1. Joint analysis of genetic and physical interactions in S. cerevisiae Igor Ulitsky Ron Shamir lab School of Computer Science Tel Aviv University

2. Motivation Only ≈18% percent of S. cerevisiae genes essential in rich medium More are probably essential under other conditions Essentials % probably even smaller in higher organisms Buffering seems abundant

3. Genetic interactions (GI) “Aggravating” interactions The observed phenotype is worse than what we would “expect” Synthetic lethality: joint deletion of two nonessential genes  lethal phenotype Synthetic sickness: joint deletion of two nonessential genes  slow growth

4. Assaying YKO strains Yeast knock-out (YKO) collection contains ≈ 4,800 single deletion strains. Each deleted gene is replaced by a short DNA “barcode” (2 x 20bp), flanked by universal primers. The contents of a mixture of strains can be assayed before and after treatment. Presence/absence patterns Presence/absence patterns Under/over-representation Under/over-representation

6. GI availability Systematically mapped by SGA and dSLAM Available for ≈200 gene queries and ≈4,500 targets in S. cerevisiae GI network: 13,632 interactions, 2,682 genes

7. Physical interactions (PI) Protein-protein interactions Y2H Y2H TAP TAP Protein-DNA interactions Chip 2 experiments Chip 2 experiments PI network: 68,172 interactions, 6,184 proteins

8. GI Analysis Spectra Genetically interacting homologs tend to exhibit “compensation” on the expression level R. Kafri et al. 2005 R. Kafri et al. 2005 Genetically interacting proteins frequently have a similar fold O. Dror et al. 2007 O. Dror et al. 2007 Genetic interactions can be used to delineate regulatory pathways R.P. Onge et al. 2007 R.P. Onge et al. 2007

9. Joint analysis of GI and PI Motivation: Identifying pathways Identifying pathways Connect pathways with phenotypes Connect pathways with phenotypes Understand system features (robustness, essentiality…) Understand system features (robustness, essentiality…) Initial analysis: Proteins close in the GI network slightly more likely to physically interact (Tong et al., 2004) Proteins close in the GI network slightly more likely to physically interact (Tong et al., 2004) PI hubs likely to be GI hubs (Ozier et al., 2003) PI hubs likely to be GI hubs (Ozier et al., 2003)

10. BPMs Kelley and Ideker, 2005: modeling “explanations” for GIs Within-pathway model Between-pathway model GI PI

11. Kelley & Ideker conclusions 40% of the GIs can be explained by physical models Between-pathway models explain x 3.5 times more GIs than within-pathway models Models capable of predicting protein functions

12. BPM rationale Between pathway models suggest redundancy Alternative paths Independent KOs  little effect Independent KOs  little effect Joint KOs in both  severe effect Joint KOs in both  severe effect Viable phenotype Lethal phenotype

13. Density model (Kelley and Ideker) Density scores: Drawbacks A pair of dense pathways with little GIs will score high A pair of dense pathways with little GIs will score high Some pathways may not be dense Some pathways may not be dense PI density within pathways GI density between pathways

14. DNA damage response example DNA Metabolism Oxydative stress response

15. BPM Hunting Look for pairs of pathways with GI evidence for buffering A pathway – a connected subnetwork in of PIs Model scoring based on the density of GIs Log-likelihood scores accounting for GI degrees

16. Connectivity vs. Density Dense pathways models Connected pathway models Average model size 11.8216.02 GIs between pathways 31173765 GI density between pathways 0.510.85 PI density within pathways 0.790.75 Functional enrichment in GO 72.6%71.4%

17. Algorithmics High-scoring seeds Finding heavy bicliques with connectivity constraints Finding heavy bicliques with connectivity constraints Seed optimization Greedy search maintaining connectivity Greedy search maintaining connectivity Significance filtering

18. Finding seeds Kelley and Ideker start from single GIs Maximal biclique heuristic: For every edge (u,v) in the PI network For every edge (u,v) in the PI network Identify B – the set of nodes adjacent to both u and v Identify B – the set of nodes adjacent to both u and v Divide B into connected components in PI B 1,..B m Divide B into connected components in PI B 1,..B m For every B i with |B i |>k min For every B i with |B i |>k min Identify A i – the set of nodes adjacent to all the nodes in B i Identify A i – the set of nodes adjacent to all the nodes in B i Divide A i into connected components A i 1,…, A i n, Divide A i into connected components A i 1,…, A i n, If |A i j |>k min add (B i, A i j ) to the seed list If |A i j |>k min add (B i, A i j ) to the seed list Filter for overlaps Filter for overlaps

