Apoplast proteome reveals that extracellular matrix contributes to multistress response in poplar
Bibliographic record
Abstract
BACKGROUND: Riverine ecosystems, highly sensitive to climate change and human activities, are characterized by rapid environmental change to fluctuating water levels and siltation, causing stress on their biological components. We have little understanding of mechanisms by which riverine plant species have developed adaptive strategies to cope with stress in dynamic environments while maintaining growth and development. RESULTS: We report that poplar (Populus spp.) has evolved a systems level "stress proteome" in the leaf-stem-root apoplast continuum to counter biotic and abiotic factors. To obtain apoplast proteins from P. deltoides, we developed pressure-chamber and water-displacement methods for leaves and stems, respectively. Analyses of 303 proteins and corresponding transcripts coupled with controlled experiments and bioinformatics demonstrate that poplar depends on constitutive and inducible factors to deal with water, pathogen, and oxidative stress. However, each apoplast possessed a unique set of proteins, indicating that response to stress is partly compartmentalized. Apoplast proteins that are involved in glycolysis, fermentation, and catabolism of sucrose and starch appear to enable poplar to grow normally under water stress. Pathogenesis-related proteins mediating water and pathogen stress in apoplast were particularly abundant and effective in suppressing growth of the most prevalent poplar pathogen Melampsora. Unexpectedly, we found diverse peroxidases that appear to be involved in stress-induced cell wall modification in apoplast, particularly during the growing season. Poplar developed a robust antioxidative system to buffer oxidation in stem apoplast. CONCLUSION: These findings suggest that multistress response in the apoplast constitutes an important adaptive trait for poplar to inhabit dynamic environments and is also a potential mechanism in other riverine plant species.
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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".