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Predicting Evolution of the Transcription Regulatory Network in a Bacteriophage
Author(s) -
Daniel J. Garry,
Adam J. Meyer,
Jared W. Ellefson,
James J. Bull,
Andrew D. Ellington
Publication year - 2018
Publication title -
genome biology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.702
H-Index - 74
ISSN - 1759-6653
DOI - 10.1093/gbe/evy191
Subject(s) - biology , promoter , rna polymerase , genetics , transcription (linguistics) , bacteriophage , gene , heterologous , bacterial transcription , molecular evolution , experimental evolution , synthetic biology , computational biology , phenotype , transcription factor , sigma factor , t7 rna polymerase , rna , gene expression , phylogenetics , escherichia coli , linguistics , philosophy
Prediction of evolutionary trajectories has been an elusive goal, requiring a deep knowledge of underlying mechanisms that relate genotype to phenotype plus understanding how phenotype impacts organismal fitness. We tested our ability to predict molecular regulatory evolution in a bacteriophage (T7) whose RNA polymerase (RNAP) was altered to recognize a heterologous promoter differing by three nucleotides from the wild-type promoter. A mutant of wild-type T7 lacking its RNAP gene was passaged on a bacterial strain providing the novel RNAP in trans. Higher fitness rapidly evolved. Predicting the evolutionary trajectory of this adaptation used measured in vitro transcription rates of the novel RNAP on the six promoter sequences capturing all possible one-step pathways between the wild-type and the heterologous promoter sequences. The predictions captured some of the regulatory evolution but failed both in explaining 1) a set of T7 promoters that consistently failed to evolve and 2) some promoter evolution that fell outside the expected one-step pathways. Had a more comprehensive set of transcription assays been undertaken initially, all promoter evolution would have fallen within predicted bounds, but the lack of evolution in some promoters is unresolved. Overall, this study points toward the increasing feasibility of predicting evolution in well-characterized, simple systems.

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