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Transcription regulation: a genomic network. Nicholas Luscombe Laboratory of Mark Gerstein Department of Molecular Biophysics and Biochemistry Yale University Gerstein lab : Haiyuan Yu Snyder lab : Christine Horak Teichmann lab : Madan Babu.

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transcription regulation a genomic network

Transcription regulation: a genomic network

Nicholas Luscombe

Laboratory of Mark GersteinDepartment of Molecular Biophysics

and Biochemistry

Yale University

Gerstein lab: Haiyuan Yu

Snyder lab: Christine Horak

Teichmann lab: Madan Babu

slide2

Understand Proteins, through analyzing populations

Structures(Motions, Composition)Functions(Locations, Interactions)Evolution(Pseudogenes)

Work on a diverse range of data

Microarrays

Sequences

Structures

central post genomic terms

Proteome

PubMed Hits

Central post-genomic terms

Transcriptome

slide4

XX

XX

X

XXX

Transcriptome: the set of all mRNAs expressed in a cell at a specific time

Microarrays:

- expression

- ChIp-chip

Transcriptome

Initial Step: genome sequence, genes and genomic features

overview
Overview
  • ChIp-chip experiments for
  • 11 transcription factors
  • - SBF & MBF
  • Computational studies of
  • yeast regulatory network
overview1
Overview
  • ChIp-chip experiments for
  • 11 transcription factors
  • - SBF & MBF
  • Computational studies of
  • yeast regulatory network
slide7

SBF and MBF are cell cycle

transcription factors

G1

SBF

MBF

M

S

G2

[Horak, Luscombe et al (2002), Genes & Dev, 16: 3017 ]

slide8

SBF

=

SCB

Binding Factor

Swi6p

Swi4p

SCB

G

1

c

y

c

l

i

n

g

e

n

e

s

SCB = Swi4p-Swi6p cell cycle box

Regulator at the G1/S phase of cell cycle

Only five known target genes

[Horak, Luscombe et al (2002), Genes & Dev, 16: 3017 ]

slide9

MBF

=

MCB

Binding Factor

Swi6p

Mbp1

MCB

S-phase

cyclin

genes

DNA

synthesis genes

Mlu1

Cell Cycle Box

MCB

=

[Horak, Luscombe et al (2002), Genes & Dev, 16: 3017 ]

slide10

SCB CGCGAAAA

MCB ACGCGN

SBF binds MCBs and MBF binds SCBs

SBF

MBF

?

SCB

MCB

[Horak, Luscombe et al (2002), Genes & Dev, 16: 3017 ]

slide11

ChIp-chip

Chromatin Immunoprecipitation

and

DNA chip technology

[Horak, Luscombe et al (2002), Genes & Dev, 16: 3017 ]

slide12

Crosslink

IP

Yeast ChIp-chip

Epitope-tagged

Untagged

Lyse

Sonicate

Reverse X-links

Label

Hybridize

[Horak, Luscombe et al (2002), Genes & Dev, 16: 3017 ]

slide14

Swi4: 183 genes

  • MBP: 98 genes
  • Swi4: 163 intergenic regions
  • Mbp1: 87 intergenic regions
  • 43 were bound by both

[Horak, Luscombe et al (2002), Genes & Dev, 16: 3017 ]

slide15

MBF

11%

22%

5%

9%

8%

14%

6%

4%

1%

7%

2%

41%

22%

24%

24%

Cell Wall

DNA synthesis

Morphogenesis

Cell Cycle

Transcription

Proteolysis

Other

Unknown

Functions of SBF and MBF Gene Targets

SBF

[Horak, Luscombe et al (2002), Genes & Dev, 16: 3017 ]

slide16

SWI4

HCM1

YOX1

TOS4

PLM2

TYE7

YHP1

YAP5

POG1

TOS8

SBF and MBF

target other

transcription Factors

WTM1

M

SPT21

MBF

MAL33

RGT1

G1

PDR1

SBF

NDD1

GAT2

SOK2

S

G2

[Horak, Luscombe et al (2002), Genes & Dev, 16: 3017 ]

slide18

Cell Cycle

Control

TF’s have distinct

Functional Roles

Hcm1

Tos4

Tos8

Pog1

Bud Growth

Chromosome

Segregation

and Mitosis

Yap5

Yhp1

Yox1

Plm2

Tye7

Protein

Synthesis and

Metabolism

DNA Synthesis

and Repair

[Horak, Luscombe et al (2002), Genes & Dev, 16: 3017 ]

slide19

Yeast transcriptional regulatory network

[Horak, Luscombe et al (2002), Genes & Dev, 16: 3017 ]

overview2
Overview
  • ChIp-chip experiments for
  • 11 transcription factors
  • - SBF & MBF
  • Computational studies of
  • yeast regulatory network
slide21

