Predicting mutational routes to new adaptive phenotypes

Lind, Peter A.; Libby, Eric; Herzog, Jenny; Rainey, Paul B.

doi:10.7554/elife.38822

Cited by 62 publications

(129 citation statements)

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“…Wsp will account for about 54%, Aws 30% and Mws 16% of the WS mutations 339 ( Figure 2E) (Lind, et al 2019). 340 341 When the three common pathways are deleted WS types evolve mainly by mutations 342 in PFLU0085, which contains an intragenic negative regulator region (Lind, et al 343 2015), and this is expected to be the fourth most common pathways when present.…”

Section: Prediction Of Pathways Used 328mentioning

confidence: 99%

“…with relative few mutations for each gene. This is supported by the relatively small 414 number of WS mutants in SBW25 that have been characterized with sensitive fitness 415 assays and where mutations in the same gene typically have similar fitness effects 416 (Lind, et al 2015;Lind, et al 2019). If this assumption is true the distribution of 417 beneficial fitness effects is not continuous and the most advantageous mutations are 418 not predicted to be equally distributed between pathways or genes.…”

Section: Prediction Of Specific Mutational Targets and Effects Of Mutmentioning

confidence: 99%

“…Mws account for >98% of WS mutations and based on a detailed understanding of the 333 molecular functions of the genes involved of each pathways mathematical models 334 predicting at which relative rates the pathways should be used were constructed 335 ( Figure 2E) (Lind, et al 2019). The prediction results varies depending on the rates of 336 disabling and enabling mutations, but if it is assumed that disabling mutations are an 337 order of magnitude more common than enabling mutations the models predict that 338…”

Section: Prediction Of Pathways Used 328mentioning

confidence: 99%

“…In addition to predicting the relative rates of the three main pathways, the previously 359 described mathematical model can also predict which proteins are likely to be 360 mutated (Lind, et al 2019). High rates of WS mutations are predicted for WspF, 361…”

Section: Prediction Of Mutated Genes 358mentioning

confidence: 99%

“…The genetic underpinnings of several adaptive phenotypes have been 218 elucidated providing a mechanistic understanding of the effects of mutations and why 219 they are adaptive. (E) The three main pathways (Wsp, Aws, Mws) to WS are 220 particularly well-understood allowing formulation of mathematical models of the 221 molecular networks and prediction of the relative rates of use of the different 222 pathways and genes (Lind, et al 2019). 223…”

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Forecasting of phenotypic and genetic outcomes of experimental evolution inPseudomonas protegens

Pentz

Lind

2018

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25Experimental evolution is often highly repeatable, but the underlying causes are 26 generally unknown, which prevents extension of evolutionary forecasts to related 27 species. Data on adaptive phenotypes, mutation rates and targets from the 28 Pseudomonas fluorescens SBW25 Wrinkly Spreader system combined with 29 mathematical models of the genotype-to-phenotype map allowed evolutionary 30 forecasts to be made for several related Pseudomonas species. Predicted outcomes of 31 experimental evolution in terms of phenotype, types of mutations, relative rates of 32 pathways and mutational targets were then tested in Pseudomonas protegens Pf-5. As 33 predicted, most mutations were found in three specific regulatory pathways resulting 34 in increased production of Pel exopolysaccharide. Mutations were, as predicted, 35 mainly found to disrupt negative regulation with a smaller number in upstream 36 promoter regions. Mutated regions in proteins could also be predicted, but most 37 mutations were not identical to those previously found. This study demonstrates the 38 potential of short-term evolutionary forecasting in experimental populations. 39 40 variation. Natural selection, genetic architecture and mutational biases can both 65 increase and decrease the predictability of evolution depending on if they can be 66 recognized beforehand and included into evolutionary forecasting models. (A) 67Fitness. Mutants that increase to high frequencies in the population are all expected to 68 have increased fitness as drift is negligible at population sizes typically used for 69 adaptation experiments with microbes. Much effort has been put into characterizing 70 the distribution of fitness effects of beneficial mutations, but the shapes of the 71 distribution and magnitudes of the fittest mutations appear to be highly context 72 dependent. Many different phenotypes are typically adaptive during experimental 73 evolution, but in most cases they are not known beforehand and their relative fitness 74 cannot be predicted. Relative fitness is, in most cases, also expected to be highly 75 dependent on external environment including the frequency of other adaptive mutants, 76 which means that even small changes to experimental protocols can lead to 77 differences in outcomes. (B) Phenotype space. Each of the adaptive phenotypes can 78 usually be realized by mutations in different positions and in different genes, but 79 distinct phenotypes are expected to have similar fitness regardless of genetic 80 foundations, which can simplify predictions. Depending on the genetic architecture 81 underlying each trait, which is often unknown, adaptive phenotypes are produced at 82 different rates. (C) Parameter space. The adaptive phenotypes are caused by changes 83 in the molecular networks of cells, which are also influenced by the external 84 environment. If the wiring of a molecular network underpinning an adaptive 85 phenotype is well understood, parameterization of the system is possible and 86 predictive models can be formulated. Mutations...

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Section: Prediction Of Pathways Used 328mentioning

confidence: 99%

Section: Prediction Of Specific Mutational Targets and Effects Of Mutmentioning

confidence: 99%

Section: Prediction Of Pathways Used 328mentioning

confidence: 99%

Section: Prediction Of Mutated Genes 358mentioning

confidence: 99%

mentioning

confidence: 99%

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Forecasting of phenotypic and genetic outcomes of experimental evolution inPseudomonas protegens

Pentz

Lind

2018

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Repeatability and Predictability in Experimental Evolution

Lind

2019

Evolution, Origin of Life, Concepts and Methods

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Emerging Frontiers in the Study of Molecular Evolution

et al. 2020

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A collection of the editors of Journal of Molecular Evolution have gotten together to pose a set of key challenges and future directions for the field of molecular evolution. Topics include challenges and new directions in prebiotic chemistry and the RNA world, reconstruction of early cellular genomes and proteins, macromolecular and functional evolution, evolutionary cell biology, genome evolution, molecular evolutionary ecology, viral phylodynamics, theoretical population genomics, somatic cell molecular evolution, and directed evolution. While our effort is not meant to be exhaustive, it reflects research questions and problems in the field of molecular evolution that are exciting to our editors.

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Predicting mutational routes to new adaptive phenotypes

Cited by 62 publications

References 121 publications

Forecasting of phenotypic and genetic outcomes of experimental evolution inPseudomonas protegens

Forecasting of phenotypic and genetic outcomes of experimental evolution inPseudomonas protegens

Repeatability and Predictability in Experimental Evolution

Emerging Frontiers in the Study of Molecular Evolution

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