Transcriptome features of stone cell development in weevil‐resistant and susceptible Sitka spruce
Bibliographic record
Abstract
Stone cells are a specialized, highly lignified cell type found in both angiosperms and gymnosperms. In conifers, abundance of stone cells in the cortex provides a robust constitutive physical defense against stem feeding insects. Stone cells are a major insect-resistance trait in Sitka spruce (Picea sitchensis), occurring in dense clusters in apical shoots of trees resistant (R) to spruce weevil (Pissodes strobi) but being rare in susceptible (S) trees. To learn more about molecular mechanisms of stone cell formation in conifers, we used laser microdissection and RNA sequencing to develop cell-type-specific transcriptomes of developing stone cells from R and S trees. Using light, immunohistochemical, and fluorescence microscopy, we also visualized the deposition of cellulose, xylan, and lignin associated with stone cell development. A total of 1293 genes were differentially expressed at higher levels in developing stone cells relative to cortical parenchyma. Genes with potential roles in stone cell secondary cell wall formation (SCW) were identified and their expression evaluated over a time course of stone cell formation in R and S trees. The expression of several transcriptional regulators was associated with stone cell formation, including a NAC family transcription factor and several genes annotated as MYB transcription factors with known roles in SCW formation.
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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".