Evidence map›Paper›PMID 19966064›Full record

ArticleGenetics2010

The evolution of control and distribution of adaptive mutations in a metabolic pathway.

Kevin M Wright, Mark D Rausher

Abstract read
In one paragraph

Article in Genetics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

39 citing papers in PubMed.

  1. Article
  2. Genome-wide association mapping within a localPhilosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022
    Article
  3. Article
  4. Article
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  6. Article
  7. Article
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  9. Article
  10. Adaptive molecular evolution ofEcology and evolution · 2017
    Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Gonads and the evolution of hormonal phenotypes.Integrative and comparative biology · 2016
    Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
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

2 authors.

Kevin M WrightDepartment of Biology, Duke University, Durham, North Carolina 27708. USA. kmw15@duke.edu
Mark D Rausher

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In an attempt to understand whether it should be expected that some genes tend to be used disproportionately often by natural selection, we investigated two related phenomena: the evolution of flux control among enzymes in a metabolic pathway and properties of adaptive substitutions in pathway enzymes. These two phenomena are related by the principle that adaptive substitutions should occur more frequently in enzymes with greater flux control. Predicting which enzymes will be preferentially involved in adaptive evolution thus requires an evolutionary theory of flux control. We investigated the evolution of enzyme control in metabolic pathways with two models of enzyme kinetics: metabolic control theory (MCT) and Michaelis-Menten saturation kinetics (SK). Our models generate two main predictions for pathways in which reactions are moderately to highly irreversible: (1) flux control will evolve to be highly unequal among enzymes in a pathway and (2) upstream enzymes evolve a greater control coefficient then those downstream. This results in upstream enzymes fixing the majority of beneficial mutations during adaptive evolution. Once the population has reached high fitness, the trend is reversed, with the majority of neutral/slightly deleterious mutations occurring in downstream enzymes. These patterns are the result of three factors (the first of these is unique to the MCT simulations while the other two seem to be general properties of the metabolic pathways): (1) the majority of randomly selected, starting combinations of enzyme kinetic rates generate pathways that possess greater control for the upstream enzymes compared to downstream enzymes; (2) selection against large pools of intermediate substrates tends to prevent majority control by downstream enzymes; and (3) equivalent mutations in enzyme kinetic rates have the greatest effect on flux for enzymes with high levels of flux control, and these enzymes will accumulate adaptive substitutions, strengthening their control. Prediction 1 is well supported by available data on control coefficients. Data for evaluating prediction 2 are sparse but not inconsistent with this prediction.

Indexed as

Evolution, MolecularMutationAdaptation, BiologicalEnzymesKineticsMetabolic Networks and PathwaysModels, BiologicalThermodynamicsEnzymes

Identifiers

PMID19966064
PMCPMC2828727

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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.