Evidence map›Paper›PMID 19216750›Full record

ArticleGenome biology2009

Asymmetric relationships between proteins shape genome evolution.

Richard A Notebaart, Philip R Kensche, Martijn A Huynen, Bas E Dutilh

Abstract read
In one paragraph

Article in Genome biology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

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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

14 citing papers in PubMed.

  1. Evolutionary design principles in metabolism.Proceedings. Biological sciences · 2019
    Review
  2. Article
  3. Article
  4. Practical aspects of protein co-evolution.Frontiers in cell and developmental biology · 2014
    Review
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  6. Article
  7. Article
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  11. Review
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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

4 authors.

Richard A NotebaartCenter for Molecular and Biomolecular Informatics, Nijmegen Center for Molecular Life Sciences, Radboud University Nijmegen Medical Center, Geert Grooteplein 26-28, 6525 GA, Nijmegen, The Netherlands.
Philip R Kensche
Martijn A Huynen
Bas E Dutilh

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe relationships between proteins are often asymmetric: one protein (A) depends for its function on another protein (B), but the second protein does not depend on the first. In metabolic networks there are multiple pathways that converge into one central pathway. The enzymes in the converging pathways depend on the enzymes in the central pathway, but the enzymes in the latter do not depend on any specific enzyme in the converging pathways. Asymmetric relations are analogous to the "if->then" logical relation where A implies B, but B does not imply A (A->B).

resultsWe show that the majority of relationships between enzymes in metabolic flux models of metabolism in Escherichia coli and Saccharomyces cerevisiae are asymmetric. We show furthermore that these asymmetric relationships are reflected in the expression of the genes encoding those enzymes, the effect of gene knockouts and the evolution of genomes. From the asymmetric relative dependency, one would expect that the gene that is relatively independent (B) can occur without the other dependent gene (A), but not the reverse. Indeed, when only one gene of an A->B pair is expressed, is essential, is present in a genome after an evolutionary gain or loss, it tends to be the independent gene (B). This bias is strongest for genes encoding proteins whose asymmetric relationship is evolutionarily conserved.

conclusionsThe asymmetric relations between proteins that arise from the system properties of metabolic networks affect gene expression, the relative effect of gene knockouts and genome evolution in a predictable manner.

Indexed as

Biological EvolutionEscherichia coliGene Expression Regulation, EnzymologicGenomeMetabolic Networks and PathwaysProteinsSaccharomyces cerevisiaeProteins

Identifiers

PMID19216750
PMCPMC2688278

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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.