An integrated genomic, proteomic and biochemical analysis of (+)‐3‐carene biosynthesis in Sitka spruce (<i>Picea sitchensis</i>) genotypes that are resistant or susceptible to white pine weevil
Bibliographic record
Abstract
Conifers are extremely long-lived plants that have evolved complex chemical defenses in the form of oleoresin terpenoids to resist attack from pathogens and herbivores. In these species, terpenoid diversity is determined by the size and composition of the terpene synthase (TPS) gene family and the single- and multi-product profiles of these enzymes. The monoterpene (+)-3-carene is associated with resistance of Sitka spruce (Picea sitchensis) to white pine weevil (Pissodes strobi). We used a combined genomic, proteomic and biochemical approach to analyze the (+)-3-carene phenotype in two contrasting Sitka spruce genotypes. Resistant trees produced significantly higher levels of (+)-3-carene than susceptible trees, in which only trace amounts were detected. Biosynthesis of (+)-3-carene is controlled, at the genome level, by a small family of closely related (+)-3-carene synthase (PsTPS-3car) genes (82-95% amino acid sequence identity). Transcript profiling identified one PsTPS-3car gene (PsTPS-3car1) that is expressed in both genotypes, one gene (PsTPS-3car2) that is expressed only in resistant trees, and one gene (PsTPS-3car3) that is expressed only in susceptible trees. The PsTPS-3car2 gene was not detected in genomic DNA of susceptible trees. Target-specific selected reaction monitoring confirmed this pattern of differential expression of members of the PsTPS-3car family at the proteome level. Kinetic characterization of the recombinant PsTPS-3car enzymes identified differences in the activities of PsTPS-3car2 and PsTPS-3car3 as a factor contributing to the different (+)-3-carene profiles of resistant and susceptible trees. In conclusion, variation of the (+)-3-carene phenotype is controlled by copy number variation of PsTPS-3car genes, variation of gene and protein expression, and variation in catalytic efficiencies.
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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".