A lentinan-loaded hydrogel with core-shell structure induces broad-spectrum resistance against plant virus by activating the expression of CML30
Bibliographic record
Abstract
Control of plant virus disease largely depends on the induced plant defense achieved by the external application of synthetic chemical inducers with the ability to modify defense-signaling pathways. However, most of the molecular mechanisms underlying these inducers remain unknown. Here, we developed a lentinan-loaded hydrogel with the core-shell structure and discovered how it protects plant from different virus infections. The hydrogel was synthesized by adding a chitosan shell on the surface of the sodium alginate-calcium ion-lentinan (LNT) hydrogel (SL-gel) to form CSL-gel. CSL-gel exhibits the capacity of prolonging the stable-release of lentinan and promoting calcium ions release. Application of CSL-gel on the root of plants significantly promotes plant growth and development and induces broad-spectrum resistance against TMV, TuMV, PVX and TRV. Furthermore, we found that the sustained release of calcium ions from the CSL-gel triggers the high expression of cal-modulin-like protein 30 (CML30), and silencing of CML30 enhances the susceptibility of tobacco to TMV. Our findings provide evidence that the novel and synthetic CSL-gel with the sustainable release of LNT and calcium ion strongly inhibits the plant virus infection. This study uncovers a novel mode of action by which CSL-gel with the stable release of calcium ion triggers CML30 expression.
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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".