Phytohormone dynamics impact fatty acid and oil accumulation during soybean seed maturation
Bibliographic record
Abstract
Abstract Fatty acid (FA) levels and profiles are vital for soybean oil quality, while cytokinins (CKs) and abscisic acid (ABA) are potent regulators of plant growth and development. Previous research suggested associations between FA biosynthesis and hormonal signalling networks; however, hormonal regulation of FA accumulation during soybean ( Glycine max ) seed maturation has never been measured. We analysed hormone and FA profiles obtained from HPLC-(ESI)-MS/MS and GC-FID screening during soybean seed maturation. A multilayered data processing approach, involving heat-maps, principal component analysis (PCA), correlation and multiregression models, suggested a strong relationship between hormone metabolism and FA/oil accumulation during seed maturation. Most strikingly, positive correlations were found between the levels of CK ribosides [ trans Zeatin riboside ( t ZR), N 6 -isopentenyladenosine (iPR)] at the early stages of SM (R5-R6) and C18:0, C18:2 and oil content at the R8 stage. Moreover, multiple regression models revealed functional linkages between several CK derivatives and FA and oil content in mature seeds. To further test the significance of hormone regulation in FA metabolism, plants of two soybean accessions with contrasting hormone and FA profiles were sprayed with exogenous ABA and trans Zeatin ( t Z) during the seed-filling period (R5-R6). Depending on the hormone type and concentration, these treatments distinctly modified biosynthesis of all tested FAs, except for C18:0. Most remarkably, t Z (50 nM) promoted production of C16:0, C18:1, C18:2, C18:3, and oil accumulation in maturing seeds. Overall, the results indicate impactful roles for ABA and CKs in FA accumulation during SM and represent a further step towards understanding FA biosynthesis, and potential improvements of soybean oil profiles.
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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".