Comprehensive TF dataset

142 transcription factors

3,420 target genes

7,074 regulatory interactions

[Yu, Luscombe et al (2003), Trends Genet, 19: 422]

slide22

Comprehensive Yeast TF network

Transcription Factors

  • Very complex network
  • But we can simplify:
    • Topological measures
    • Network motifs

Target Genes

[Barabasi, Alon]

slide23

Comprehensive Yeast TF network

Transcription Factors

  • Very complex network
  • But we can simplify:
    • Topological measures
    • Network motifs

Target Genes

[Barabasi, Alon]

slide24

Topological measures

Indicate the gross topological structure of the network

Degree

Path length

Clustering coefficient

[Barabasi]

slide25

Incoming degree = 2.1

  • each gene is regulated by ~2 TFs

Outgoing degree = 49.8

each TF targets ~50 genes

Topological measures

Number of incoming and outgoing connections

Degree

[Barabasi]

slide26

Regulatory hubs

>100 target genes

Dictate structure of network

Scale-free distribution of outgoing degree

  • Most TFs have few target genes
  • Few TFs have many target genes

[Barabasi]

slide27

1 intermediate TF

Topological measures

Number of intermediate TFs until final target

Starting TF

Final target

Indicate how immediate

a regulatory response is

Average path length = 4.7

= 1

Path length

[Barabasi]

slide28

4 neighbours

1 existing link

6 possible links

Topological measures

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

Measure how inter-connected the network is

Average coefficient = 0.11

Clustering coefficient

= 1/6 = 0.17

[Barabasi]

slide29

Comprehensive Yeast TF network

Transcription Factors

  • Very complex network
  • But we can simplify:
    • Topological measures
    • Network motifs

Target Genes

[Alon]

slide30

Network motifs

Regulatory modules within the network

SIM

MIM

FBL

FFL

[Alon]

sim single input motifs
SIM = Single input motifs

HCM1

ECM22

STB1

SPO1

YPR013C

[Alon; Horak, Luscombe et al (2002), Genes & Dev, 16: 3017 ]

mim multiple input motifs
MIM = Multiple input motifs

SBF

MBF

SPT21

HCM1

[Alon; Horak, Luscombe et al (2002), Genes & Dev, 16: 3017 ]

fbl feed back loops
FBL = Feed-back loops

MBF

SBF

Tos4

[Alon; Horak, Luscombe et al (2002), Genes & Dev, 16: 3017 ]

ffl feed forward loops
FFL = Feed-forward loops

SBF

Pog1

Yox1

Tos8

Plm2

[Alon; Horak, Luscombe et al (2002), Genes & Dev, 16: 3017 ]

slide35

Comprehensive Yeast TF network

Transcription Factors

  • Very complex network
  • But we can simplify:
    • Topological measures
    • Network motifs

Target Genes

[Barabasi, Alon]

slide36

Essentiality of regulatory hubs

  • Hubs dictate the overall structure of the network
  • Represent vulnerable points
  • How important are hubs for viability?
  • Integrate gene essentiality data

Transcription Factors

[Yu et al (2004), Trends Genet, 20: 227

slide37

hubs

(<100 targets)

All TFs

% TFs that are essential

non-hubs

(<100 targets)

non-hubs

(<100 targets)

All TFs

All TFs

Essentiality of regulatory hubs

  • Essential genes are lethal if deleted from genome
    • 1,105 yeast genes are essential
  • Which TFs are essential?

Hubs tend to be more essential than non-hubs

[Yu et al (2004), Trends Genet, 20: 227

slide38

Yeast expression data

[Brown]

Influence of network on gene expression

Transcription Factors

  • How does the regulatory network affect gene expression?

