Integration of the Pokeweed miRNA and mRNA Transcriptomes Reveals Targeting of Jasmonic Acid-Responsive Genes
Bibliographic record
Abstract
The American pokeweed plant, Phytolacca americana, displays broad-spectrum resistance to plant viruses and is a heavy metal hyperaccumulator. However, little is known about the regulation of biotic and abiotic stress responses in this non-model plant. To investigate the control of miRNAs in gene expression, we sequenced the small RNA transcriptome of pokeweed treated with jasmonic acid (JA), a hormone that mediates pathogen defense and stress tolerance. We predicted 145 miRNAs responsive to JA, most of which were unique to pokeweed. These miRNAs were low in abundance and condition-specific, with discrete expression change. Integration of paired mRNA-Seq expression data enabled us to identify correlated, novel JA-responsive targets that mediate hormone biosynthesis, signal transduction, and pathogen defense. The expression of approximately half the pairs was positively correlated, an uncommon finding that we functionally validated by mRNA cleavage. Importantly, we report that a pokeweed specific miRNA targets the transcript of OPR3, novel evidence that a miRNA regulates a JA biosynthesis enzyme. This first large-scale small RNA study of a Phytolaccaceae family member shows that miRNA-mediated control is a significant component of the JA response, associated with widespread changes in expression of genes required for stress adaptation.
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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".