Combinatorics of promoter regulatory elements determines gene expression profiles Yitzhak (Tzachi) Pilpel Priya Sudarsanam George Church DJ Club, Feb.

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)