Target Genes

[Yu, Luscombe et al (2003), Trends Genet, 19: 422]

clustering the yeast cell cycle

[Brown, Botstein, Church]

Clusteringthe yeast cell cycle

mRNA expression level (ratio)

Time->

Microarray timecourse of 1 ribosomal protein

clustering the yeast cell cycle1
Clusteringthe yeast cell cycle

Random relationship from 18M

clustering the yeast cell cycle2

Clusteringthe yeast cell cycle

Clusteringthe yeast cell cycle

Close relationship from 18M

(Associated Ribosomal Proteins)

local clustering algorithm identifies further reasonable types of expression relationships

Time-Shifted

Inverted

Simultaneous

Traditional

Global

Correlation

Local Clustering algorithm identifies further (reasonable) types of expression relationships

(Algorithm adapted from local sequence alignment)

[Qian et al]

single input motifs
Single input motifs

[Yu, Luscombe et al (2003), Trends Genet, 19: 422]

multiple input motifs
Multiple input motifs

[Yu, Luscombe et al (2003), Trends Genet, 19: 422]

feed forward motifs
Feed forward motifs

[Yu, Luscombe et al (2003), Trends Genet, 19: 422]

summary of expression relationships
Summary of expression relationships
  • Target genes tend to be co-expressed
  • Co-expression is higher when more TFs are involved
  • TFs and target genes have complex relationships
  • Often expression profiles are time-shifted

[Yu, Luscombe et al (2003), Trends Genet, 19: 422]

slide47

Dynamic Yeast TF network

Transcription Factors

  • Analysed network as a static entity
  • But network is dynamic
    • Different sections of the network are active under different cellular conditions
  • Integrate more gene expression data

Target Genes

[Luscombe et al, Nature (In press)]

slide48

Gene expression data

  • Genes that are differentially expressed under five cellular conditions
  • Assume these genes undergo transcription regulation

[Luscombe et al, Nature (In press)]

slide49

Define differentially expressed genes

  • Identify TFs that regulate these genes
  • Identify further TFs that regulate these TFs

Backtracking to find active sub-network

Active regulatory sub-network

[Luscombe et al, Nature (In press)]

slide50

cell cycle sub-network

  • 70 TFs
  • 280 genes
  • 550 interactions

Network usage under cell cycle

complete network

  • 142 TFs
  • 3,420 genes
  • 7,074 interactions

[Luscombe et al, Nature (In press)]

slide51

Network usage under different conditions

Cell cycle

How do the networks change?

  • topological measures
  • network motifs

Sporulation

Diauxic shift

DNA damage

Stress

[Luscombe et al, Nature (In press)]

slide52

Network usage under different conditions

Cell cycle

How do the networks change?

  • topological measures
  • network motifs

Sporulation

Diauxic shift

DNA damage

Stress

[Luscombe et al, Nature (In press)]

slide53

incoming degree

path length

clustering coefficient

outgoing degree

random network size

Our expectation

  • Literature: Network topologies are perceived to be invariant
    • [Barabasi]
    • Scale-free, small-world, and clustered
    • Different molecular biological networks
    • Different genomes
  • Random expectation: Sample different size sub-networks from complete network and calculate topological measures

Measures should remain constant

[Luscombe et al, Nature (In press)]

slide54

Binary:

Quick, large-scale

turnover of genes

Multi-stage:

Controlled, ticking

over of genes

at different stages

Outgoing degree

  • “Binary conditions” greater connectivity
  • “Multi-stage conditions” lower connectivity

[Luscombe et al, Nature (In press)]

slide55

Binary

Multi-stage

Path length

  • “Binary conditions”

 shorter path-length

 “faster”, direct action

  • “Multi-stage” conditions

 longer path-length

 “slower”, indirect action

[Luscombe et al, Nature (In press)]

slide56

Binary

Multi-stage

Clustering coefficient

  • “Binary conditions”

smaller coefficients

less TF-TF inter-regulation

  • “Multi-stage conditions”

 larger coefficients

 more TF-TF inter-regulation

[Luscombe et al, Nature (In press)]

slide57

Network usage under different conditions

Cell cycle

How do the networks change?

