Variation in the molecular phenotype of βglu1, an insect defense-related beta-glucosidase gene, in two transcontinental Picea species
Bibliographic record
Abstract
Forest trees face threats from many insect pest species, underscoring the importance of understanding their defense mechanisms for survival. In a North American conifer species Picea glauca, white spruce, a defense-related gene, βglu1, is responsible for releasing phenolic compounds (acetophenones) to defend against its insect defoliator, Choristoneura fumiferana, the eastern spruce budworm. βglu1 is also expressed in a Eurasian conifer species Picea abies, Norway spruce, although no major insect defoliator is present within the species’ natural range. We compared range-wide variation of βglu1 transcript levels from foliage samples of P. glauca in North America and P. abies in Europe using RT-qPCR and targeted transcriptome sequencing. βglu1 transcript levels were highly correlated between the two methods, with wide ranges of variation being detected within and between populations in both species. We found a significant longitudinal gradient in βglu1 transcript levels in P. glauca, with one βglu1 gene form being differentially expressed across populations, but not in P. abies. The expression level differences in P. glauca are consistent with the historically higher C. fumiferana outbreak frequency and severity in eastern compared to western populations, with C. fumiferana defoliation severity being a significant explanatory variable for βglu1 transcript levels. Climate per se was not a significant explanatory factor in either species. Overall, these results enhance our understanding of potential adaptive variation in acetophenone defenses in P. glauca, while the factors influencing βglu1 transcript variation in P. abies require further investigation.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".