Comparative Proteomics Analysis of Arabidopsis Phloem Exudates Collected During the Induction of Systemic Acquired Resistance
Bibliographic record
Abstract
Systemic acquired resistance (SAR) is a plant defense response that provides long-lasting, broad-spectrum pathogen resistance to uninfected systemic leaves following an initial localized infection. In Arabidopsis (Arabidopsis thaliana), local infection with virulent or avirulent strains of Pseudomonas syringae pv tomato generates long-distance SAR signals that travel from locally infected to distant leaves through the phloem to establish SAR In this study, a proteomics approach was used to identify proteins that accumulate in phloem exudates in response to the induction of SAR To accomplish this, phloem exudates collected from mock-inoculated or SAR-induced leaves of wild-type Columbia-0 plants were subjected to label-free quantitative liquid chromatography-tandem mass spectrometry proteomics. Comparing mock- and SAR-induced phloem exudate proteomes, 16 proteins were enriched in phloem exudates collected from SAR-induced plants, while 46 proteins were suppressed. SAR-related proteins THIOREDOXIN h3, ACYL-COENZYME A-BINDING PROTEIN6, and PATHOGENESIS-RELATED1 were enriched in phloem exudates of SAR-induced plants, demonstrating the strength of this approach and suggesting a role for these proteins in the phloem during SAR To identify novel components of SAR, transfer DNA mutants of differentially abundant phloem proteins were assayed for SAR competence. This analysis identified a number of new proteins (m-type thioredoxins, major latex protein-like protein, ULTRAVIOLET-B RESISTANCE8 photoreceptor) that contribute to the SAR response. The Arabidopsis SAR phloem proteome is a valuable resource for understanding SAR long-distance signaling and the dynamic nature of the phloem during plant-pathogen interactions.
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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 | 0.001 | 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 | 0.001 |
| 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".