The in vivo impact of MsLAC1, a Miscanthus laccase isoform, on lignification and lignin composition contrasts with its in vitro substrate preference
Bibliographic record
Abstract
BACKGROUND: Understanding lignin biosynthesis and composition is of central importance for sustainable bioenergy and biomaterials production. Species of the genus Miscanthus have emerged as promising bioenergy crop due to their rapid growth and modest nutrient requirements. However, lignin polymerization in Miscanthus is poorly understood. It was previously shown that plant laccases are phenol oxidases that have multiple functions in plant, one of which is the polymerization of monolignols. Herein, we link a newly discovered Miscanthus laccase, MsLAC1, to cell wall lignification. Characterization of recombinant MsLAC1 and Arabidopsis transgenic plants expressing MsLAC1 were carried out to understand the function of MsLAC1 both in vitro and in vivo. RESULTS: Using a comprehensive suite of molecular, biochemical and histochemical analyses, we show that MsLAC1 localizes to cell walls and identify Miscanthus transcription factors capable of regulating MsLAC1 expression. In addition, MsLAC1 complements the Arabidopsis lac4-2 lac17 mutant and recombinant MsLAC1 is able to oxidize monolignol in vitro. Transgenic Arabidopsis plants over-expressing MsLAC1 show higher G-lignin content, although recombinant MsLAC1 seemed to prefer sinapyl alcohol as substrate. CONCLUSIONS: In summary, our results suggest that MsLAC1 is regulated by secondary cell wall MYB transcription factors and is involved in lignification of xylem fibers. This report identifies MsLAC1 as a promising breeding target in Miscanthus for biofuel and biomaterial applications.
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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".