Molecular analysis of nematode-responsive defence genes <i>CRF1</i>, <i>WRKY45</i>, and <i>PR7</i> in <i>Solanum lycopersicum</i> tissues during the infection of plant-parasitic nematode species of the genus <i>Meloidogyne</i>
Bibliographic record
Abstract
Several pathogens, including nematodes, have severe effects on plant development and growth, and immense populations of parasitic nematodes may cause plant death and crop loss. Obligate plant-parasitic nematodes and root-knot nematodes belonging to the genus Meloidogyne are significant parasites in crops. During nematode infection, damage-associated molecular patterns play a role in the activation of plant defence responses to pathogens. Several genes are involved in Meloidogyne parasitism. However, the expression of nematode-responsive genes CRF1, WRKY45, and PR7 during infection with different parasitic nematode species is not well understood. Therefore, this study aimed to reveal plant responses to differential gene expression of nematode-responsive genes in tomato plants, and their relationship to nematode reproduction and comparative phylogeny. Molecular methods for gene expression, greenhouse work for nematode reproduction, and phylogenetic analysis were used to determine nematode–plant interactions. The results revealed that differential gene expression of CRF1, WRKY45, and PR7 depended on the nematode species. The relative CRF1 gene expression reached its highest level at 3 dpi, following nematode infection. In conclusion, plant defense responses disturbed the expression of nematode-responsive genes, and the differential expression of nematode-responsive genes was affected by nematode species and nematode parasitism.
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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 | 0.001 |
| 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".