Improvement of Forage Quality by Downregulation of Maize <i>O</i>‐Methyltransferase
Bibliographic record
Abstract
Lignin is a complex, aromatic polymer that limits plant cell wall degradation by ruminants and reduces the nutritional value of forages. Genetic engineering, using an antisense strategy, offers the potential to modulate enzymes in the lignin biosynthetic pathway as a way to reduce lignin, thereby improving forage quality and animal performance. We investigated the effectiveness of expressing antisense sorghum O‐methyltransferase gene (omt) to downregulate maize OMT and reduce lignin. Constructs contained a sorghum omt coding region in the antisense orientation driven by the maize ubiquitin‐1 (Ubi) promoter (with the first intron and exon) along with bar, that confers glufosinate herbicide resistance, driven by the CaMV 35S promoter. Twenty‐eight T0 plants regenerated from 17 herbicide‐resistant callus lines from 13 independent bombardments expressed the brown midrib phenotype. O‐methyltransferase activity was significantly lower in T1 transgenics compared with controls, with some plants showing a 60% reduction. Those T1 transgenics with downregulated OMT averaged 20% less lignin in stems and 12% less lignin in leaves compared with controls. On a whole‐plant basis, lignin was reduced by an average of 17% with the greatest reduction being 31%. Digestibility was significantly improved in transgenic plants by 2% in leaves and 7% in stems. Mean whole‐plant digestibility increased from 72 to 76%. This research demonstrates that genetic engineering has the potential to improve forage grass digestibility. This could be important, especially in tropical forage species, which generally have lower quality than temperate species.
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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) | 0.000 | 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 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".