19. Adding “pivot proteins” to BPMs Pathway bifurcationRedundant sub-complexes

20. Pivot proteins Physically connected to both pathways in the BPM Connection significant given the general network degrees

21. Analysis flow 13,632 genetic interactions 68,172 physical interactions 140 models 124 pivot proteins in 40 models BPM analysis Pivot extraction Physiological characteristics Essentiality Phenotypes Protein Abundance Codon adaptation index mRNA half-life Phoshorylation

22. BPMs as functional modules Functional enrichment analysis (TANGO): 71.4% enriched for GO “biological process” 71.4% enriched for GO “biological process” 69.3% enriched for GO “cellular compartment” 69.3% enriched for GO “cellular compartment” 46.3% of known complexes enriched in at least one BPM 46.3% of known complexes enriched in at least one BPM

23. BPMs as phenotypic modules Source data: quantitative patterns of fitness of single deletion mutants in diverse conditions (Brown et al., 2006) Phenotypic pattern homogeneity within a BPM pathway highly significant (p<10 -3 )

24. Pivots tend to be multifunctional No. of proteins PivotsExpectedSignificance GO complexes 206214.35 p=1.27ּ10 -9 KEGG pathways 7161.50 p=3.68 ּ 10 -3 Curated multi- complexed proteins (Krause et al. 2006) 3980.55 p=7.49 ּ 10 -9

25. Pivot proteins are essential Q: Are proteins active in multiple partially redundant pathways more essential? 72/124 (58%) pivots essential (p=1.42 · 10 -23 ) Enrichment is not explained by node degrees (p<10 -5 ) Essential pivots closer in function to their BPMs than nonessential pivots Pivots significantly retained during evolution (p=9.79 · 10 -9 )

26. Evolutionary retention of pivots Essentiality linked to evolutionary retention (Gustafson et al., 2006) Essentiality linked to evolutionary retention (Gustafson et al., 2006) Retention computed using 26 eukaryotic genomes Retention computed using 26 eukaryotic genomes Pivots significantly retained in evolution (Wilcoxon rank-sum test, p=9.79·10 -9 ) Pivots significantly retained in evolution (Wilcoxon rank-sum test, p=9.79·10 -9 ) Enrichment is not explained by essentiality (p = 0.029) Enrichment is not explained by essentiality (p = 0.029)

27. SWR1 Ino80 SWR1/Ino80 Example Pivots

28. SAGA/Nuclear pore example Nuclear pore SAGA Pivots

29. Spt15 example

30. Smc1 example MIND Ndc80 COMA Mitotic spindle checkpoint

31. Physiological properties of BPMs Proteins studied: 850 proteins within BPMs 850 proteins within BPMs 120 pivot proteins 120 pivot proteins Focus on mRNA half life mRNA half life # of phosphorylation sites # of phosphorylation sites

32. BPMs are strongly regulated Genes within BPMs Shorter mRNA half-life (p=1.9·10 -9 ) Shorter mRNA half-life (p=1.9·10 -9 ) More phosphorylation sites (p=6.3·10 -9 ) More phosphorylation sites (p=6.3·10 -9 ) Both properties may represent regulation Redundant pathways  Strict regulation ? Enrichments not explained by degrees, essentiality, any enriched function mRNA half-life : experimental data, phosphosites : predicted Properties generally not correlated

34. Next generation – quantitative GIs Data on quantitative genetic interactions scores is becoming available Data on quantitative genetic interactions scores is becoming available These include also alleviating interactions These include also alleviating interactions

35. Alleviating interactions The observed phenotype is better than what we would “expect” “Expect” – multiplicative model: Mutation A – 80% fitness is retained Mutation A – 80% fitness is retained Mutation B – 60% fitness is retained Mutation B – 60% fitness is retained A & B – 48% fitness is retained A & B – 48% fitness is retained The first deletion ruins the functionally of the pathway, and the second one does not have an effect An alleviating interaction is a sign of participation in the same pathway

36. Extracting BPMs from quantitative data Probabilistic model for the aggravating/alleviating interaction scores Detection of pathways and buffering using quantitative data

37. Extracting BPMs from quantitative data Next challenge: simultaneously extraction of multiple pathways The output includes: Pathway boundaries Pathway boundaries Pairs of buffering pathways Pairs of buffering pathways

38. Extracting BPMs from quantitative data

39. Summary Computational analysis outlines pathways and buffering pathway pairs - BPMs BPM pathways tend to be strictly regulated BPM pivots correspond to essential multifunctional proteins Quantitative GIs carry the promise of a reacher analysis