Transcriptome-wide Identification and Characterization of microRNAs and Their Targets in a Highly Adaptable Conifer Platycladus orientalis
Bibliographic record
Abstract
MicroRNAs (miRNAs) are short noncoding RNAs (20–25 nucleotides) that regulate gene expression posttranscriptionally. However, identification and characterization of miRNAs remain limited for conifer species. In this study, we applied transcriptome-wide miRNAs sequencing to a conifer species Platycladus orientalis , which is highly adaptable to a wide range of environmental adversities, including drought, barren soil, and mild salinity. A total of 17,181,542 raw reads were obtained from the Illumina sequencing platform; 31 conserved and 91 novel miRNAs were identified, and their unique characteristics were further analyzed. Ten randomly selected miRNAs were validated by quantificational real-time polymerase chain reaction. Through miRNA target predictions based on psRNATarget, 2331 unique mRNAs were predicted to be targets of P. orientalis miRNAs that involved in 187 metabolic pathways in KEGG database. These targets included not only important transcription factors (e.g., class III homeodomain leucine zipper targeted by por-miR166d) but also indispensable nontranscriptional factor proteins (i.e., por-miR482a-3p regulated nucleotide-binding site leucine-rich repeat protein). Interestingly, six miRNAs (por-miR16, -miR44, -miR60-5p, -miR69–3p, -miR166b-5p, and -miR395c) were found in adaptation-related pathways (e.g., drought), indicating their possible involved in this species’ stress-tolerance characteristics. The present study provided essential information for understanding the regulatory role of miRNAs in P. orientalis and sheds light on their possible use in tree improvement for stress tolerance.
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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) | 0.000 | 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".