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The error threshold or ribo-organisms

The error threshold or ribo-organisms. Eörs Szathmáry. Collegium Budapest AND Eötvös University. Crucial assumptions. There was in fact an RNA-dominated worlds RNAs acted as genes and as ribozymes Replication as a problem was solved The accuracy problem?

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The error threshold or ribo-organisms

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  1. The error threshold or ribo-organisms Eörs Szathmáry Collegium Budapest ANDEötvös University

  2. Crucial assumptions • There was in fact an RNA-dominated worlds • RNAs acted as genes and as ribozymes • Replication as a problem was solved • The accuracy problem? • The internal cometition problem?

  3. Inaccurate replication immediately raises further concerns (Eigen, 971) • Early replication must have been error-prone • Error threshold sets the limit of maximal genome size to <100 nucleotides • Not enough for several genes • Unlinked genes will compete • Genome collapses • Resolution???

  4. An example of “replication” RNA RNA RNA RNA RNA RGA RNA RNA RNA RNX RNA RNA RNH DNM RNA RNA RNA RQA RNA RNJ RPA WORLD WORLF WORLD WORLL IDRYD WORLD WORLD KORLD WORLD WORLD WORLD WORLD WORUD WORLD WORHD WORLD WORLD WORWD WORLD WORLD WRRLD HYPOTHESIS EYPKTHYSII HYPEXHESIS HYPOTHESIS HYPOTHESIS HYPETHESKS HYYOTHESIS HYPOTHESIS HYPOTHESIS HYPOTHESIS HYPOTHESIS HYPOTHESIS HYPOSHESIS HYPOTMESIS HTPOTHESIS CYPOTGESIS HYPOTHEGIA HYPOXHLSIS HYPXTHESIS HYPOTHESIS HYPUTHESIS

  5. Eigen’s Paradox and theError threshold N length ssuperiority of the master q error rate per digit

  6. Quasispecies made simple • For didactics, there are only two genotypes • Only forward mutations • Fitness values and mutations rates

  7. Simplified error threshold x + y = 1

  8. Error theshold and error catastrophe

  9. Error threshold and extinction threshold

  10. Population dynamics on surfaces • Reaction-diffusion on the surface (following Hogeweg and Boerlijst, 1991) • One tends to interact with one’s neighbours • This is important, because lesson from theoretical ecology indicates that such conditions promote coexistence of competitors • Important effect on the dynamics of the primordial genome (cf. Eigen’s paradox)

  11. Nature420, 360-363 (2002). Replicase RNA Other RNA

  12. Elements of the model • A cellular automaton model simulating replication and dispersal in 2D • Replication needs a template next door • Replication probability proportional to rate constant (allowing for replication) • Diffusion

  13. Maximum as a function of molecule length • Target and replicase efficiency • Copying fidelity • Trade-off among all three traits: worst case

  14. Evolving population Error rate Replicase activity

  15. SCM is better than HPC at high mutation rates (Zintzaras, Santos, Szathmáry, J. theor. Biol. 2002) • Survival of the flattest • SCM is better only at high mutations rates • Exactly relevant for early systems

  16. RNA structure and the error theshold: Kun, Santos, Szathmáry (2005) Nature Genetics37, 1008-1011. • The 3D shape of the molecule • Enzymatic activity depends on the structure • Phenotype of a ribozyme is the structure • There are fewer structuresthan sequences • A few mutations in the sequence usually do not change the structure • The 2D structure can be computed easily

  17. RNA structure – an example Master copy: Same structure Same fitness AUCGUCUGUCGGCGAU Mutant: GCAUGACUCAUUAUGC (different text can have the same meaning)

  18. Aim / question • The phenotype is more easily maintained than the genotype. • Phenotypic error threshold, which is higher than the genotypic error threshold. • For estimating the error threshold a fitness landscape is needed • The proposed fitness landscapes will be based on mutagenesis experimental data • Enzymatic activity will be used as a proxy for fitness (protocell)

  19. Neurospora Varkund Satellite Ribozyme N = 144 83/144 (57%) of the positions were mutated, we used 183 mutants

  20. Hairpin Ribozyme N = 50 39/50 (78%) of the positions were mutated, we used 142 mutants

  21. General observations on ribozymes • Structure is important, individual base pairs are not • Structure can be slightly varied • There are critical sites • The landscape is multiplicative (there might be a slight synergy)

  22. RNA Population dynamics • Replication rate is proportional to fitness • Copying is error-prone, but length does not change • Degradation is independent of fitness

  23. Phenotypic error threshold VS Ribozyme Hairpin m* = 0.146 m* = 0.053

  24. Comparison with other types of landscapes Mnt. Fuji type of landscape • No structure • Activities based on point mutations Single peak fitness landscape • Based on average activity of point mutants

  25. Neutral mutions tame the error threshold • Extrapolation from the available mutants as samples to the whole fitness landscape • Accuracy of viral RNA polymerases would be sufficient to run the genome of a ribo-organism of about 70 genes

  26. Error rates and the origin of replicators

  27. Some open questions • The maximum genome size of the stochastic corrector model (how many genes in the bag?) • The evolution of genome size through duplication and divergence of metabolic enzyme functions • The origin of chromosomes

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