions in a cell

Global structureClass/Fold

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)

Summary I – Structure of transcriptional networks

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

Transcriptional networks have a scale-free structure which confers robustnessto such systems, with hubs assuming importance

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

Global dynamics (network dynamics) of regulatory networks

Local dynamics (node dynamics) of regulatory networks

Structure of the transcriptional regulatory network

Outline

How does the global structure change in different conditions?

Cell cycle

Sporulation

Stress

Static regulatory network in yeast

~ 7000 interactions involving 3000 genes and 142 TFs

Across all cellular conditions

Global dynamics of the regulatory networks

How does the local structure change in different cellular conditions?

DNA damage

Cell cycleSporulation

Diauxic shift

Stress

Regulatory program specific transcriptional networks

Multi-stepProcesses

Regulatory programs

involved indevelopment

BinaryProcesses

Regulatory programs

involved insurvival

Pre-sporulation Sporulation GerminationE L M

G0 G1 S G2 M

Temporal dynamics of local structure

Network motifs that allow for efficient execution of regulatory stepsare preferentially used in different regulatory programs

fast acting &

direct

slow acting &

indirect

fast acting &

direct

Multi-step regulatory programs (development)

Binary regulatory programs (survival)

Temporal dynamics of global structure (hubs)

Condition specific hubs

Each regulatory program is triggered

by specific hubs

Permanent hubs

Active across allregulatory programs

Hubs regulate other hubs to trigger cellular events

This suggests a dynamic structure which transfers ‘power’ between hubs to

trigger distinct regulatory programs (developmental &

survival)

Sub-networks re-wire both their local and global structure to respond to cellular conditions efficiently

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

binary conditions

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

Diauxic shift DNA damage Stress

Quick response

multi-stage conditions

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

Cell cycle Sporulation

Fidelity in response

Summary II – Temporal dynamics of network structure

Different regulatory proteins become global regulatory hubsunder various cellular conditions. This allows for transition between

regulatory programs by switching on condition specific hubs

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

Network motifs are preferentially used by different regulatory programs. This allows for

efficient response to different cellular conditions

Global dynamics (network dynamics) of regulatory networks

Local dynamics (node dynamics) of regulatory networks

Structure of the transcriptional regulatory network

Outline

Dynamics in transcription and translation

Each node in the network represents several entities (gene, mRNA, and protein) and events (transcription, translation, degradation, etc) that are compressed in

both space and time

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7 8

10

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7 8

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9 3

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7 810

2

9

1

Higher-order organization of transcriptional regulatory networks

Flat structureInherent higher-order organization Hierarchical structure

TranscriptionFactor (TF)

Target gene (TG)

TranscriptionFactor

Target gene

Hierarchical organization of the yeast regulatory network

MET32

OAF1

PIP2

MBP1*MAC1 ARG80 ARO80

UME6*

HSF1*◄ INO2

NDT80

ABF1*◄ MCM1◄

SKN7*

MAL33 NRG1*HAL9

AZF1

MET31

IXR1

CHA4

ZAP1

ACE2

CUP9

GAT3

HMLALPHA2

PLM2*

SMP1

TOS8*

YOX1*

ADR1

DAL80

GCN4*

INO4

PUT3

SOK2*

TYE7

AFT1*

DAL81

GLN3

LEU3

RAP1*◄

STE12*

XBP1

AFT2*

FHL1*◄

GTS1

MIG1

REB1*◄

SUT1

YAP1

ARG81

FKH1

GZF3

MIG2

RIM101

SWI4*

YAP5*

ASH1

FKH2*

HAP1

MSN2*

ROX1

SWI5

YAP6*

CBF1*

GAL4

HAP4

MSN4*

RPN4

TEC1*

YAP7*

CIN5*

GAT1

HCM1*

PHD1*

SKO1

TOS4*

YHP1

RTG3

HMRA1

RME1

SFL1

SRD1

HMRA2

SIP4

STP2

PDR3

CAT8

SPT23

BAS1

HMLALPHA1

MATALPHA1

PDR8

HMS1

STP1 PHO4

UGA3

GCR1◄

STB4

FZF1 MET4◄

RGT1

YAP3

MET28

STB5

HMS2

PDR1

SUM1

YDR026C

MGA1*

ACA1

CUP2CRZ1

CST6

MIG3

YRR1

CAD1

ECM22 LYS14

MSN1

RGM1

YER130C

HAC1

MAL13

MSS11

SFP1

YER184C

PDC2◄

RTG1

MOT3

RDS1

WAR1

YML081W

ARR1

DAT1

URC2 USV1

OT

U1

R

PH

1

PH

O2

ED

S1

FLO8*

PPR1

RDR1

RLM1

STP4

THI2

UPC2

YBL054W

YDR266C

YJL206C

YKR064W

YRM1

Level 7

Level 6

Level 5

Level 4Level 3

Level 2

Level 1

To

p (

25

)C

ore

(6

4)

Bo

tto

m (

59

)

YPR196W

◄ Essential genes* Regulatory hubs

DAL82

IME2

YDR049W

TopologicalSort

Toplayer

Corelayer

Bottomlayer

Target Genes

Hierarchical organization of regulatory proteins

Regulatory proteins are hierarchically organizedinto three basic layers

Do TFs in the different hierarchical levels havedistinct dynamic properties?

