Post on 05-Jan-2016
description
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
1
3
4
5
6
7 8
10
2
9
1
3
4
5
6
7 8
10
2
9 3
4
5
6
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
5
3
19
6
8
2
12
1
7
1
3
10
4
9
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
1
2
3
4
5
6
2-4
Leaf-removal algorithm BFS-level algorithm Topological sort
predator prey
1
2
3
4
1
2
3
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)