How lignin biosynthesis responds to nitrogen in plants: a scoping review
Bibliographic record
Abstract
Nitrogen (N) plays a critical role in the functioning of key amino acids and synthetic enzymes responsible for the various stages of lignin biosynthesis. However, the precise mechanisms through which N influences lignin biosynthesis have not been fully elucidated. This scoping review explores how lignin biosynthesis responds to N in plants. A systematic search of the literature in several databases was conducted using relevant keywords. Only 44 of the 1842 selected studies contained a range of plant species, experimental conditions, and research approaches. Lignin content, structure, and biosynthetic pathways in response to N are discussed, and possible response mechanisms of lignin under low N are proposed. Among the selected studies, 64.52% of the studies reter to lignin content found a negative correlation between N availability and lignin content. Usually, high N decreases the lignin content, delays cell lignification, increases p-hydroxyphenyl propane (H) monomer content, and regulates lignin synthesis through the expression of key genes (PAL, 4CL, CCR, CAD, COMT, LAC, and POD) encoding miRNAs and transcription factors (e.g., MYB, bHLH). N deficiency enhances lignin synthesis through the accumulation of phenylpropanoids, phenolics, and soluble carbohydrates, and indirect changes in phytohormones, secondary metabolites, etc. This review provides new insights and important references for future studies on the regulation of lignin biosynthesis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".