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ZENODO
Dataset . 2019
License: CC 0
Data sources: ZENODO
DRYAD
Dataset . 2019
License: CC 0
Data sources: Datacite
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Data from: Destabilizing mutations encode nongenetic variation that drives evolutionary innovation

Authors: Petrie, Katherine L.; Palmer, Nathan D.; Johnson, Daniel T.; Medina, Sarah J.; Yan, Stephanie J.; Li, Victor; Burmeister, Alita R.; +1 Authors

Data from: Destabilizing mutations encode nongenetic variation that drives evolutionary innovation

Abstract

Petrie Science 2018 Fig 1B and S1Data collected for results shown in figure 1B and S1, see notes within the file and the manuscript for further guidanceDryad Petrie Science 2018 Fig 1B and S1.csvPetrie Science 2018 Fig 1C and S2Data collected for results shown in figures 1C and S2, see notes within the file and the manuscript for further guidance.Petrie Science Fig 1C and S2.csvPetrie Science 2018 sequence of 3-mut replicate 1Sequence of engineered 3-mut J gene replicate 1 (see table S1 for additional guidance).3-Mut_4-Jrev.seqPetrie 2018 Science sequence of 3-mut replicate 2.Sequence of engineered 3-mut J gene replicate 2 (see table S1 for additional guidance).3-Mut_7-Jrev.seqPetrie Science 2018 sequence of 4-mut replicate 1Sequence of engineered 4-mut J gene replicate 1 (see table S1 for additional guidance).4-MUT (P5_03-JRev).seqPetrie Science 2018 sequence of 4-mut replicate 2Sequence of engineered 4-mut J gene replicate 2 (see table S1 for additional guidance).4-MUT (P5_04-JRev).seqPetrie Science 2018 sequence of 5-mut replicate 1Sequence of engineered 5-mut J gene replicate 1 (see table S1 for additional guidance).5-MUT (Mage01_1-JRev).seqPetrie Science 2018 sequence of 5-mut replicate 2Sequence of engineered 5-mut J gene replicate 2 (see table S1 for additional guidance).5-MUT (Mage01_2-JRev).seqPetrie Science 2018 Sequence of engineered 7-mut lyso.Sequence of engineered 7-mut lyso J gene (see table S1 for additional guidance)31F1_1a-J_Rev.ab1-1.faPetrie Science 2018 Fig 2AData collected for results shown in figure 2A, see notes within the file and the manuscript for further guidance.Petrie Science 2018 Fig 2BData collected for results shown in figure 2B, see notes within the file and the manuscript for further guidance.Petrie Science 2018 Fig 2CData collected for results shown in figure 2C, see notes within the file and the manuscript for further guidance.Petrie Science 2018 Fig 3A and 3BData collected for results shown in figures 3A and 3B, see notes within the file and the manuscript for further guidance.Petrie Science 2018 Fig 3CData collected for results shown in figure 3C, see notes within the file and the manuscript for further guidance.Petrie Science Fig 3DData collected for results shown in figure 3C, see notes within the file and the manuscript for further guidance.Petrie Science 2018 Fig 3D.csvPetrie Science 2018 Fig S4A and S4BData collected for results shown in figure S4A and S4B, see notes within the file and the manuscript for further guidance.Petrie Science 2018 Fig S5Data collected for results shown in figure S5, see notes within the file and the manuscript for further guidance.

Evolutionary innovations are often achieved by repurposing existing genes to perform new functions; however, the mechanisms enabling the transition from old to new remain controversial. We identified mutations in bacteriophage λ’s host-recognition gene J that confer enhanced adsorption to λ’s native receptor, LamB, and the ability to access a new receptor, OmpF. The mutations destabilize particles and cause conformational bistability of J, which yields progeny of multiple phenotypic forms, each proficient at different receptors. This work provides an example of how nongenetic protein variation can catalyze an evolutionary innovation. We propose that cases where a single genotype can manifest as multiple phenotypes may be more common than previously expected and offer a general mechanism for evolutionary innovation. 

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Keywords

non genetic variation, Bacteriophage Lambda, Innovation, novelty

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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