Quantitative iTRAQ proteome and comparative transcriptome analysis of elicitor‐induced Norway spruce (<b><i>Picea abies</i></b>) cells reveals elements of calcium signaling in the early conifer defense response
Bibliographic record
Abstract
Long-lived conifer trees depend on both constitutive and induced defenses for resistance against a myriad of potential pathogens and herbivores. In species of spruce (Picea spp.), several of the late events of pathogen-, insect-, or elicitor-induced defense responses have previously been characterized at the anatomical, biochemical, transcriptome, and proteome levels in stems and needles. However, accurately measuring the early events of induced cellular responses in a conifer is technically challenging due to limitations in the precise timing of induction and tissue sampling from intact trees following insect or fungal treatment. In the present study, we used the advantages of Norway spruce (Picea abies) cell suspensions combined with chitosan elicitation to investigate the early proteome response in a conifer. A combination of iTRAQ labeling and a new design of iterative sample analysis employing data-dependent exclusion lists were used for proteome analysis. This approach improved the coverage of the spruce proteome beyond that achieved in any prior study in a conifer system. Comparison of elicitor-induced proteome and transcriptome responses in Norway spruce cells consistently identified features associated with calcium-mediated signaling and response to oxidative stress that have not previously been observed in the response of intact trees to fungal attack.
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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".