Cuticular wax biosynthesis in blueberries (<i>Vaccinium corymbosum</i> L.): Transcript and metabolite changes during ripening and storage affect key fruit quality traits
Bibliographic record
Abstract
Abstract In fruits, cuticular waxes affect fruit quality traits such as surface color at harvest and water loss during postharvest storage. This study investigated the transcriptional regulation of cuticular wax deposition in northern highbush blueberries (Vaccinium corymbosum L.) in relation to fruit water loss and surface color during ripening and postharvest storage, as well as the effects of abscisic acid (ABA)-mediated changes in cuticular wax deposition on these fruit quality traits. Total cuticular wax content (μg∙cm−2) decreased during fruit ripening and increased during postharvest storage. Transcriptome analysis revealed a transcript network for cuticular wax deposition in blueberries. Particularly, five OSC-Likes were identified as putative genes for triterpene alcohol production, with OSC-Like1 and OSC-Like2 encoding mixed amyrin synthases, OSC-Like3 encoding a lupeol synthase, and OSC-Like4 and OSC-Like5 encoding cycloartenol synthases. The expression of three CYP716A-like genes correlated to the accumulation of two triterpene acids oleanolic acid and ursolic acid, the major wax compounds in blueberries. Exogenous ABA application induced the expression of triterpenoid biosynthetic genes and the accumulation of β-amyrin and oleanolic acid, as well as increased the ratio of oleanolic acid to ursolic acid. These changes were associated with reduced fruit water loss. The content of β-diketones was also increased by ABA application, and this increase was associated with increased fruit lightness (measured as L* using CIELAB Color Space by a colorimeter). This study provided key insights on the molecular basis of cuticular wax deposition and its implications on fruit quality traits in blueberries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".