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Topological and thermodynamic factors that influence the evolution of small networks of catalytic RNA species
An RNA-directed recombination reaction can result in a network of interacting RNA species. It is now becoming increasingly apparent that such networks could have been an important feature of the RNA world during the nascent evolution of life on the Earth. However, the means by which such small RNA n...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory Press
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5473143/ https://www.ncbi.nlm.nih.gov/pubmed/28389432 http://dx.doi.org/10.1261/rna.061093.117 |
Sumario: | An RNA-directed recombination reaction can result in a network of interacting RNA species. It is now becoming increasingly apparent that such networks could have been an important feature of the RNA world during the nascent evolution of life on the Earth. However, the means by which such small RNA networks assimilate other available genotypes in the environment to grow and evolve into the more complex networks that are thought to have existed in the prebiotic milieu are not known. Here, we used the ability of fragments of the Azoarcus group I intron ribozyme to covalently self-assemble via genotype-selfish and genotype-cooperative interactions into full-length ribozymes to investigate the dynamics of small (three- and four-membered) networks. We focused on the influence of a three-membered core network on the incorporation of additional nodes, and on the degree and direction of connectivity as single new nodes are added to this core. We confirmed experimentally the predictions that additional links to a core should enhance overall network growth rates, but that the directionality of the link (a “giver” or a “receiver”) impacts the growth of the core itself. Additionally, we used a simple mathematical model based on the first-order effects of lower-level interactions to predict the growth of more complex networks, and find that such a model can, to a first approximation, predict the ordinal rankings of nodes once a steady-state distribution has been reached. |
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