Darwinian Genomics Csaba Pal Biological Research Center Szeged, Hungary

1. Darwinian Genomics Csaba Pal Biological Research Center Szeged, Hungary

2. Genomics: Major revolution in the past 10 – 15 years with the rise of high-throughput molecular technologies: New methods for rapid and relatively cheap measurements of biological molecules on a global scale

3. Systematic mapping components, interactions and functional states of the cell Genomics: genome sequencing and annotation Transcriptomics: mRNA levels, mRNA half-lives Proteomics: protein levels, protein – protein interactions, protein modifications Metabolomics: metabolite concentrations Phenomics: creating collections of mutant strains and measuring phenotypes (e.g. cell growth) under various conditions

4. Darwinian genomics: Testing key issues in evolutionary biology • Examples: • Role of chance and necessity • Gradual changes or jumps • Extent and evolution of robustness against mutations

5. Darwinian genomics: Testing key issues in evolutionary biology • Examples: • Role of chance and necessity • Gradual changes or jumps • Extent and evolution of robustness against mutations

6. Yeast (S. cerevisiae) is an ideal model organism • Complete genome sequence/detailed biochemical studies • -> network reconstruction • 2) Genome-scale computational models • -> systems level properties of cellular networks • 3) Large-scale mutant libraries • -> test predictions of the models • 4) Complete genome sequences for ~30 closely related species • -> study evolution across species

7. The knock-out paradox High-throughput single gene knock-out studies: no phenotype for most genes in the lab

8. Why keep them during evolution? • Keep optimal cellular performance in face of harmful mutations and non-heritable errors • Allow cellular growth under wide range of external conditions

9. (Seemingly) dispensable genes.... • compensated by a gene duplicate (genetic redundancy) • compensated by alternative genetic pathways (distributed robustness) • have important functions only under specific environmental conditions Gene A Gene B Gene A Gene B

10. Redundancy is only apparent, most genes should have important contribution to survival under special environmental conditions

11. Hillenmeyer et al. Science 2008

12. Compared growth rates of ~ 5000 single gene knock-out strains under >1000 environments 97% of the mutants show slow growth under at least one condition Hillenmeyer et al. Science 2008

13. Are these explanations mutually exclusive? • compensated by a gene duplicate (genetic redundancy) • compensated by alternative genetic pathways (distributed robustness) • have important functions only under specific environmental conditions Gene A Gene B Gene A Gene B

14. Does the capacity to compensate the impact of gene deletions depend on the environment?

15. Observed gene deletion phenotypes( viable, lethal): The extent of compensation may depend on nutrient availability Environment I. Environment II. Environment III. A A A B B B A B A B A B a B a B a B A b A b A b a b a b a b synthetic lethality no interaction no interaction

16. Computational tool: Flux Balance Analysis (FBA) Amino acids Carbohydrates Ribonucleotides Deoxyribonucleotides Lipids Phospholipids Steroles Fatty acids fitness • Network reconstruction In S. cerevisiae ~1400 biochemical reactions, including transport processes. • Application of constraints Specify the nutrients available in the environment (B,E), the key metabolites or biomass constituents (X, Y, Z) essential for survival, presence/absence of genes • Find a particular enzymaticflux distribution -> rate of biomass production (fitness)

17. What are the advantages of flux balance analysis? • Study large number of genes and environments simultaneously • Predictions: • a) Changes in enzyme activity as a response to nutrient conditions and genetic deletions • b) Impact of gene deletions and gene addition on growth rates • 3) Good agreement between experimental studies and model predictions (~90%) Forster et al. 2003 OMICS, Papp et al. Nature 2004

18. Interactions between mutations in metabolic networks A special case: Synthetic lethal genetic interactions Redundant gene duplicates Gene A Gene B A B normal growth a– B A b– Gene A a– b– lethal (or sick) Gene B Alternative cellular pathways

19. ΔA B A ΔB n n sporulation A/ΔA B/ΔB 2n Model predictions and verification of genetic interactions • Using Flux Balance analysis, we simulated all possible single and double gene deletions (~125 000) in the metabolic network under 53 different nutrient conditions •  98 gene pairs are synthetic lethal under at least one condition • We performed lab experiments to validate them:

20. Results: 1) 50% of the predictions were correct (only ~ 0.6% expected by chance!) 2) 85% of the interacting gene pairs show condition-dependent synthetic lethality unconditional synthetic lethality

21. An example: Harrison et al. (2007) PNAS 104:2307-2312

22. An example: Harrison et al. (2007) PNAS 104:2307-2312

23. Conclusions • The metabolic network model can reliably predict (synthetic lethal) genetic interactions. • The presence of genetic interactions (and hence the extent of compensation) vary extensively across nutrient conditions.

24. Speculations and potential implications: • Experimental design. Different environments should be screened to identify the majority of genetic interactions • Functional genomics. Redundancy is more apparent than real. Many seemingly dispensable genes have important physiological role under specific conditions • Evolution. Robustness against mutations may not be a directly selected trait, but rather a by-product of evolution of novel metabolic pathways towards new environmental conditions

25. Shortcomings: • The computational model is far from perfect, and ignores many biological details • Only specific genetic interactions have been studied • No systematic experimental screen Harrison et al. (2007) PNAS 104:2307-2312

26. Collaboration with Charles Boone lab • Using robotic protocols, they map genetic interactions across the whole yeast genome (~107 combinations ) • They developed high-throughput protocols to measure fitness at high precision

27. Why study evolution?

28. Evolution of antibiotics resistance: 33 Billion $ annual costs in US

29. Ignoring evolution has serious health consequences

30. Evolutionary Systems Biology Group • Projects: • Analyses of genetic interactions • Evolution of antibiotics resistance http://www.brc.hu/sysbiol/

31. Interactions between genes are masked by distant gene duplicates Confirmed by creating corresponding triple knock-outs: Overlapping enzymatic activities between duplicates conserved across more than 100 million years of evolution