Achim Tresch Computational Biology Gene Center Munich

Achim TreschComputational BiologyGene Center Munich

(The Sound of One-Hand Clapping)

Modeling Combinatorial Intervention Effects in Transcription Networks

The Question

(Japanese Kōan)

If two hands clap and there is a sound; what is the sound of one hand?


Kōan

A paradoxical anecdote or riddle, used in Zen Buddhism to demonstrate the inadequacy of logical reasoning and to provoke enlightenment.

Synthetic Genetic Interactions

modified after Collins, Krogan et al., Nature 2007

How to define “Interaction“ mathematically?

Synthetic Genetic Array

ΔA

GrowthYA of single manipulation of A

ΔBGrowthYB of single manipulation of B

ΔA ΔB

Growth YAB

of double manipulation of A and B


ΔB

ΔA

ΔA ΔB

Phenotype Measurement YA

of single perturbation

Phenotype Measurement YB

of single perturbation

Phenotype Measurement YAB

of double perturbation

How to define “Interaction“ mathematically?

The interaction score SAB is a function

of the two single perturbations and the combined perturbation,

SAB = SAB (YA ,YB ,YAB )


Common Interaction Scores

Common choices for f :

f = min(YA ,YB ) (v. Liebig´s minimum rule for plant growth)

f = YA ·YB (chemical equilibrium a + b ↔ ab , [a][b] = [ab])

f = YA + YB (log version of YA ·YB )

f = log2[(2YA - 1)(2YB - 1) + 1] (essentially the same as YA + YB )

Define an expected phenotype of the double perturbation as a function f(YA ,YB ) of the single perturbation phenotypes YA and Yb. The interaction score SAB is then the deviation from the expected phenotype

SAB = YAB - f(YA ,YB )

Interaction Scores are not very reliable

Results crucially depend on f

Mani, Roth et al., PNAS 2007


Pan, Boeke et al., Cell 2006

Cartoon by Van de Peppel et al, Mol. Cell 2005

Collins, Krogan et al., Nature 2007

Breakthrough: Combine a set of weak predictors to create a strong predictor (guilt by association = correlation of interaction scores)


Costanzo M, Myers CL, Andrews BJ, Boone C, et al.: Science 2010

Take home message: Two components are likely to interact (physically) whenever they have the same interaction partners

Screening for TF interactions

ΔA

One manipulation

High dimensionalreadout



Genetic interactions from one perturbation

Harbison, Fraenkel, Young et al. Nature 2004MacIsaac, Fraenkel et al. BMC Bioinformatics 2006

Ansari et al., Nature Methods 2010Berger, Bulyk et al., Nature Biotech 2006

a) From ChIP binding experiments

b) From protein binding arrays, followed by PWM-based predictions

Step 1: Construct a transcription factor - target graph


Step 1: Construct a transcription factor - target graph

Intersection size of target sets of TF1 and TF2 can be used alone to assess TF cooperativity. (Beyer, Ideker et al., PlOS Comp. Biol 2006)


~2.000 target genes

118 transcription factors

Graph obtained from MacIsaac et al. (BMC Bioinformatics 2006)

Established Methods for the detection of univariate TF activity :

GSEA (Subramanian, Tamayo PNAS 2005)

Globaltest (Goemann, Bioinformatics 2004)

MGSEA (Bauer, Gagneur, Nucl. Acids Res. 2010)

and many more …

Step 2: Combine TF-target information and expression data

Common Idea: A TF is active if its set of target genes shows significantly altered expression.To quantify this, various tests are constructed.

gene 4time

Antagonistic interaction of TF 1+2

TF 1+2 active


gene 1

TF 1 TF 2 Synthesis rates during salt stress

gene 2

TF 1 is active

gene 3

TF 2 is active

Binding sites

TF1

TF1

TF2

TF2

Step 3: Given TF1 and TF2, group genes into 4 interaction classes


gene 1

TF 2 is inactivegene 3

TF 1 is inactivegene 2

TF 1 TF 2 Synthesis rates during salt stressBinding sites

time

Synergistic interaction of TF1+2

gene 4TF 1+2 active

Step 3: Given TF1 and TF2, group genes into 4 interaction classes


Our interaction score for the pair (T1,T2) is then β12.

TF2) and TF1 of target a is (

TF2) of target a is (

)TF1 of target a is (

~ )inducednot is (

)induced is (log

12

2

1

0

gInd

gInd

gInd

gP

gP

Step 4: Use these 4 groups to define an interaction score

(for all genes g)

For any pair of transcription factors T1 and T2, we perform a logistic regression.


),()()(~ . . . 211222110 gTFTFIndgTFIndgTFInd

gene 1

gene 3

gene 2

gene 4time

TF 1 is active

TF 2 is active

Antagonistic interaction

010

020

012210

Example:

0 ~

00

10 ~

20 ~

12210 ~ TF 1+2 active

Step 4: Use these 4 groups to define an interaction score

TF 1 TF 2Binding sites

0)()( 1010012

Application: Osmotic stress in yeast

Use the guilt by association trick to construct an interaction matrix for all transcription factors using only a two group microarray comparison!

Inclusion criterion: only TFs with >70 targets

Miller, Tresch, Cramer et al., Mol. Syst. Biol. 2010, in revision

„One hand clapping“

Validation with BioGRID database:


Among 84 TFs under consideration (with enough targets), 3486 potential interactionsExist. Only 97 interactions are recorded.


Single interactions scores don‘t work well

Profile correlations do work

Validation with BioGRID database:

Genetic interactions from one intervention

One hand clapping can be applied to: Microarray data, Pol II ChIP data, nascent RNA data

3 stress responses: osmotic stress NaCl, osmotic stress KCl, heat shock

(Mitchell, Pilpel at al. Nature 2009):

Application to a similar dataset leads to similar results:

Acknowledgements

20

Gene Center Munich:

Patrick CramerDietmar Martin

Björn Schwalb

Sebastian Dümcke

My Answer

Two hands clap and there is a sound; what is the sound of one hand?

Two hands clap and there is a sound; what is the sound of one hand?

It is similar for transcription factors that interact.It is similar for transcription factors that interact.

Zen BiologySystems Buddhism

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