Transcriptome and Degradome of microRNAs and Their Targets in Response to Drought Stress in the Plants of a Diploid and Its Autotetraploid Paulownia australis
Bibliographic record
Abstract
MicroRNAs (miRNAs) are small, non-coding RNAs that play vital roles in plant growth, development, and stress response. Increasing numbers of studies aimed at discovering miRNAs and analyzing their functions in plants are being reported. In this study, we investigated the effect of drought stress on the expression of miRNAs and their targets in plants of a diploid and derived autotetraploid Paulownia australis. Four small RNA (sRNA) libraries and four degradome libraries were constructed from diploid and autotetraploid P. australis plants treated with either 75% or 25% relative soil water content. A total of 33 conserved and 104 novel miRNAs (processing precision value > 0.1) were identified, and 125 target genes were identified for 36 of the miRNAs by using the degradome sequencing. Among the identified miRNAs, 54 and 68 were differentially expressed in diploid and autotetraploid plants under drought stress (25% relative soil water content), respectively. The expressions of miRNAs and target genes were also validated by quantitative real-time PCR. The results showed that the relative expression trends of the randomly selected miRNAs were similar to the trends predicted by Illumina sequencing. And the correlations between miRNAs and their target genes were also analyzed. Furthermore, the functional analysis showed that most of these miRNAs and target genes were associated with plant development and environmental stress response. This study provided molecular evidence for the possible involvement of certain miRNAs in the drought response and/or tolerance in P. australis, and certain level of differential expression between diploid and autotetraploid 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 | 0.000 |
| 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) | 0.000 | 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".