Evidence map›Paper›PMID 30184065›Full record

ArticleGenome biology and evolution2018

Predicting Evolution of the Transcription Regulatory Network in a Bacteriophage.

Daniel J Garry, Adam J Meyer, Jared W Ellefson, James J Bull, Andrew D Ellington

Abstract read
In one paragraph

Article in Genome biology and evolution, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Daniel J GarryCenter for Systems and Synthetic Biology, Department of Molecular Biosciences, Institute for Cellular and Molecular Biology, University of Texas, Austin.
Adam J MeyerSynthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology.
Jared W EllefsonCenter for Systems and Synthetic Biology, Department of Molecular Biosciences, Institute for Cellular and Molecular Biology, University of Texas, Austin.
James J BullCenter for Computational Biology and Bioinformatics, Department of Integrative Biology, Institute for Cellular and Molecular Biology, University of Texas, Austin.
Andrew D EllingtonCenter for Systems and Synthetic Biology, Department of Molecular Biosciences, Institute for Cellular and Molecular Biology, University of Texas, Austin.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Gene Regulatory NetworksBacteriophage T7Biological EvolutionMutationPromoter Regions, Genetic

Identifiers

PMID30184065
PMCPMC6171733

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.