Enhancing monolignol ferulate conjugate levels in poplar lignin via OsFMT1
Bibliographic record
Abstract
BACKGROUND: The phenolic polymer lignin is one of the primary chemical constituents of the plant secondary cell wall. Due to the inherent plasticity of lignin biosynthesis, several phenolic monomers have been shown to be incorporated into the polymer, as long as the monomer can undergo radicalization so it can participate in coupling reactions. In this study, we significantly enhance the level of incorporation of monolignol ferulate conjugates into the lignin polymer to improve the digestibility of lignocellulosic biomass. RESULTS: Overexpression of a rice Feruloyl-CoA Monolignol Transferase (FMT), OsFMT1, in hybrid poplar (Populus alba x grandidentata) produced transgenic trees clearly displaying increased cell wall-bound ester-linked ferulate, p-hydroxybenzoate, and p-coumarate, all of which are in the lignin cell wall fraction, as shown by NMR and DFRC. We also demonstrate the use of a novel UV-Vis spectroscopic technique to rapidly screen plants for the presence of both ferulate and p-hydroxybenzoate esters. Lastly we show, via saccharification assays, that the OsFMT1 transgenic p oplars have significantly improved processing efficiency compared to wild-type and Angelica sinensis-FMT-expressing poplars. CONCLUSIONS: The findings demonstrate that OsFMT1 has a broad substrate specificity and a higher catalytic efficiency compared to the previously published FMT from Angelica sinensis (AsFMT). Importantly, enhanced wood processability makes OsFMT1 a promising gene to optimize the composition of lignocellulosic biomass.
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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".