Investigating the molecular underpinnings underlying morphology and changes in carbon partitioning during tension wood formation in <i>Eucalyptus</i>
Bibliographic record
Abstract
Tension wood has distinct physical and chemical properties, including altered fibre properties, cell wall composition and ultrastructure. It serves as a good system for investigating the genetic regulation of secondary cell wall biosynthesis and wood formation. The reference genome sequence for Eucalyptus grandis allows investigation of the global transcriptional reprogramming that accompanies tension wood formation in this global wood fibre crop. We report the first comprehensive analysis of physicochemical wood property changes in tension wood of Eucalyptus measured in a hybrid (E. grandis × Eucalyptus urophylla) clone, as well as genome-wide gene expression changes in xylem tissues 3 wk post-induction using RNA sequencing. We found that Eucalyptus tension wood in field-grown trees is characterized by an increase in cellulose, a reduction in lignin, xylose and mannose, and a marked increase in galactose. Gene expression profiling in tension wood-forming tissue showed corresponding down-regulation of monolignol biosynthetic genes, and differential expression of several carbohydrate active enzymes. We conclude that alterations of cell wall traits induced by tension wood formation in Eucalyptus are a consequence of a combination of down-regulation of lignin biosynthesis and hemicellulose remodelling, rather than the often proposed up-regulation of the cellulose biosynthetic pathway.
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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) | 0.000 | 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".