Gene expression analysis of primordial shoot explants collected from mature white spruce (Picea glauca) trees that differ in their responsiveness to somatic embryogenesis induction
Bibliographic record
Abstract
Within a plantation of clonal somatic embryo-derived white spruce trees that belonged to four genotypes, one genotype (G6) has consistently responded for the last 16 years, to the induction of somatic embryogenesis within primordial shoot explants. Analysis of fourteen individuals within this genotype subsequently revealed a group of clonal trees that were nonresponsive. This in turn provided a unique opportunity to conduct differential gene expression analysis in the absence of genotype-specific factors. Absolute qPCR was first used to expand the analysis of several genes previously identified via microarray analysis to be differentially expressed during SE induction, along with the inclusion of two nonresponsive genotypes. While this demonstrated a high level of repeatability within, and between, responsive and nonresponsive genotypes, it did not support our previous contention that an adaptive stress response plays a role in SE induction responsiveness, at least with respect to the candidate genes we analyzed. RNAseq analysis was then used to compare responsive and nonresponsive G6 primordial shoots during the somatic embryogenesis induction treatment. Although not analyzed in this study, this included samples of callus and embryonal masses previously generated from G6 explants. In addition to revealing a large number of differentially expressed genes, de novo assembly of unmapped reads was used to generate over 25,000 contigs that potentially represent previously unidentified transcripts. This included a MADS-domain gene that was found to be the most highly differentially expressed gene within responsive shoot explants during the first seven days of the induction treatment.
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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".