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Learning from perturbations of biological systems

CSCI5461: Functional Genomics, Systems Biology and Bioinformatics. Learning from perturbations of biological systems. Department of Computer Science and Engineering University of Minnesota. Announcements. HW #3 will be assigned by tomorrow Paper discussions next week:. Outline for today.

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Learning from perturbations of biological systems

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  1. CSCI5461: Functional Genomics, Systems Biology and Bioinformatics Learning from perturbations of biological systems Department of Computer Science and Engineering University of Minnesota

  2. Announcements HW #3 will be assigned by tomorrow Paper discussions next week:

  3. Outline for today Costanzo et al. The Genetic Landscape of a Cell. Science 2010, 327(5964):425-431. For more details, see: Baryshnikova et al. Quantitative analysis of fitness and genetic interactions in yeast on a genome scale.Nat Methods2010. Bellay et al. Putting genetic interactions in context through a global modular decomposition.Genome Research 2011 • Combinatorial perturbations (genetic interactions) and methods for analyzing them • Case study:

  4. { ~80% can be independently deleted with no severe fitness defect! How much can we learn from single perturbations? Fitness of yeast deletion mutants (~6000 different gene-level perturbations) 1800 1600 1400 1200 1000 800 600 400 200 0 # of deletion mutants Fitness of single mutant (relative to wild-type cell) Winzeler EA et al: Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science 1999, 285(5429):901-906.

  5. One interesting outcome of combining gene deletions A A a a X X X X b B B b Viable Wild-type Viable Lethal Baker’s yeast (Saccharomyces cerevisiae) A B Essential function “synthetic lethality” Or, more generally, “genetic interaction”

  6. Example 1: Measuring epistasis from quantitative fitness measures(1-dimensional phenotype) Null model: Circuit analogy a b Pconnection = Pa * Pb

  7. Synthetic Genetic Array (SGA) Analysis Tong, A.H.Y. et al. Systematic genetic analysis with ordered arrays of yeast deletion mutants. Science294, 2364-2368 (2001).

  8. Robots to the rescue! Sasan Raghibizadeh Boone/Andrews lab (U. Toronto)

  9. Our goal: measure reliable, quantitative genetic interactions from double mutant colonies Synthetic Genetic Array Colony growth model (1536 colonies, 308 different double mutants)

  10. Types of genetic interactions: Negative Fitness wt 1 a 0.5 A B b 0.5 ab Essential function “Negative” Synthetic Lethal Example: Parallel pathways “Neutral” Expected Result

  11. Types of genetic interactions: Positive Fitness 1 A wt 1 B C A Protein complex 0.7 a B C 0.5 Non-functional A Non-functional b 0.5 C B ab C 0.5 Non-functional Essential function “Neutral” Expected Result “Positive” (Co-equal) e.g. two genes whose products are not functionally independent

  12. Types of genetic interactions: Positive Fitness wt 1 a 0.3 b 0.8 ab 0.8 “Positive” (Suppression) e.g. Mutant b suppresses growth defect of mutant a 0.24 = “Neutral” Expected Result

  13. Current progress toward mapping interactions for the whole genome ~4000 Genes  fij -fifj ~1700 Genes  • 1721 queries x 3885 arrays • ~5.4 million gene pairs (~30% of all possible gene-gene combinations) (Costanzo et al., 2010)

  14. Global distribution of interactions High-confidence (> 60% precision): Neg: ~110k, Pos: ~60k

  15. Genetic interaction degree distribution Genetic interaction hubs Top 10% hubs connect ~40% of the interactions (Costanzo et al., 2010)

  16. Properties of genetic interaction network hubs Red: negative GI Green: positive GI Black: PPI (Costanzo et al., 2010)

  17. Interaction Frequency Varies Across Biological Processes Background “Notably, all six of the hub genes in our network encode components of chromatin-modifying complexes…” (Costanzo et al., 2010) Nature Genetics38, 896 - 903 (2006)

  18. Interaction profiles are predictive of gene function High correlation Low correlation High correlation Constructing a genetic interaction similarity network Edge-weighted spring-embedded layout 0.8 0.8 0.8 0.3 0.78 0.9 0.7 0.25 0.75 0.8 0.9

  19. Similar genetic interaction profiles reveal global functional map (connection  similar interaction profile)

  20. Similar genetic interaction profiles reveal global functional map DNA replication

  21. Similar genetic interaction profiles reveal global functional map Vesicle-mediated transport DNA replication

  22. Similar genetic interaction profiles reveal global functional map RNA processing Chromatin & transcription Ribosome, Translation Nuclear-cytoplasmic transport Mitochondria Nuclear migration Protein Degradation Peroxisomes Vesicle-mediated transport Chromosome segregation and mitosis Amino acid biosynthesis DNA replication and repair Glycosylation & cell wall Polarity & cell morphogenesis

  23. Yeast genetic interaction network

  24. Yeast genetic interaction network Chromatin remodeling RNA Pol II RNA splicing Tubulin folding Dynein- dynactin DNA recomb. and repair RNA decay Nuclear pore DNA replication Vacuole sorting Spindle assembly Cohesion and Kinetochore Actin cytoskeleton APC complex Budding, cytokenesis Proteosome

