Genetic improvement of the shoot architecture and yield in soya bean plants via the manipulation of <i>GmmiR156b</i>
Bibliographic record
Abstract
The optimization of plant architecture in order to breed high-yielding soya bean cultivars is a goal of researchers. Tall plants bearing many long branches are desired, but only modest success in reaching these goals has been achieved. MicroRNA156 (miR156)-SQUAMOSA PROMOTER BINDING PROTEIN-LIKE (SPL) gene modules play pivotal roles in controlling shoot architecture and other traits in crops like rice and wheat. However, the effects of miR156-SPL modules on soya bean architecture and yield, and the molecular mechanisms underlying these effects, remain largely unknown. In this study, we achieved substantial improvements in soya bean architecture and yield by overexpressing GmmiR156b. Transgenic plants produced significantly increased numbers of long branches, nodes and pods, and they exhibited an increased 100-seed weight, resulting in a 46%-63% increase in yield per plant. Intriguingly, GmmiR156b overexpression had no significant impact on plant height in a growth room or under field conditions; however, it increased stem thickness significantly. Our data indicate that GmmiR156b modulates these traits mainly via the direct cleavage of SPL transcripts. Moreover, we found that GmSPL9d is expressed in the shoot apical meristem and axillary meristems (AMs) of soya bean, and that GmSPL9d may regulate axillary bud formation and branching by physically interacting with the homeobox gene WUSCHEL (WUS), a central regulator of AM formation. Together, our results identify GmmiR156b as a promising target for the improvement of soya bean plant architecture and yields, and they reveal a new and conserved regulatory cascade involving miR156-SPL-WUS that will help researchers decipher the genetic basis of plant architecture.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".