RNAi downregulation of three key lignin genes in sugarcane improves glucose release without reduction in sugar production
Bibliographic record
Abstract
Sugarcane is a subtropical crop that produces large amounts of biomass annually. It is a key agricultural crop in many countries for the production of sugar and other products. Residual bagasse following sucrose extraction is currently underutilized and it has potential as a carbohydrate source for the production of biofuels. As with all lignocellulosic crops, lignin acts as a barrier to accessing the polysaccharides, and as such, is the focus of transgenic efforts. In this study, we used RNAi to individually reduce the expression of three key genes in the lignin biosynthetic pathway in sugarcane. These genes, caffeoyl-CoA O -methyltransferase ( CCoAOMT ), ferulate 5-hydroxylase ( F5H ) and caffeic acid O -methyltransferase ( COMT ), impact lignin content and/or composition. For each RNAi construct, we selected three events for further analysis based on qRT-PCR results. For the CCoAOMT lines, there were no lines with a reduction in lignin content and only one line showed improved glucose release. For F5H , no lines had reduced lignin, but one line had a significant increase in glucose release. For COMT , one line had reduced lignin content, and this line and another released higher levels of glucose during enzymatic hydrolysis. Two of the lines with improved glucose release (F5H-2 and COMT-2) also had reduced S:G ratios. Along with improvements in bagasse quality for the production of lignocellulosic-based fuels, there was only one line with reduction in juice sucrose extraction, and three lines with significantly improved sucrose production, providing evidence that the alteration of sugarcane for improved lignocellulosic ethanol production can be achieved without negatively impacting sugar production and perhaps even enhancing it.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".