Distinct and overlapping functions of <i>Miscanthus sinensis</i> MYB transcription factors SCM1 and MYB103 in lignin biosynthesis
Bibliographic record
Abstract
Abstract Cell wall recalcitrance is a major constraint for the exploitation of lignocellulosic biomass as renewable resource for energy and bio-based products. Transcriptional regulators of the lignin biosynthetic pathway represent promising targets for tailoring lignin content and composition in plant secondary cell walls. A wealth of research in model organisms has revealed that transcriptional regulation of secondary cell wall formation is orchestrated by a hierarchical transcription factor (TF) network with NAC TFs as master regulators and MYB factors in the lower tier regulators. However, knowledge about the transcriptional regulation of lignin biosynthesis in lignocellulosic feedstocks, such as Miscanthus, is limited. Here, we characterized two Miscanthus MYB TFs, MsSCM1 and MsMYB103, and compared their transcriptional impact with that of the master regulator MsSND1. In Miscanthus leaves MsSCM1 and MsMYB103 are expressed at growth stages associated with lignification. Ectopic expression of MsSCM1 and MsMYB103 in tobacco leaves was sufficient to trigger secondary cell wall deposition with distinct sugar and lignin composition. Moreover, RNA-seq analysis revealed that the transcriptional responses to MsSCM1 and MsMYB103 overexpression showed extensive overlap with the response to MsSND1 , but were distinct from each other, underscoring the inherent complexity of secondary cell wall formation. Together, MsSCM1 and MsMYB103 represent interesting targets for manipulations of lignin content and composition in Miscanthus towards tailored 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".