Transcriptomic analysis reveals the gibberellin signaling pathway involved in the regulation of flower number in Hevea brasiliensis Muell. Arg.
Bibliographic record
Abstract
The rubber tree ( Hevea brasiliensis Muell. Arg.) holds significant socio-economic value due to its contribution to the production of natural rubber (NR). However, prolonged sexual reproduction leads to nutrient depletion (N, P, K, Mg²⁺) in rubber trees, resulting in reduced latex yield and quality. Gibberellin (GA) regulates critical processes including plant growth and flowering time. To investigate the regulatory mechanism of GA on rubber tree flowering, inflorescences were sprayed with GA 3 and the GA biosynthesis inhibitor chlormequat chloride (CCC). Both treatments inhibited flowering and reduced nutrient accumulation in inflorescences and leaves. Subsequent transcriptome sequencing of inflorescences sampled at multiple time points post-treatment identified differentially expressed genes (DEGs). Analysis of these DEGs, combined with endogenous hormone detection, revealed crosstalk between plant hormone signaling pathways. Specifically, differential expression was observed in genes associated with GA signal transduction, flowering regulation, and NR biosynthesis. These findings were confirmed by real-time quantitative PCR (RT-qPCR). This study provides novel insights into the mechanisms of underlying GA signaling-mediated inhibition of flowering in rubber trees and offers potential avenues for enhancing latex production within the rubber industry. • Rubber tree flowering inhibition by gibberellin. • Reduced accumulation of nutrient content is associated with flowering inhibition. • Plant hormone signaling genes co-regulate flowering. • A theoretical model for regulating flowering in rubber trees.
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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".