Target Genes

Top layer (29)

Core layer (57)

Bottom layer (58)

Datasets characterizing dynamics of transcription and translation

Transcript abundances for yeast grown in YPD

(S. cerevisiae) and Edinburgh minimal

medium (S. pombe) were determined by using an Affymetrix high density oligonucleotide array.

Transcript abundance(Holstege et al., 1998)

Transcript half-life(Wang et al., 2002)(Yang et al., 2003)

Transcript half-lives were determined by obtaining

transcript levels over several minutes after inhibiting

transcription. This was done using the temperature

sensitive RNA polymerase rpb1-1 mutant S. cerevisiae

strain.

Transcriptional dynamics

Protein half-life(Belle et al, 2006 )

Protein half-lives were determined by first inhibiting protein

synthesis via the addition of cyclohexamide and by

monitoring the abundance of each TAP-tagged protein in the yeast

genome as a function of time.

Estimates of the endogenous protein

expression levels during log-phase were

obtained by TAP-tagging every yeast

protein for S. cerevisiae.

Protein abundance (Ghaemmaghami et al, 2003)

Translational dynamics

Regulation of regulatory proteins within the hierarchical framework

Regulation of protein abundance and degradation

appears to be a major control mechanism by which the

steady state levels of TFs are controlled

post-transcriptional regulation plays an important role in ensuring the availability of right amounts of each TF within the cell

Regulation of transcript abundance or degradation

does not appear to be a major control mechanism by which the steady state levels of TFs

are controlled

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0

3

1

8

Population of cells

No. of copiesof Protein i

Fre

qu

en

cy

No of copies

Almostno noise

Fre

qu

en

cy

No of copies

Noisy

Fre

qu

en

cyNo of copies

Very Noisy

Noise in protein levels in a population of cells

Noise in a population of cells can be beneficial where phenotypic diversity could be advantageous but detrimental if homogeneity and fidelity in cellular behaviour is required

Noise levels of regulatory proteins

Regulatory proteins in the top layer are more noisy than the ones in the core or bottom layer

More noisyLess noisy

Target Genes

Top layer

Core layer

Bottom layer

Implication

Underlying network

Noise in TF expression may permit differential utilization of the sameunderlying regulatory network in different individuals of a population

Differential utilization of the same underlying network by different individuals in a population of cells

Individual 2 Individual 3 Individual 4Individual 1

Noise in expression of a master regulator of sporulation in yeast

Gene network controlling sporulation

High variability in the expression of top-level TFs in a population of cells may confer a selective advantage as

this permits at least some members in a population to respond quickly to

changing conditions

Nachman et al Cell (2008)

Summary III – local dynamics of regulatory networks

Our results suggest that the core- and bottom-level TFs are more tightly regulated at the post-transcriptional level

rather than at the transcriptional level itself

Our findings suggest that the interplay between the inherent hierarchy of the network and the dynamics of the TFs permits differential utilization of the same underlying

network in distinct members of a population

Conclusions

Global

dynamics

Local

dynamics

Different regulatory programs preferentially use different network motifs

Top, core and bottom level TFs display distinct patterns in protein abundances and

half-lives

Condition specific hubs allow for transition between regulatory programs

Top level TFs are more noisy than the core or bottom level TFs

Regulatory networks are dynamic within the timescale of an organism and the interplay between the global and local dynamics makes the network

both robust and adaptable to changing conditions

May permit robust response to changing conditions by efficiently re-wiring the

active regulatory network

May provide flexibility in response in a population of cells, making the organism more adaptable to changing conditions

