Evidence map›Paper›PMID 27547540›Full record

ArticlePeerJ2016

Love the one you're with: replicate viral adaptations converge on the same phenotypic change.

Craig R Miller, Anna C Nagel, LuAnn Scott, Matt Settles, Paul Joyce, Holly A Wichman

Abstract read
In one paragraph

Article in PeerJ, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Bacteriophage-Host Interactions and Coevolution.Methods in molecular biology (Clifton, N.J.) · 2024
    Article
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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

6 authors.

Craig R MillerCenter for Modeling Complex Interactions, University of Idaho, Moscow, ID, United States; Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, ID, United States; Department of Biological Sciences, University of Idaho, Moscow, ID, United States; Department of Mathematics, University of Idaho, Moscow, ID, United States.
Anna C NagelDepartment of Biological Sciences, University of Idaho , Moscow , ID , United States.
LuAnn ScottDepartment of Biological Sciences, University of Idaho , Moscow , ID , United States.
Matt SettlesBioinformatics Core, University of California , Davis , CA , United States.
Paul JoyceInstitute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, ID, United States; Department of Mathematics, University of Idaho, Moscow, ID, United States.
Holly A WichmanCenter for Modeling Complex Interactions, University of Idaho, Moscow, ID, United States; Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, ID, United States; Department of Biological Sciences, University of Idaho, Moscow, ID, United States.

Funding

Genomics Resources Core (GRC)P30GM103324 · NIGMS · UNIVERSITY OF IDAHO · PI FORNEY, LARRY J · 2013 to 2017
$5.0M
Patterns of Adaptive EvolutionR01GM076040 · NIGMS · UNIVERSITY OF IDAHO · PI MILLER, CRAIG R, WICHMAN, HOLLY A · 2006 to 2021
$3.6M
NIGMS NIH HHS P30 GM103324NIGMS NIH HHS R01 GM076040
6 · The paper itself

Abstract

Parallelism is important because it reveals how inherently stochastic adaptation is. Even as we come to better understand evolutionary forces, stochasticity limits how well we can predict evolutionary outcomes. Here we sought to quantify parallelism and some of its underlying causes by adapting a bacteriophage (ID11) with nine different first-step mutations, each with eight-fold replication, for 100 passages. This was followed by whole-genome sequencing five isolates from each endpoint. A large amount of variation arose-281 mutational events occurred representing 112 unique mutations. At least 41% of the mutations and 77% of the events were adaptive. Within wells, populations generally experienced complex interference dynamics. The genome locations and counts of mutations were highly uneven: mutations were concentrated in two regulatory elements and three genes and, while 103 of the 112 (92%) of the mutations were observed in ≤4 wells, a few mutations arose many times. 91% of the wells and 81% of the isolates had a mutation in the D-promoter. Parallelism was moderate compared to previous experiments with this system. On average, wells shared 27% of their mutations at the DNA level and 38% when the definition of parallel change is expanded to include the same regulatory feature or residue. About half of the parallelism came from D-promoter mutations. Background had a small but significant effect on parallelism. Similarly, an analyses of epistasis between mutations and their ancestral background was significant, but the result was mostly driven by four individual mutations. A second analysis of epistasis focused on de novo mutations revealed that no isolate ever had more than one D-promoter mutation and that 56 of the 65 isolates lacking a D-promoter mutation had a mutation in genes D and/or E. We assayed time to lysis in four of these mutually exclusive mutations (the two most frequent D-promoter and two in gene D) across four genetic backgrounds. In all cases lysis was delayed. We postulate that because host cells were generally rare (i.e., high multiplicity of infection conditions developed), selection favored phage that delayed lysis to better exploit their current host (i.e., 'love the one you're with'). Thus, the vast majority of wells (at least 64 of 68, or 94%) arrived at the same phenotypic solution, but through a variety of genetic changes. We conclude that answering questions about the range of possible adaptive trajectories, parallelism, and the predictability of evolution requires attention to the many biological levels where the process of adaptation plays out.

Indexed as

BacteriophageEpistasisExperimental evolutionParallelismϕX174

Identifiers

PMID27547540
PMCPMC4958007

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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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.