  • topological measures
  • network motifs

Sporulation

Diauxic shift

DNA damage

Stress

[Luscombe et al, Nature (In press)]

slide58

single input motif

multiple input motif

feed-forward loop

random network size

Our expectation

  • Literature: motif usage is well conserved for regulatory networks across different organisms [Alon]
  • Random expectation: sample sub-networks and calculate motif occurrence

Motif usage should remain constant

[Luscombe et al, Nature (In press)]

slide59

Network motifs

[Luscombe et al, Nature (In press)]

summary of sub network structures

binary conditions

multi-stage conditions

  • fewer target genes
  • longer path lengths
  • more inter-regulation
  • between TFs
  • more target genes
  • shorter path lengths
  • less inter-regulation
  • between TFs
Summary of sub-network structures

[Luscombe et al, Nature (In press)]

slide61

Network hubs

Regulatory hubs

Do hubs stay the same or do they change over between conditions?

Do different TFs become important?

[Luscombe et al, Nature (In press)]

slide62

Our expectation

  • Literature: current opinion is divided:
    • Hubs are permanent features of the network regardless of condition
    • Hubs vary between conditions
  • Random expectation: sample sub-networks from complete regulatory network
    • Random networks converge on same TFs
    • 76-97% overlap in TFs classified as hubs
    • ie hubs are permanent

[Luscombe et al, Nature (In press)]

slide63

cell cycle

Swi4, Mbp1

Ime1, Ume6

sporulation

Unknown functions

transitient hubs

transient hubs

diauxic shift

DNA damage

Msn2, Msn4

stress response

all conditions

permanent hubs

permanent hubs

  • Some permanent hubs
    • house-keeping functions
  • Most are transient hubs
    • Different TFs become key regulators in the network
  • Implications for condition-dependent vulnerability of network

[Luscombe et al, Nature (In press)]

slide64

sporulation

cell

cycle

diauxic

shift

stress

Network shifts its weight between centres

permanent

hubs

DNA

damage

[Luscombe et al, Nature (In press)]

slide65

Regulatory circuitry of cell cycle time-course

  • Multi-stage conditions have:
    • longer path lengths
    • more inter-regulation between TFs
  • How do these properties actually look?
    • examine TF inter-regulation
    • during the cell cycle

[Luscombe et al, Nature (In press)]

slide66

transcription factors used in cell cycle

phase specific

ubiquitous

Regulatory circuitry of cell cycle time-course

early G1 late G1 S G2 M

1. serial inter-regulation

Phase-specific TFs show serial inter-regulation

 drive cell cycle forward through time-course

[Luscombe et al, Nature (In press)]

slide67

transcription factors used in cell cycle

phase specific

ubiquitous

Regulatory circuitry of cell cycle time-course

2. parallel inter-regulation

  • Ubiquitous and phase-specific TFs show parallelinter-regulation
  • Many ubiquitous TFs are permanenthubs
  • Channel of communication between house-keeping functions and cell cycle progression

[Luscombe et al, Nature (In press)]

summary 1
Summary 1
  • Use of microarrays for ChIp-chip experiments
  • Identification of genomic binding sites for 11 TFs
    • Large-scale
    • Direct in vivo binding
summary 2
Summary 2
  • Incorporation of many data sources to build a comprehensive network of regulatory interactions
    • 142 TFs
    • 3,420 target genes
    • 7,074 regulatory interactions
  • Very complex network of inter-regulation
  • Can be simplified
    • Topological measures that describe the overall structure
    • Network motifs that describe regulatory building blocks
summary 3
Summary 3
  • Essentiality of TFs
    • TFs with many target genes tend to be essential
  • Study of regulatory influence on gene expression
    • Regulatory targets are often co-expressed
    • TF-target expression relationships are complex
summary 4
Summary 4
  • Dynamic usage of the regulatory network
  • Different sections of the network are used in different cellular conditions
  • Underlying topology of the network changes
    • Network parameters
    • Network motifs
  • Network shifts weight between hubs
  • Serial and parallel inter-regulation between TFs to drive cell cycle progression
snyder group and collaborators
Snyder group and collaborators

Michael Snyder

Christine Horak

Lara Umansky

Kevin madden

Susana Vidan

Ghia Euskirchen

Rebecca Goetsch

John Rinn

Madhuparna Roychowdry

Gillian Hooker

Mike McEvoy

Stacy Piccirillo

Sherman Weissman

Milind Mahajan

Patrick Brown

Vishwanath Iyer

David Botstein

Charles Scafe

Kenneth Nelson

Yale Microarray Facility

gerstein group
Gerstein group

Mark Gerstein

Haiyuan Yu

Sarah Teichmann

Madan Babu

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