Transcription, Alternative Splicing, and Post-Translational Regulation of CaLOXs in the Dynamic Regulation of Jasmonate Levels in Wounded Pepper Leaves
Bibliographic record
Abstract
In response to stresses, jasmonates increase rapidly, leading to plant resistance against necrotrophic pathogens and chewing insect herbivores. Jasmonate biosynthesis is regulated at many levels, including transcriptionally, through alternative splicing, and the phosphorylation of the 13S-lipoxygenase (LOX) that catalyzes an early step in jasmonate biosynthesis. In pepper, transcriptomic analysis of a foliar wounding time course was conducted to deepen our understanding of these regulatory mechanisms. All four CaLOXs are constitutively expressed. CaLOX2, which encodes an enzyme with a Ser in a predicted regulatory phosphosite, shows a rapid but short-lived increase in wound-induced expression. In contrast, CaLOX7, which encodes a protein with a non-phosphorylatable Ala at the phosphosite, shows higher wound-induced expression at 6 h. As well, at this timepoint, there is a predicted increase in exon 4 retention in CaLOX8 transcripts in wounded plants. ChimeraX protein modeling predicts that the retention of exon 4 may negatively affect enzyme activity, possibly by blocking access to the enzyme’s active site. The transcription, alternative splicing, and post-translational regulation of CaLOX enzymes support the dynamic fluctuations observed in the jasmonates, which increase rapidly upon wounding and return to basal levels at 6 h post-stress.
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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) | 0.000 | 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".