Combined Metabolite and Transcriptome Profiling Reveals the Norisoprenoid Responses in Grape Berries to Abscisic Acid and Synthetic Auxin
Bibliographic record
Abstract
The abscisic acid (ABA) increase and auxin decline are both indicators of ripening initiation in grape berry, and norisoprenoid accumulation also starts at around the onset of ripening. However, the relationship between ABA, auxin, and norisoprenoids remains largely unknown, especially at the transcriptome level. To investigate the transcriptional and posttranscriptional regulation of the ABA and synthetic auxin 1-naphthaleneacetic acid (NAA) on norisoprenoid production, we performed time-series GC-MS and RNA-seq analyses on Vitis vinifera L. cv. Cabernet Sauvignon grape berries from pre-veraison to ripening. Higher levels of free norisoprenoids were found in ABA-treated mature berries in two consecutive seasons, and both free and total norisoprenoids were significantly increased by NAA in one season. The expression pattern of known norisoprenoid-associated genes in all samples and the up-regulation of specific alternative splicing isoforms of VviDXS and VviCRTISO in NAA-treated berries were predicted to contribute to the norisoprenoid accumulation in ABA and NAA-treated berries. Combined weighted gene co-expression network analysis (WGCNA) and DNA affinity purification sequencing (DAP-seq) analysis suggested that VviGATA26, and the previously identified switch genes of myb RADIALIS (VIT_207s0005g02730) and MAD-box (VIT_213s0158g00100) could be potential regulators of norisoprenoid accumulation. The positive effects of ABA on free norisoprenoids and NAA on total norisoprenoid accumulation were revealed in the commercially ripening berries. Since the endogenous ABA and auxin are sensitive to environmental factors, this finding provides new insights to develop viticultural practices for managing norisoprenoids in vineyards in response to changing climates.
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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) | 0.001 | 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".