Conifer defense against insects: Proteome analysis of Sitka spruce (<b><i>Picea sitchensis</i></b>) bark induced by mechanical wounding or feeding by white pine weevils (<b><i>Pissodes strobi</i></b>)
Bibliographic record
Abstract
Feeding insects can have major ecological and economic impacts on both natural and planted forests. Understanding the molecular and biochemical mechanisms by which conifers defend themselves from insect pests is a major goal of ongoing research in forest health genomics. In previous work, we demonstrated a complex system of anatomical, chemical, and transcriptome responses in Sitka spruce (Picea sitchensis) upon feeding by the economically significant insect pest, the white pine weevil (Pissodes strobi). In this study, changes to the proteome of Sitka spruce bark tissue were examined subsequent to feeding by white pine weevils or mechanical wounding. 2-D PAGE and high-throughput MS/MS were used to examine induced changes in protein abundance and protein modification. Significant changes were observed as early as 2 h following the onset of insect feeding. Among the insect-induced proteins are a series of related small heat shock proteins, other stress response proteins, proteins involved in secondary metabolism, oxidoreductases, and a novel spruce protein. Comparison of protein expression and cDNA microarray profiles of induced spruce stem tissues reveals the complementary nature of transcriptome and proteome analyses and the need to apply a multifaceted approach to the large-scale analysis of plant defense systems.
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 | 0.000 | 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".