Integration of small RNAs, degradome, and transcriptome sequencing in <i>Populus</i> × <i>euramericana</i> “Neva” provides insights into the allelopathic interference of <i>para</i>-hydroxybenzoic acid
Bibliographic record
Abstract
Allelopathy is a hot topic of research; however, little is known regarding microRNA (miRNA) expression profiles in plants in response to allelochemicals. In this study, we combined the analyses of the transcriptome, small RNAs (sRNAs), and the degradome to identify key regulatory miRNA-targeted circuits under para-hydroxybenzoic acid (pHBA) stress. A total of 739 and 673 miRNAs were identified in leaves and roots, respectively. Of those, 214 and 148 miRNAs were significantly differentially expressed and identified as pHBA-responsive miRNAs in leaves and roots, respectively. The target genes for the pHBA-responsive miRNAs are involved in signal transduction, response to stress, and secondary metabolite pathways. Furthermore, an integrated analysis of the miRNA–target expression profiles was used to screen the 60 differentially expressed target genes from the 46 differentially expressed miRNAs in the leaves and the 51 differentially expressed target genes from the 36 differentially expressed miRNAs in roots. This integrated analysis revealed 17 and 30 pairs of miRNA targets in the leaves and roots, respectively, which had negatively correlated expression profiles. According to a real-time quantitative polymerase chain reaction (PCR) analysis, 14 miRNA–target pairs also exhibited negative correlations. Moreover, four coexpression regulatory networks were constructed based on the profiles of the differentially expressed miRNA–target pairs. These results suggest that comprehensive analyses of transcriptomes, sRNAs, and the degradome provide a useful platform for investigating the molecular mechanism underlying the pHBA-induced stress response in plants.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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 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".