Inactivation of LACCASE8 and LACCASE5 genes in Brachypodium distachyon leads to severe decrease in lignin content and high increase in saccharification yield without impacting plant integrity
Bibliographic record
Abstract
Dedicated lignocellulosic feedstock from grass crops for biofuel production is extensively increasing. However, the access to fermentable cell wall sugars by carbohydrate degrading enzymes is impeded by lignins. These complex polymers are made from reactive oxidized monolignols in the cell wall. Little is known about the laccase-mediated oxidation of monolignols in grasses, and inactivation of the monolignol polymerization mechanism might be a strategy to increase the yield of fermentable sugars. LACCASE5 and LACCASE8 are inactivated in a Brachypodium double mutant. Relative to the wild type, the lignin content of extract-free mature culms is decreased by 20–30% and the saccharification yield is increased by 140%. Release of ferulic acid by mild alkaline hydrolysis is also 2.5-fold higher. Interfascicular fibers are mainly affected while integrity of vascular bundles is not impaired. Interestingly, there is no drastic impact of the double mutation on plant growth. This work shows that two Brachypodium laccases with clearly identified orthologs in crops are involved in lignification of this model plant. Lignification in interfascicular fibers and metaxylem cells is partly uncoupled in Brachypodium . Orthologs of these laccases are promising targets for improving grass feedstock for cellulosic biofuel production.
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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) | 0.001 | 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 | 0.001 |
| 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".