A complex regulatory network underlies de novo root regeneration in red maple (<i>Acer rubrum</i>)
Bibliographic record
Abstract
Red maple (Acer rubrum L.) is ornamentally and medicinally valuable; however, its wider application is restricted by the difficulty of rooting from cuttings. We analyzed paraffin sections of roots regenerating using RNA-Seq to decipher the mechanisms underlying de novo root regeneration (DNRR) in red maple cuttings. This work contributes to improving the rooting rate and shortening the rooting time. We identified four stages during DNRR: 0 days after induction (DAI), no new cell formation; 30 DAI, root meristem organization; 36 DAI, root primordium formation; and 45 DAI, root elongation growth. We identified 37 959 unigenes by de novo assembly, with 25 477 (67.12%) functionally annotated. Furthermore, we identified 1285 differentially expressed genes (DEGs) between adjacent stages. From Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment networks, we found evidence that plant hormones are significant in DNRR of red maple cuttings. Specifically, 149 DEGs functioned in hormone signal transduction pathways, particularly those involving ethylene, auxin, and jasmonic acid (JA). We propose a complex regulatory network model of DNRR in red maple, where wounding induces root regeneration through pathways of JA and auxin signaling. The transcription factors ETHYLENE RESPONSE FACTOR 109 (ERF109) and ERF115 integrate JA signal and participate in DNRR directly by regulating SCARECROW activation and indirectly by promoting auxin biosynthesis.
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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".