OsMYB103L, an R2R3-MYB transcription factor, influences leaf rolling and mechanical strength in rice (Oryza sativaL.)
Bibliographic record
Abstract
BACKGROUND: The shape of grass leaves possesses great value in both agronomy and developmental biology research. Leaf rolling is one of the important traits in rice (Oryza sativa L.) breeding. MYB transcription factors are one of the largest gene families and have important roles in plant development, metabolism and stress responses. However, little is known about their functions in rice. RESULTS: In this study, we report the functional characterization of a rice gene, OsMYB103L, which encodes an R2R3-MYB transcription factor. OsMYB103L was localized in the nucleus with transactivation activity. Overexpression of OsMYB103L in rice resulted in a rolled leaf phenotype. Further analyses showed that expression levels of several cellulose synthase genes (CESAs) were significantly increased, as was the cellulose content in OsMYB103L overexpressing lines. Knockdown of OsMYB103L by RNA interference led to a decreased level of cellulose content and reduced mechanical strength in leaves. Meanwhile, the expression levels of several CESA genes were decreased in these knockdown lines. CONCLUSIONS: These findings suggest that OsMYB103L may target CESA genes for regulation of cellulose synthesis and could potentially be engineered for desirable leaf shape and mechanical strength in rice.
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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".