Achim Tresch Computational Biology Gene Center Munich

1. Modeling Combinatorial Intervention Effects in Transcription Networks (The Sound of One-Hand Clapping) Achim TreschComputational Biology Gene Center Munich

2. The Question If two hands clap and there is a sound; what is the sound of one hand? (Japanese Kōan) Kōan A paradoxical anecdoteor riddle, used in Zen Buddhism to demonstrate the inadequacy of logical reasoning and to provoke enlightenment.

3. Synthetic Genetic Interactions How to define “Interaction“ mathematically? GrowthYB of single manipulation of B ΔB GrowthYA of single manipulation of A Synthetic Genetic Array ΔA Growth YABof double manipulation of A and B ΔA ΔB modified after Collins, Krogan et al., Nature 2007

4. Synthetic Genetic Interactions Phenotype Measurement YBof single perturbation How to define “Interaction“ mathematically? ΔB ΔA Phenotype Measurement YAof single perturbation Phenotype Measurement YABof double perturbation The interaction score SAB is a function of the two single perturbations and the combined perturbation, ΔA ΔB SAB= SAB (YA ,YB ,YAB )

5. Synthetic Genetic Interactions Common Interaction Scores 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 ) 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 ) Results crucially depend on f Interaction Scores are not very reliable Mani, Roth et al., PNAS 2007

6. Synthetic Genetic Interactions Breakthrough: Combine a set of weak predictors to create a strong predictor (guilt by association = correlation of interaction scores) Pan, Boeke et al., Cell 2006 Collins, Krogan et al., Nature 2007 Cartoon by Van de Peppel et al, Mol. Cell 2005

7. Synthetic Genetic Interactions Take home message: Two components are likely to interact (physically) whenever they have the same interaction partners Costanzo M, Myers CL, Andrews BJ, Boone C, et al.: Science 2010

8. Screening for TF interactions If two hands clap and there is a sound; what is the sound of one hand? ΔA One manipulation High dimensionalreadout

9. Genetic interactions from one perturbation Step 1: Construct a transcription factor - target graph a) From ChIP binding experiments Harbison, Fraenkel, Young et al. Nature 2004MacIsaac, Fraenkel et al. BMC Bioinformatics 2006 b) From protein binding arrays, followed by PWM-based predictions Ansari et al., Nature Methods 2010 Berger, Bulyk et al., Nature Biotech 2006

10. Genetic interactions from one perturbation 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)

11. Genetic interactions from one perturbation Step 2: Combine TF-target information and expression data ~2.000 target genes 118 transcription factors 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 … Common Idea: A TF is active if its set of target genes shows significantly altered expression. To quantify this, various tests are constructed. Graph obtained from MacIsaac et al. (BMC Bioinformatics 2006)

12. Genetic interactions from one perturbation Step 3: Given TF1 and TF2, group genes into 4 interaction classes TF1 TF1 Binding sites Synthesis rates during salt stress TF 1 TF 2 gene 1 TF2 TF2 TF 1 is active gene 2 TF 2 is active gene 3 TF 1+2 active gene 4 time Antagonistic interaction of TF 1+2

13. Genetic interactions from one perturbation Step 3: Given TF1 and TF2, group genes into 4 interaction classes Binding sites Synthesis rates during salt stress TF 1 TF 2 gene 1 TF 1 is inactive gene 2 TF 2 is inactive gene 3 TF 1+2 active gene 4 time Synergistic interaction of TF1+2

14. Genetic interactions from one perturbation Step 4: Use these 4 groups to define an interaction score For any pair of transcription factors T1 and T2, we perform a logistic regression. (for all genes g) Our interaction score for the pair (T1,T2) is then β12.

15. Genetic interactions from one perturbation Step 4: Use these 4 groups to define an interaction score Binding sites Example: TF 1 TF 2 gene 1 TF 1 is active gene 2 TF 2 is active gene 3 TF 1+2 active gene 4 time Antagonistic interaction

16. 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 „One hand clapping“ Miller, Tresch, Cramer et al., Mol. Syst. Biol. 2010, in revision

17. Application: Osmotic stress in yeast Validation with BioGRID database: Among 84 TFs under consideration (with enough targets), 3486 potential interactions Exist. Only 97 interactions are recorded.

18. Application: Osmotic stress in yeast Validation with BioGRID database: Single interactions scores don‘t work well Profile correlations do work

19. Genetic interactions from one intervention One hand clapping can be applied to: Microarray data, Pol II ChIP data, nascent RNA data Application to a similar dataset leads to similar results: (Mitchell, Pilpel at al. Nature 2009): 3 stress responses: osmotic stress NaCl, osmotic stress KCl, heat shock

20. Acknowledgements Gene Center Munich: Patrick CramerDietmar MartinBjörn Schwalb Sebastian Dümcke

21. My Answer Two hands clap and there is a sound; what is the sound of one hand? It is similar for transcription factors that interact. Systems Buddhism Zen Biology