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Record W2561319718 · doi:10.1055/s-0036-1596787

Metabolite and transcriptome profiling of russeted and waxy apple skins highlighted genes involved in triterpene-hydroxycinnamate biosynthesis

2016· article· en· W2561319718 on OpenAlexaboutno aff
CM Andre, Sylvain Legay, Sophie Charton, Jenny Renaut, J-F Hausman

Bibliographic record

VenuePlanta Medica · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Surface Properties and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsTriterpeneBetulinic acidTerpeneMetabolomicsMetabolite profilingChemistryTranscriptomeHydroxycinnamic acidMetaboliteBiochemistryBiologyBotanyGene expressionGeneChromatographyMedicine

Abstract

fetched live from OpenAlex

Pentacyclic triterpenes possess numerous biomedical properties, including anti-inflammatory, anti-cancer, and anti-plasmodial activities. Esterification of triterpenic acids with hydroxycinnamic acids such as caffeic acid has been shown to increase their biological potential. In previous studies, we showed that russeted old heritage apple varieties are characterized by the accumulation of suberin in skin tissues [1], which present higher concentrations of specific triterpene esters such as betulinic acid-3-trans-caffeate [2] (Figure 1) as compared to their waxy-skinned counterparts. Knowledge on the molecular events associated with triterpene-caffeate production is however still lacking, although it could be therapeutically of major interest. For that purpose, apple fruits from two nearly isogenic yet contrasting varieties, i.e. 'Reinette du Canada Gris' (russeted skin) and 'Reinette du Canada Blanc' (waxy skin), were collected at five time points during the 2013 growing season. Metabolomics data were obtained by Ultra-Performance Liquid chromatography hyphenated with a high-resolution mass spectrometer (UPLC-TripleTOF HR-MS) and compared with whole gene expression profiling data (RNA-Seq).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.172

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.196
Teacher spread0.180 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2016
Admission routes1
Has abstractyes

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