Dynamics and origin of cytokinins involved in plant manipulation by a leaf‐mining insect
Bibliographic record
Abstract
Several herbivorous insects and plant-associated microorganisms control the phytohormonal balance, thus enabling them to successfully exploit the plant by inhibiting plant defenses and withdrawing plant resources for their own benefit. The leaf-mining moth Phyllonorycter blancardella modifies the cytokinin (CK) profile of mined leaf-tissues, and the insect symbiotic bacteria Wolbachia is involved in the plant manipulation to the benefit of the insect host. To gain a deeper understanding into the possible origin and dynamics of CKs, we conducted an extensive characterization of CKs in larvae and in infected apple leaves. Our results show the enhanced CK levels in mines, both on green and yellow leaves, allowing insects to control their nutritional supply under fluctuating environmental conditions. The spatial distribution of CKs within the mined leaves shows that hormone manipulation is strictly limited to the mine suggesting the absence of CK translocation from distant leaf areas toward the insect feeding site. Mass spectrometry analyses reveal that major CK types accumulating in mines and larvae are similar to what is observed for most gall-inducers, suggesting that strategies underlying the plant manipulation may be shared between herbivorous insects with distinct life histories. Results further show that CKs are detected in the highest levels in larvae, reinforcing our hypothesis that CKs accumulating in the mines originate from the insect itself. Presence of bacteria-specific methylthio-CKs is consistent with previous results suggesting that insect bacterial symbionts contribute to the observed phenotype. Our study provides key findings toward the understanding of molecular mechanisms underlying this intricate plant-insect-microbe interaction.
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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".