Evidence map›Paper›PMID 17684697›Full record

ArticleJournal of molecular evolution2007

Evolutionarily conserved optimization of amino acid biosynthesis.

Ethan O Perlstein, Benjamin L de Bivort, Samuel Kunes, Stuart L Schreiber

Abstract read
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In one paragraph

Article in Journal of molecular evolution, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Amino Acid metabolism conflicts with protein diversity.Molecular biology and evolution · 2014
    Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. 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

4 authors.

Ethan O PerlsteinHoward Hughes Medical Institute, Broad Institute of Harvard and MIT, 7 Cambridge Center, Cambridge, MA 02142, USA. perlst@fas.harvard.edu
Benjamin L de Bivort
Samuel Kunes
Stuart L Schreiber

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The "cognate bias hypothesis" states that early in evolutionary history the biosynthetic enzymes for amino acid x gradually lost residues of x, thereby reducing the threshold for deleterious effects of x scarcity. The resulting reduction in cognate amino acid composition of the enzymes comprising a particular amino acid biosynthetic pathway is predicted to confer a selective growth advantage on cells. Bioinformatic evidence from protein-sequence data of two bacterial species previously demonstrated reduced cognate bias in amino acid biosynthetic pathways. Here we show that cognate bias in amino acid biosynthesis is present in the other domains of life-Archaebacteria and Eukaryota. We also observe evolutionarily conserved underrepresentations (e.g., glycine in methionine biosynthesis) and overrepresentations (e.g., tryptophan in asparagine biosynthesis) of amino acids in noncognate biosynthetic pathways, which can be explained by secondary amino acid metabolism. Additionally, we experimentally validate the cognate bias hypothesis using the yeast Saccharomyces cerevisiae. Specifically, we show that the degree to which growth declines following amino acid deprivation is negatively correlated with the degree to which an amino acid is underrepresented in the enzymes that comprise its cognate biosynthetic pathway. Moreover, we demonstrate that cognate fold representation is more predictive of growth advantage than a host of other potential growth-limiting factors, including an amino acid's metabolic cost or its intracellular concentration and compartmental distribution.

Indexed as

Evolution, MolecularAmino AcidsBacillus subtilisBiosynthetic PathwaysEscherichia coliHumansModels, BiologicalSaccharomyces cerevisiaeSelection, GeneticAmino Acids

Identifiers

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

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.