Literature DB >> 26640651

The structure of the genotype-phenotype map strongly constrains the evolution of non-coding RNA.

Kamaludin Dingle1, Steffen Schaper2, Ard A Louis2.   

Abstract

The prevalence of neutral mutations implies that biological systems typically have many more genotypes than phenotypes. But, can the way that genotypes are distributed over phenotypes determine evolutionary outcomes? Answering such questions is difficult, in part because the number of genotypes can be hyper-astronomically large. By solving the genotype-phenotype (GP) map for RNA secondary structure (SS) for systems up to length L = 126 nucleotides (where the set of all possible RNA strands would weigh more than the mass of the visible universe), we show that the GP map strongly constrains the evolution of non-coding RNA (ncRNA). Simple random sampling over genotypes predicts the distribution of properties such as the mutational robustness or the number of stems per SS found in naturally occurring ncRNA with surprising accuracy. Because we ignore natural selection, this strikingly close correspondence with the mapping suggests that structures allowing for functionality are easily discovered, despite the enormous size of the genetic spaces. The mapping is extremely biased: the majority of genotypes map to an exponentially small portion of the morphospace of all biophysically possible structures. Such strong constraints provide a non-adaptive explanation for the convergent evolution of structures such as the hammerhead ribozyme. These results present a particularly clear example of bias in the arrival of variation strongly shaping evolutionary outcomes and may be relevant to Mayr's distinction between proximate and ultimate causes in evolutionary biology.

Entities:  

Keywords:  arrival of variation; bias in development; genotype–phenotype map; robustness

Year:  2015        PMID: 26640651      PMCID: PMC4633861          DOI: 10.1098/rsfs.2015.0053

Source DB:  PubMed          Journal:  Interface Focus        ISSN: 2042-8898            Impact factor:   3.906


  36 in total

1.  Bias in the introduction of variation as an orienting factor in evolution.

Authors:  L Y Yampolsky; A Stoltzfus
Journal:  Evol Dev       Date:  2001 Mar-Apr       Impact factor: 1.930

Review 2.  The ubiquitous hammerhead ribozyme.

Authors:  Christian Hammann; Andrej Luptak; Jonathan Perreault; Marcos de la Peña
Journal:  RNA       Date:  2012-03-27       Impact factor: 4.942

3.  Compact and ordered collapse of randomly generated RNA sequences.

Authors:  Erik A Schultes; Alexander Spasic; Udayan Mohanty; David P Bartel
Journal:  Nat Struct Mol Biol       Date:  2005-11-06       Impact factor: 15.369

Review 4.  Topological constraints: using RNA secondary structure to model 3D conformation, folding pathways, and dynamic adaptation.

Authors:  Maximillian H Bailor; Anthony M Mustoe; Charles L Brooks; Hashim M Al-Hashimi
Journal:  Curr Opin Struct Biol       Date:  2011-04-14       Impact factor: 6.809

Review 5.  Convergence, adaptation, and constraint.

Authors:  Jonathan B Losos
Journal:  Evolution       Date:  2011-04-07       Impact factor: 3.694

6.  Natural selection and the concept of a protein space.

Authors:  J M Smith
Journal:  Nature       Date:  1970-02-07       Impact factor: 49.962

7.  The spandrels of San Marco and the Panglossian paradigm: a critique of the adaptationist programme.

Authors:  S J Gould; R C Lewontin
Journal:  Proc R Soc Lond B Biol Sci       Date:  1979-09-21

8.  The arrival of the frequent: how bias in genotype-phenotype maps can steer populations to local optima.

Authors:  Steffen Schaper; Ard A Louis
Journal:  PLoS One       Date:  2014-02-05       Impact factor: 3.240

9.  Neutral network sizes of biological RNA molecules can be computed and are not atypically small.

Authors:  Thomas Jörg; Olivier C Martin; Andreas Wagner
Journal:  BMC Bioinformatics       Date:  2008-10-30       Impact factor: 3.169

10.  A comprehensive comparison of comparative RNA structure prediction approaches.

Authors:  Paul P Gardner; Robert Giegerich
Journal:  BMC Bioinformatics       Date:  2004-09-30       Impact factor: 3.169

View more
  23 in total

1.  Distribution of genotype network sizes in sequence-to-structure genotype-phenotype maps.

Authors:  Susanna Manrubia; José A Cuesta
Journal:  J R Soc Interface       Date:  2017-04       Impact factor: 4.118

Review 2.  Developmental Bias and Evolution: A Regulatory Network Perspective.

Authors:  Tobias Uller; Armin P Moczek; Richard A Watson; Paul M Brakefield; Kevin N Laland
Journal:  Genetics       Date:  2018-08       Impact factor: 4.562

3.  Neutral components show a hierarchical community structure in the genotype-phenotype map of RNA secondary structure.

Authors:  Marcel Weiß; Sebastian E Ahnert
Journal:  J R Soc Interface       Date:  2020-10-21       Impact factor: 4.118

4.  Adding levels of complexity enhances robustness and evolvability in a multilevel genotype-phenotype map.

Authors:  Pablo Catalán; Andreas Wagner; Susanna Manrubia; José A Cuesta
Journal:  J R Soc Interface       Date:  2018-01       Impact factor: 4.118

5.  Phenotypes can be robust and evolvable if mutations have non-local effects on sequence constraints.

Authors:  Marcel Weiß; Sebastian E Ahnert
Journal:  J R Soc Interface       Date:  2018-01       Impact factor: 4.118

Review 6.  Structural properties of genotype-phenotype maps.

Authors:  S E Ahnert
Journal:  J R Soc Interface       Date:  2017-07       Impact factor: 4.118

7.  Using small samples to estimate neutral component size and robustness in the genotype-phenotype map of RNA secondary structure.

Authors:  Marcel Weiß; Sebastian E Ahnert
Journal:  J R Soc Interface       Date:  2020-05-20       Impact factor: 4.118

8.  Populations of genetic circuits are unable to find the fittest solution in a multilevel genotype-phenotype map.

Authors:  Pablo Catalán; Susanna Manrubia; José A Cuesta
Journal:  J R Soc Interface       Date:  2020-06-03       Impact factor: 4.118

9.  Evolution towards increasing complexity through functional diversification in a protocell model of the RNA world.

Authors:  Suvam Roy; Supratim Sengupta
Journal:  Proc Biol Sci       Date:  2021-11-17       Impact factor: 5.349

10.  Genetic Correlations Greatly Increase Mutational Robustness and Can Both Reduce and Enhance Evolvability.

Authors:  Sam F Greenbury; Steffen Schaper; Sebastian E Ahnert; Ard A Louis
Journal:  PLoS Comput Biol       Date:  2016-03-03       Impact factor: 4.475

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.