MicroRNAs in plant-parasitic nematodes: what are they and why should we care?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 it