Sarah TeichmannMRC-LMB, UK

MRC - Laboratory of Molecular Biology, UKNational Institutes of Health, USA

Schlumberger and Darwin College, Cambridge, UK

L AravindTeresa PrzytyckaNCBI, NIH, USA

Nick LuscombeEBI, Hinxton, UK

Mark GersteinHaiyuan YuMike Snyder

Yale University, USA

Acknowledgements

Raja JothiS Balaji

NCBI, NIH, USA

Arthur WusterJoerg Gsponer

MRC-LMB, UK

Joshua GrochowChicago University, USA

4a. Run iterative leaf-removal algorithm

Layer 3

Layer 4 1

Layer 1 9

Layer 2 2

7,8

3,4,5,6

11

10

5. Invert the levels

Layer 4 1

Layer 2

103,4,5,6

Layer 1 9

Layer 3

7,82

11

1. Identify strongly connectedcomponents (SCCs)

9

3

5

4

6

1

2

7 811

10

2. Obtain Directed Acyclic Graph (DAG) by collapsing the SCCs

9 3,4,5,6

1

7,82

11

10

3. Construct the transpose of DAG (reverse the direction of edges)

10

3,4,5,6

1

7,82

11

9

hie

rarch

y

6. Combine to

d

de

ceu

Layer 3

Layer 4 1

Layer 1 9

Layer 2 2

3,4,5,6

10

7,8

11

Layer 3

Layer 4 1

Layer 1 7

Layer 2 2

10

11

7. Construct the final hierarchy

3 4 5

8 9

6

4b. Run iterative leaf-removal algorithm

Layer 4 9

Layer 2 103,4,5,6

Layer 1 1

Layer 3 7,82

11

4b. Run iterative leaf-removal algorithm

Topological Sort: An approach to infer hierarchical organization in networks

Identify strongly connectedcomponents (SCCs)Obtain Directed Acyclic Graph (DAG) by collapsing the SCCsRun iterative leaf-removal algorithm

Construct the transpose of DAG(reverse the direction of edges)Run iterative leaf-removal algorithmInvert the layersCombine to deduce the hierarchyConstruct the final hierarchy

Applicable on cyclic networks

Consistent ordering

Scalable

Allows for ambiguity (e.g. Mouse)

Food web network

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3

4

5

6

2-4

Leaf-removal algorithm BFS-level algorithm Topological sort

predator prey

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4

1

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4

?

Methods to infer hierarchical organization in networks

Yeast transcriptional regulatory network

TranscriptionFactor (TF)

Target gene (TG)

156 TFs (regulatory proteins)

4,403 TGs (proteins being regulated)

12,702 links (regulatory interactions)

In-degree (#incoming connections) = 2.9

On average, each gene is regulated by ~3 TFs

Out-degree (#outgoing connections) = 81.4

On average, each TF regulates ~80 genes

y = 8.7209x-1.0941

R2 = 0.920.01

0.1

1

0 100 200 300 400

TF out-degree

Fre

qu

ency

AllTopCoreBottomUnclassified

y = 43.706x-1.4426

R2 = 0.87

y = 1E-08x3 - 8E-06x2 + 0.0015x + 0.0122R2 = 0.47

y = 145.13x-1.8078

R2 = 0.92

y = 1.8904x-0.7092

R2 = 0.57

Topological properties of regulatory proteins within the hierarchical framework

85.4

145.8

28.2

0 80 160

Top

Core

Bottom

Avg # TGs (out-degree)

1771

3639

1152

0 2000 4000

Top

Core

Bottom

Total number of TGsTarget Genes

Top

Core

Bottom

20.7

43.9

0.0

0 25 50

Top

Core

Bottom

% TFs that are hubs

p < 10-4

Target Genes

Top

Core

Bottom

13.8

5.3

3.4

0 5 10 15

Top

Core

Bottom

% TFs that are essential

p < 0.0644

70.6

65.3

56.9

40 60 80

Top

Core

Bottom

% Conservation

p < 10-4

p < 0.041

p < 3x10-3

Genetic and Evolutionary aspects of regulatory proteins withinthe hierarchical framework

Do different proteins become hubs under different conditions?Is it the same protein that acts as a regulatory hub across multiple conditions?

Cell cycle Sporulation Diauxic shift DNA damage Stress

Temporal dynamics of global structure

Condition specific networks are all scale-free

Internal & multi-stepregulatory programs

(Development)

External & binary regulatory programs(Survival)

Target Genes

Top layer

Core layer

Bottom layer

TranscriptionFactor (TF)

Target gene (TG)

Top Layer(29)

Core Layer(57)

Bottom Layer(58)

Unclassified (12)