  25. Yeast genetic interaction network Cell polarity & morphogenesis Rim101, MVB pathways Endosome, Vacuole sorting tRNA modification Autophagy AP-1 clathrin Adaptor complex Cell wall biosynth. & integrity Glycosylation, Protein folding Amino acid biosynthesis AP-3 clathrin Adaptor complex OCA Signaling complex ERAD V-ATPase complex ER-Golgi traffic

  26. Gap1 permease sorting network

  27. ecm30D, ubp15D and par32D mutants exhibit Gap1 sorting defects Gap1-GFP DIC Wild-type 150 125 gtr1D 100 Gap1 activity (% of WT) 75 ecm30D 50 25 par32D 0 Wild-type gtr1D ecm30D par32D ubp15D ubp15D Eric Spear & Chris Kaiser

  28. “Matrix” view of the global yeast genetic interaction network ~ 1700 x ~4000 genes (~6 million) pairs Query genes negative positive Array genes

  29. RAD55 RAD57 RAD51 RAD54 RAD52 RAD55 RAD57 RAD51 RAD54 RAD52 REV7 REV1 REV3 RAD55 RAD57 RAD51 RAD54 RAD52 What kind of local network structure leads to highly correlated interaction profiles? Positive Negative Interaction matrix

  30. RAD55 RAD57 RAD51 RAD54 RAD52 RAD55 RAD57 RAD51 RAD54 RAD52 REV7 REV1 REV3 RAD55 RAD57 RAD51 RAD54 RAD52 Local network structure Essential function (DNA repair) • Network structures reveal: • gene modules • redundancy often occurs at the module (not gene) level

  31. REV7 REV1 REV3 RAD55 RAD57 RAD51 RAD54 RAD52 GI Network structure mining • Can we find ALL structures in the network that look like this?

  32. Crash course in association analysis For reference: Introduction to Data Mining. Tan, Steinbach, and Kumar. Addison-Wesley, 2005. Apriori algorithm: Agrawal and Srikant. Fast algorithms for mining association rules in large databases. Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, pg.487-499, Santiago, Chile, September 1994.

  33. Crash course in association rule mining For reference: Introduction to Data Mining. Tan, Steinbach, and Kumar. Addison-Wesley, 2005. Apriori algorithm: Agrawal and Srikant. Fast algorithms for mining association rules in large databases. Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, pg.487-499, Santiago, Chile, September 1994.

  34. Enabling exhaustive combinatorial pattern search: smart pruning of patterns For reference: Introduction to Data Mining. Tan, Steinbach, and Kumar. Addison-Wesley, 2005. Apriori algorithm: Agrawal and Srikant. Fast algorithms for mining association rules in large databases. Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, pg.487-499, Santiago, Chile, September 1994.

  35. Enabling exhaustive combinatorial pattern search: smart pruning of patterns For reference: Introduction to Data Mining. Tan, Steinbach, and Kumar. Addison-Wesley, 2005. Apriori algorithm: Agrawal and Srikant. Fast algorithms for mining association rules in large databases. Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, pg.487-499, Santiago, Chile, September 1994.

  36. Block structure mining results Real network: 10,459 blocks Randomized network: ~20 blocks We estimate that ~70-80% of negative interactions occur in blocks vs. Jeremy Bellay, Gowtham Atluri, Gaurav Pandey, Vipin Kumar

  37. Block structures reveal a surprising degree of multi-functionality among yeast genes Example: biclusters for VIP1 VIP1: inositol pyrophoshate synthase

  38. Block structures reveal a surprising degree of multi-functionality among yeast genes VIP1 VIP1

  39. VIP1 confirmed role in DNA repair Spot Assay FACS Analysis Tina Sing Grant Brown

  40. Reminder about suppression positive interactions Fitness wt 1 a 0.3 b 0.8 ab 0.8 “Positive” (Suppression) e.g. Mutant b suppresses growth defect of mutant a 0.24 = “Neutral” Expected Result

  41. Global map of cross-complex suppression interactions Baryshnikova et al., Nature Methods 2010 Bellay et al., Genome Research 2011

  42. Far3-11 complex suppresses actin polarization defects associated with TORC2 mutants Baryshnikova et al., Nature Methods 2010 Bellay et al., Genome Research 2011

  43. Exciting directions in the area of genetic interaction networks • High-throughput genetic interaction technology in higher eukaryotes • Genetic interactions and cancer • Chemical genetics applications • Higher order combinations of mutations (triples, quadruples, ...) • Linking perturbation studies in model organisms with genotypic/phenotypic variation in populations

  44. Genetic interaction maps in other species S. cerevisiae S. pombe common ancestor ~400 million years ago Open question: do the rules governing GI network topology generalize beyond S. cerevisiae?

  45. How are GIs relevant to understanding/treating cancer? Assume: (1) human gene A, gene B have a genetic interaction (synthetic lethal) (2) mutations in A are associated with cancer (e.g. BRCA1) Tumor cell AB A Cell dies + B Normal cell (design drug to inhibit protein B) AB A Cell lives Message: Genetic interactions point towards cancer-specific vulnerabilities!

  46. How are GIs relevant to understanding/treating cancer? • Success story: • BRCA1 mutant cells are completely dependent on PARP1 (poly polymerase) for survival • PARP1 is not essential for normal cell function • Clinical trials for PARP1 inhibitors in progress (mixed results) Nature434, 917-921 (14 April 2005)

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