Gibberellin-induced changes in the transcriptome of grapevine (Vitis labrusca × V. vinifera) cv. Kyoho flowers
Bibliographic record
Abstract
BACKGROUND: Gibberellins are well known for their growth control function in flower, fruit and seed development, and as such, exogenous gibberellic acid (GA) application plays an important role in viticulture. Unfortunately, the mechanism by which GA3 acts in the regulation of these complicated developmental processes in grape remains unclear. RESULTS: In the present study, we demonstrated that application of GA3 to 'Kyoho' grapevine inflorescences at pre-bloom promoted flower opening, and induced fruit coloring as well as seed abortion. In an attempt to obtain a deeper understanding of the molecular mechanisms driving these responses to GA3 treatment, we performed large-scale transcriptome sequencing of grape flowers following GA3 treatment using Illumina sequencing technology. Global expression profiles of GA3-treated and untreated grape flowers were compared and a large number of GA3-responsive genes were identified. Gene ontology (GO) term classification and biochemical pathway analyses indicated that GA3 treatment caused changes in the levels of transcripts involved in cellular processes, reproduction, hormone and secondary metabolism, as well as the scavenging and detoxification of reactive oxygen species (ROS). These findings suggest that GA3-induced morphological alterations may be related to the control of hormone biosynthesis and signaling, regulation of transcription factors, alteration of secondary metabolites, and the stability of redox homeostasis. CONCLUSIONS: Taken together, this comprehensive inflorescence transcriptome data set provides novel insight into the response of grape flowers to GA3 treatment, and also provides possible candidate genes or markers that could be used to guide future efforts in this field.
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 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".