The MicroRNA397a-LACCASE17 module regulates lignin biosynthesis in Medicago ruthenica (L.)
Bibliographic record
Abstract
Mechanical strength is essential for the upright growth habit, which is one of the most important characteristics of terrestrial plants. Lignin, a phenylpropanoid-derived polymer mainly present in secondary cell walls plays critical role in providing mechanical support. Here, we report that the prostrate-stem cultivar of the legume forage Medicago ruthenica cultivar ‘Mengnong No. 1’ shows compromised mechanical strength compared with the erect-stem cultivar ‘Zhilixing’. The erect-stem cultivar, ‘Zhilixing’ has significantly higher lignin content, leading to higher mechanical strength than the prostrate-stem cultivar. The low abundance of miRNA397a in the Zhiixing cultivar causes reduced cleavage of MrLAC17 transcript, which results in enhanced expression level of MrLAC17 compared to that in the prostrate-stem cultivar Mengnong No. 1. Complementation of the Arabidopsis lac4 lac17 double mutants with MrLAC17 restored the lignin content to wild-type levels, confirming that MrLAC17 perform an exchangeable role with Arabidopsis laccases. LAC17-mediated lignin polymerization is therefore increased in the ‘Zhilixing’, causing the erect stem phenotype. Our data reveal the importance of the miR397a in the lignin biosynthesis and suggest a strategy for molecular breeding targeting plant architecture in legume forage.
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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".