Expression profiling across wild and cultivated tomatoes supports the relevance of early miR482/2118 suppression for <i>Phytophthora</i> resistance
Bibliographic record
Abstract
Plants possess a battery of specific pathogen resistance ( R- )genes. Precise R- gene regulation is important in the presence and absence of a pathogen. Recently, a microRNA family, miR482/2118, was shown to regulate the expression of a major class of R- genes , nucleotide-binding site leucine-rich repeats ( NBS-LRRs ). Furthermore, RNA silencing suppressor proteins, secreted by pathogens, prevent the accumulation of miR482/2118, leading to an upregulation of R- genes. Despite this transcriptional release of R -genes, RNA silencing suppressors positively contribute to the virulence of some pathogens . To investigate this paradox, we analysed how the regulation of NBS-LRRs by miR482/2118 has been shaped by the coevolution between Phytophthora infestans and cultivated and wild tomatoes. We used degradome analyses and qRT-PCR to evaluate and quantify the co-expression of miR482/2118 and their NBS-LRR targets. Our data show that miR482/2118-mediated targeting contributes to the regulation of NBS-LRRs in Solanum lycopersicum. Based on miR482/2118 expression profiling in two additional tomato species—with different coevolutionary histories with P. infestans —we hypothesize that pathogen-mediated RNA silencing suppression is most effective in the interaction between S. lycopersicum and P. infestans . Furthermore, an upregulation of miR482/2118 early in the infection may increase susceptibility to P. infestans .
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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".