ABF1 ARG80 ARO80 ARR1 AZF1 CHA4 DAT1 HAL9

HSF1 INO2 IXR1 MAC1 MAL33 MBP1 MCM1 MET31

MET32 NDT80 NRG1 OAF1 OTU1 PIP2 PDC2 PHO2

RPH1 RTG1 SKN7 UME6 ZAP1 FLO8

ACE2 ADR1 AFT1 AFT2 ARG81 ASH1 CBF1 CIN5 PPR1

CUP9 DAL80 DAL81 FHL1 FKH1 FKH2 GAL4 GAT1 RDR1

GAT3 GCN4 GLN3 GTS1 GZF3 HAP1 HAP4 HCM1 RLM1

HMLALPHA2 INO4 LEU3 MIG1 MIG2 MSN2 MSN4 PHD1 STP4

PLM2 PUT3 RAP1 REB1 RIM101 ROX1 RPN4 SKO1 THI2

SMP1 SOK2 STE12 SUT1 SWI4 SWI5 TEC1 TOS4 UPC2

TOS8 TYE7 XBP1 YAP1 YAP5 YAP6 YAP7 YHP1 YBL054W

YOX1 YDR266C

ACA1 BAS1 CAD1 CAT8 CRZ1 CST1 CUP2 ECM22 YJL206C

FZF1 GCR1 HAC1 HMALPHA1 HMRA1 HMRA2 HMS1 HMS2 YKR064W

LYS14 MAL13 MATALPHA1 MET28 MET4 MGA1 MIG3 MOT3 YRM1

MSN1 MSS11 PDR1 PDR3 PDR8 PHO4 RDS1 RGM1

RGT1 RME1 RTG3 SFL1 SFP1 SIP4 SPT23 SRD1

STB4 STB5 STP1 STP2 SUM1 UGA3 WAR1 YAP3

YBR033W YDR026C YDR049W YDR520C YER130C YER184C YML081W YPL230W

YPR196W YRR1

Higher-order organization in yeast regulatory network

MAC1 NDT80ARG81

MCM1◄MAL33IXR1INO4GLN3 LEU3GTS1 MIG1 MIG2HMS1

HSF1*◄ INO2HAL9 HAP1 HAP4 HCM1* HMRA1 HMRA2HMLALPHA2 HMS2

UME6*SUT1 SWI4* SWI5 TEC1* TOS4*STP2STP1 UGA3SUM1ZAP1YOX1*XBP1 YAP5* YAP6* YAP7* YHP1YDR026CYAP3

OAF1 PIP2 TOS8* TYE7 YAP1RPN4 SPT23PDC2◄

PDR3MATALPHA1IME1 MET28 MIG3LYS14 MSN1MAL13 MSS11MOT3

ARO80ABF1*◄ CHA4ACE2 DAL80AFT1* CBF1* CIN5*ARR1 DAT1

SKN7* SMP1 SOK2* STE12*ROX1 SKO1RTG3 SIP4RTG1RPH1

PLM2* PUT3 RAP1*◄ REB1*◄ RIM101PHD1* RME1PHO4 RGT1PHO2

HMLALPHA1BAS1 GCR1◄ACA1 CUP2CRZ1 CST6CAD1 ECM22 EDS1

GAT3 GCN4*FHL1*◄ FKH1 GZF3GAL4 GAT1FZF1 HAC1FLO8*

SFL1 SRD1PDR8 STB4RGM1 SFP1RDS1PPR1 RDR1 RLM1

YRR1YER130C YER184C YML081WYJL206C YKR064W YRM1YPR196W

ARG80 AZF1 CUP9ADR1 DAL81AFT2* ASH1 FKH2*CAT8 DAL82

MET32MBP1* NRG1*MET31 MSN2* MSN4*MET4◄ PDR1MGA1* OTU1

STB5 WAR1URC2 USV1STP4 THI2 UPC2 YBL054W YDR266CYDR049W

Level 4

Level 3

Level 2

Level 1

◄ Essential genes* Regulatory hubsTFs with zero in-degree

A BFS-level algorithm on Yeast transcription network

NDT80

AFT2

UME6

AFT1

OAF1

YML081WSTB4RGM1YER130C Level 1

Level 2

Level 3

Level 4

Level 2

Level 3

GAT1

ARG81

CUP9

YAP6 XBP1

IME1RDS1 ACA1

Level 2

Level 3

Level 4

Level 2

StronglyConnectedComponent

B An instance of inaccurate hierarchyas inferred by the BFS-level algorithm

“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 networks

Temporal dynamics of global structure (hubs)

CC SP DS DD SR TF

250 45 20 30 15 Swi6

Con

diti

on s

peci

fic

hubs

Per

man

ent h

ubs

Connectivity profile: No of regulated genes

Existence of condition specific hubs

Each regulatory program is triggered

by specific hubs

Presence of permanenthubs across all

regulatory programs

Hubs regulate other hubs to trigger cellular events

This suggests a dynamic structure which transfers ‘power’ between hubs to trigger distinct regulatory programs (developmental & survival)