Characterization of Eukaryotic Translation Initiation Factor 5A-2 (eIF5A-2) in <i>Arabidopsis thaliana</i>: Effects of Wounding and Pathogen Attack
Bibliographic record
Abstract
Plants respond to wounding and pathogen ingress by synthesizing defence proteins that facilitate wound healing, cell death or provide protection against further pathogen attack. The accumulation of these proteins is mainly due to rapid transcriptional activation of corresponding genes, though preliminary evidence implies that translational control may also have a role in the stress response. This study focuses on the function of Arabidopsis thaliana eukaryotic translation initiation factor 5A (eIF5A) in mechanical wounding and during infection with the phytopathogen Pseudomonas syringae. Recent evidence suggests that eIF5A acts as a nucleo-cytoplasmic shuttling protein that facilitates mRNA translation through selective transport from the nucleus. Three isoforms of eIF5A have been identified and isolated in Arabidopsis thaliana, suggesting that each distinct isoform may be involved in transport of different subsets of mRNA required for a specific physiological event. The analysis in this study revealed that the eIF5A-2 isoform appears to be involved in the signal transduction pathways that result in cell death following virulent infection, systemic acquired resistance and wounding in Arabidopsis thaliana. Transgenic plants with reduced expression of eIF5A-2 were developed using antisense T-DNA insertion. These lines have shown marked resistance to Pseudomonas syringae pv tomato DC3000, exhibiting up to 99% decrease of bacteria in planta and cell death associated with disease. The capacity to effectively limit growth of various pathogens is important for the design of strategies to improve disease resistance in crops. Development of disease-resistant lines allows efficient crop production with reduced reliance on environmentally undesirable toxic agrichemicals.
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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".