From genotypes to organisms: State-of-the-art and perspectives of a cornerstone in evolutionary dynamics

Manrubia, Susanna C.; Cuesta, José A.; Aguirre, Jacobo; Ahnert, Sebastian E.; Altenberg, Lee; Cano, Alejandro V.; Catalán, Pablo; Dı́az-Uriarte, Ramón; Elena, Santiago F.; García-Martín, Juan Antonio; Hogeweg, Paulien; Khatri, Bhavin S.; Krug, Joachim; Louis, Ard A.; Martin, Nora S; Payne, Joshua L.; Tarnowski, Matthew J.; Weiß, Marcel

doi:10.1016/j.plrev.2021.03.004

Cited by 71 publications

(111 citation statements)

References 364 publications

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“…Many other GP maps also show strong phenotype bias ( Dingle et al 2018 ; Manrubia et al 2021 ). An important question for future work will be whether there is a universal structure to this phenotype bias that holds more widely and whether it also has such a clear effect on evolutionary outcomes in other biological systems.…”

Section: Discussionmentioning

confidence: 99%

“…One way to make progress on these big questions in evolutionary theory is to study genotype–phenotype (GP) maps that are tractable enough to provide access to the full spectrum of possible variation ( Ahnert 2017 ; Manrubia et al 2021 ) so that counterfactuals ( Louis 2016 ) can be explored. In this article, we follow this strategy, employing the well-known GP mapping from RNA sequences to secondary structures (SS), to explain in detail how noncoding RNA (ncRNA) found in nature populates the morphospace of all possible RNA SS shapes.…”

Section: Introductionmentioning

confidence: 99%

“…An important driver of the growing interest in GP maps is that they allow us to open up the black box of variation—to explain, via a stripped-down version of the process of development, how changes in genotypes are translated into changes in phenotypes. Unfortunately, it remains much harder to establish how the patterns typically observed in studies of GP maps ( Ahnert 2017 ; Manrubia et al 2021 ) translate into evolutionary outcomes, because natural selection must then also be taken into account. For GP maps, this means attaching fitness values to phenotypes which is difficult because fitness is hard to measure and is of course dependent on the environment, and so fluctuates.…”

Section: Introductionmentioning

confidence: 99%

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Phenotype Bias Determines How Natural RNA Structures Occupy the Morphospace of All Possible Shapes

Dingle

Ghaddar

Šulc

et al. 2021

Molecular Biology and Evolution

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Morphospaces –representations of phenotypic characteristics– are often populated unevenly, leaving large parts unoccupied. Such patterns are typically ascribed to contingency, or else to natural selection disfavouring certain parts of the morphospace. The extent to which developmental bias, the tendency of certain phenotypes to preferentially appear as potential variation, also explains these patterns is hotly debated. Here we demonstrate quantitatively that developmental bias is the primary explanation for the occupation of the morphospace of RNA secondary structure (SS) shapes. Upon random mutations, some RNA SS shapes (the frequent ones) are much more likely to appear than others. By using the RNAshapes method to define coarse-grained SS classes, we can directly compare the frequencies that non-coding RNA SS shapes appear in the RNAcentral database to frequencies obtained upon random sampling of sequences. We show that: a) Only the most frequent structures appear in nature; the vast majority of possible structures in the morphospace have not yet been explored. b) Remarkably small numbers of random sequences are needed to produce all the RNA SS shapes found in nature so far. c) Perhaps most surprisingly, the natural frequencies are accurately predicted, over several orders of magnitude in variation, by the likelihood that structures appear upon uniform random sampling of sequences. The ultimate cause of these patterns is not natural selection, but rather strong phenotype bias in the RNA genotype-phenotype map, a type of developmental bias or “findability constraint”, which limits evolutionary dynamics to a hugely reduced subset of structures that are easy to “find”.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Phenotype Bias Determines How Natural RNA Structures Occupy the Morphospace of All Possible Shapes

Dingle

Ghaddar

Šulc

et al. 2021

Molecular Biology and Evolution

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“…Therefore, our neutral sets are sets of L = 30 sequences which share the same structure p , i.e. the established definition of neutral sets [ 1 ]. Once the neutral set is defined, we can compute phenotype robustness and mutation probabilities as averages over the neutral set (as shown in figure 1 ).…”

Section: Methodsmentioning

confidence: 99%

“…The genotype–phenotype relationship is a ‘cornerstone’ [ 1 ] of molecular evolution because it captures the structural and functional consequences of mutations. These mutations can include substitutions as well as insertions and deletions and are the source of variation.…”

Section: Introductionmentioning

confidence: 99%

Insertions and deletions in the RNA sequence–structure map

Martin

Ahnert

2021

J. R. Soc. Interface.

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Genotype–phenotype maps link genetic changes to their fitness effect and are thus an essential component of evolutionary models. The map between RNA sequences and their secondary structures is a key example and has applications in functional RNA evolution. For this map, the structural effect of substitutions is well understood, but models usually assume a constant sequence length and do not consider insertions or deletions. Here, we expand the sequence–structure map to include single nucleotide insertions and deletions by using the RNAshapes concept. To quantify the structural effect of insertions and deletions, we generalize existing definitions for robustness and non-neutral mutation probabilities. We find striking similarities between substitutions, deletions and insertions: robustness to substitutions is correlated with robustness to insertions and, for most structures, to deletions. In addition, frequent structural changes after substitutions also tend to be common for insertions and deletions. This is consistent with the connection between energetically suboptimal folds and possible structural transitions. The similarities observed hold both for genotypic and phenotypic robustness and mutation probabilities, i.e. for individual sequences and for averages over sequences with the same structure. Our results could have implications for the rate of neutral and non-neutral evolution.

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Modeling Complex Biological Systems: Tackling the Parameter Curse Through Evolution

Hogeweg

2021

Evolutionary Systems Biology

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As we all know: "Nothing in biology makes sense except in the light of evolution" (Dobzhansky [9]). Among the challenges of modeling complex biological systems is to determine the relevant parameters. Common practice is to extract parameters from the literature, or to determine them from ongoing experiments, or by collectively fitting the parameters to the experimental results the model tries to explain. Doing so ignores, or at least does not exploit, Dobzhansky's wisdom. In this perspective paper we argue and demonstrate the importance of using evolutionary methods to derive relevant parameters. We show that by doing so we can debug experimental and modeling artifacts.

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From genotypes to organisms: State-of-the-art and perspectives of a cornerstone in evolutionary dynamics

Cited by 71 publications

References 364 publications

Phenotype Bias Determines How Natural RNA Structures Occupy the Morphospace of All Possible Shapes

Phenotype Bias Determines How Natural RNA Structures Occupy the Morphospace of All Possible Shapes

Insertions and deletions in the RNA sequence–structure map

Modeling Complex Biological Systems: Tackling the Parameter Curse Through Evolution

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