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? The internal cometition problem?

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???

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

Eigen’s Paradox and the Error threshold N length s superiority of the master q error rate per digit

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

Simplified error threshold x + y = 1

Error theshold and error catastrophe

Error threshold and extinction threshold

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)

Nature 420, (2002). Replicase RNA Other RNA

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

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

Evolving population Error rate Replicase activity

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

RNA structure and the error theshold: Kun, Santos, Szathmáry (2005) Nature Genetics 37, The 3D shape of the molecule Enzymatic activity depends on the structure Phenotype of a ribozyme is the structure There are fewer structures than sequences A few mutations in the sequence usually do not change the structure The 2D structure can be computed easily

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

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)

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

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

General observations on ribozymes 1.Structure is important, individual base pairs are not 2.Structure can be slightly varied 3.There are critical sites 4.The landscape is multiplicative (there might be a slight synergy)

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

Phenotypic error threshold  * =  * = VS RibozymeHairpin

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

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

Error rates and the origin of replicators

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