MicroRNAs in plant-parasitic nematodes: what are they and why should we care?
Bibliographic record
Abstract
Plant-parasitic nematodes (PPNs) establish intimate interactions with their host plants, leading to significant economic losses worldwide. The molecular mechanisms underlying parasitism are complex, requiring tight regulation of numerous genes. MicroRNAs (miRNAs), small non-coding RNAs, regulate gene expression at the post-transcriptional level by binding to target messenger RNAs. However, the diversity and functional roles of miRNAs in PPNs are only beginning to be uncovered. This review summarizes the current knowledge on the nature, biogenesis, functions, and trafficking of miRNAs in PPNs. Beyond advancing our understanding of gene regulation throughout the nematode life cycle and during parasitism, miRNA characterization holds significant promise for novel control strategies. Emerging evidence suggests that miRNAs may function across kingdoms, modulating gene expression in host plants during parasitic interactions. We highlight compelling examples from other pathosystems and discuss preliminary findings on miRNA-mediated communication between PPNs and their hosts. Finally, we provide an overview of the main computational tools and databases available for identifying and predicting miRNAs and their targets, aimed at supporting researchers interested in this emerging field.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".