Combinatorics of promoter regulatory elements determines

gene expression profiles

Yitzhak (Tzachi) Pilpel

Priya Sudarsanam

George Church

DJ Club, Feb. 2001

Goals of study

• Identify regulatory networks on a genome-wide scale

• study the combinatorial nature of transcription regulation

• Propose causal link between promoter sequence elements and expression patterns

.

Time Point 1

The current methodology for expression - regulatory motif analysis(Tavazoie et al.)

Collaboration

?

Co-occurrence

(AND)

Redundancy

(OR)

In case of two motifs derived from a cluster

Two motifs derived from the same cell-cycle cluster

Nor

mal

ized

exp

ress

ion

leve

l

0 5 10 15-3

-2

-1

0

1

2

3

4

MCB andSCB

0 5 10 15-3

-2

-1

0

1

2

3

4

Time

MCB but notSCB

0 5 10 15-4

-3

-2

-1

0

1

2

3

4

SCB but notMCB

TimeTime

.

Time Point 1

Is this motif necessarily non-functional ?

In case of multiple clusters that give rise to a motif

Condition-specific TF-TF interaction can be identified (in cell cycle)

Mcm1 ForkheadForkhead & Mcm1

0 5 10 15-3

-2

-1

0

1

2

3

4

0 5 10 15-3

-2

-1

0

1

2

3

4

Time Time Time

0 5 10 15-3

-2

-1

0

1

2

3

4

Assigning promoters to motifs :ScanACE(Hughes et al.)

Expression

.

Time Point 1

A proposed reversed analysis method:ScanACE

ScanACE

To avoid circularity we generated expression-independent motif data set

• 327 - motifs derived from MIPs functional classification (Hughes J et al.)

• 40 motifs of known TFs were added (27 overlapped to the MIPs derived motifs)

Expression experiments used

• Cell cycle (Cho et al.) • Sporulation (Chu et al.) • Diauxic shift (DeRisi et al.) • Heat shock (Eisen et al.) • Cold shock (Eisen et al.) • Reduction with dtt (Eisen et al.) • MAPK signaling (Roberts et al.) • NER (Jalinski et al.) • Peroxide (Cohen et al.)

.

0 2 4 6 8-3

-2

-1

0

1

2

3

time

.

0 5 10 15-3

-2

-1

0

1

2

3

4

time

.

0 2 4 6 8-3

-2

-1

0

1

2

3

time

Ndt80.

0 5 10 15-3

-2

-1

0

1

2

3

4

time

Putativemotif

Sporulation Cell-cycleUse a Diversity of expression data to diagnose motifs

The expression coherence score

*

**

*

*

*

*

**

*dij

Threshold dij (top 5 %)

Expression coherence=fraction of i,j pairs with dij <Threshold dij

Gene Set 1 Gene Set 2

Identification of functional motifs

0

0.05

0.1

0.15

0.2

0.25

0.3

cell cycle

sporulation

diauxic shift

heat shock

MAPK

NER

0.00

5.00

10.00

15.00

20.00

25.00

30.00

cell cycle

sporulation

diauxic shift

temp shift

New significantly highly scoring motifs

For a motif with 300 occurrences in URs the genome, the p-value for an expression coherence score of 0.1 is < 1e-12 P ( p) ~ BinomCDF(p,P,0.05), where p, and P are numbers of correlated pairs and total number of pairs, respectively

For two motifs, RRPE and PAC

.

0.06 0.1 0.14 0.18 0.22 0.260

50

100

150

200

RRPE-PAC

Expression coherence

PACRRPE

For every combination of N=2,3 motifs

•Calculate the expression coherence score of the orf that have the N motifs

•Calculate the expression coherence score of orfs that have every possible subset of N-1 motifs

•Test (statistically) the hypothesis the score of the orfs with N motifs is significantly higher than that of orfs that have any sub set of N-1 motifs

Ribosomal motifs

Rap1rRSE3

rRPE

PAC

LYS

rRSE10

RPE58 RPE49

RPE34

OCSE15

RPE57

RPE69

RPE21

RPE6

RPE72

CCA

MERE17

RPE8

RPE17

Rap1-rRPErRPE-PACPAC-rPPS2...

Cell cycle and sporulation motifs

MCB

SCB

Ndt80

SSF

Mcm1

Middlesporulation

G1-Scell cycle

G1-Scell cycle

G2-Mcell cycle

G2-Mcell cycle

Cell-cycle

Sporulation

Motif combinations establish sequence-expression causality

2

222

11 1 11

1

12121212

* *

*

53

53 5465

5

546

54

InterGMC

123456

123456

Intra-GMC

0.3

0.6

0.9

1.2

1.5

1.8

1-C

.C

0.2

0.4

0.6

0.8

1

Exp

ress

ion

cohe

renc

e 'MCB' 'cytok9' 'ndt80' 'Ume6' 'meiosis_3' 'SCB' 'CLB2' 'FKH1Sh'

Cell-cycle

Less than a minuteon a PowerMac G4(after pre-processing)

0

0.3

0.6

0.9

1.2

1.5

1.8

1-C

.C

0.2

0.4

0.6

0.8

1

Exp

ress

ion

cohe

renc

e 'MCB' 'cytok9' 'ndt80' 'Ume6' 'meiosis_n3' 'SCB' 'CLB2' 'FKH1Sh'

Sporulation

From the literature: 1)Meiotic role of SWI6 in

(Nucleic Acids Res. 1998)

2) Role for MCB in sporulation(Nature Genetics 2001)

• Different role for MCB and SCB

• A potential role of SCB-fkh in giving rise to an Ndt80-type of response

• Ndt80’s only synergistic partners in sporulation are cell cycle motifs

We add:

.

0.3

0.6

0.9

1.2

1.5

0.2

0.4

0.6

0.8

'Rap1' 'RPE6' 'PAC' 'rRPE' 'rRSE3' 'rRSE10' 'Abf1' 'REB1' 'CCA' 'RPN4' 'HAP234' 'LFTE17'

'Rap1' 'RPE6' 'PAC' 'rRPE' 'rRSE3' 'rRSE10' 'Abf1' 'REB1' 'CCA' 'RPN4' 'HAP234' 'LFTE17'

NER

What can we infer about specific network architecture ?

• Asses the contribution of each motif in a combination

• Establish hierarchy motifs

• Identify the logical association between motifs: OR for cases of redundancy, and for cases of synergy

A global motif interaction map

RPN4

Abf1

HAP2-3-4

STRE

MCB

Gcr1FKH1

Rap1

MERE11 MERE4

rRSE3rRPE

rRSE10CCA

LFTE17

OCSE15

Mcm1

FKH1Sh

SCB

Leu3

GCN4

PAC

RPE6

LYS14

cytokinesis9

Cell cycleRibosomalproteinsrRNAtranscriptiona.ametabolismStressEnergy

ChromosomeStructure

a1

2

1

What can we learns about global interaction ?

• Identify central motif players

• Suggest regulatory role of un-annotated motifs

Acknowledgments• Priya Sudarsanam

• Barak Cohen

• John Aach

• Aimee Dudley

• Jason Hughes

• Rob Mitra

• Wayne Rindone

• Fritz Roth

• Uri Keich (UCSF)

• George Church

1 2 3 . . . NGMC1

GMC1

GMC2

GMC2

GMC1

GMC1

GMC1

GMC1

GMC1

Genes defined by Motif Combination (GMC)