Evidence map›Paper›PMID 23271801›Full record

ArticleMolecular & cellular proteomics : MCP2013

Linking post-translational modifications and variation of phenotypic traits.

Warren Albertin, Philippe Marullo, Marina Bely, Michel Aigle, Aurélie Bourgais, Olivier Langella, Thierry Balliau, Didier Chevret, Benoît Valot, Telma da Silva and 3 more

Abstract read
In one paragraph

Article in Molecular & cellular proteomics : MCP, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

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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

13 authors.

Warren AlbertinCNRS, UMR 0320/UMR 8120 Génétique Végétale, Gif-sur-Yvette, France.
Philippe Marullo
Marina Bely
Michel Aigle
Aurélie Bourgais
Olivier Langella
Thierry Balliau
Didier Chevret
Benoît Valot
Telma da Silva
Christine Dillmann
Dominique de Vienne
Delphine Sicard

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Enzymes can be post-translationally modified, leading to isoforms with different properties. The phenotypic consequences of the quantitative variability of isoforms have never been studied. We used quantitative proteomics to dissect the relationships between the abundances of the enzymes and isoforms of alcoholic fermentation, metabolic traits, and growth-related traits in Saccharomyces cerevisiae. Although the enzymatic pool allocated to the fermentation proteome was constant over the culture media and the strains considered, there was variation in abundance of individual enzymes and sometimes much more of their isoforms, which suggests the existence of selective constraints on total protein abundance and trade-offs between isoforms. Variations in abundance of some isoforms were significantly associated to metabolic traits and growth-related traits. In particular, cell size and maximum population size were highly correlated to the degree of N-terminal acetylation of the alcohol dehydrogenase. The fermentation proteome was found to be shaped by human selection, through the differential targeting of a few isoforms for each food-processing origin of strains. These results highlight the importance of post-translational modifications in the diversity of metabolic and life-history traits.

Indexed as

Genetic VariationProtein Processing, Post-TranslationalAlcohol DehydrogenaseAmino Acid SequenceChromatography, High Pressure LiquidCluster AnalysisElectrophoresis, Gel, Two-DimensionalFermentationFood MicrobiologyHumansIsoenzymesMass SpectrometryMetabolic Networks and PathwaysMolecular Sequence DataPhenotypePhylogenyAlcohol DehydrogenaseIsoenzymesProteomeSaccharomyces cerevisiae Proteins

Identifiers

PMID23271801
PMCPMC3591664

What OpenQuestion holds

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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.