Selection of reference genes for expression analysis of plant-derived microRNAs in Plutella xylostella using qRT-PCR and ddPCR
Bibliographic record
Abstract
The establishment of an expression quantification system that can be easily applied for the comparison of microRNAs (miRNAs) from biological samples is an important step toward understanding functional mechanisms in organisms. However, there is lack of attention on the selection of reference genes for miRNA expression profiling in insect herbivores. Here, we explored the candidate reference genes in a notorious pest of cruciferous crops, Plutella xylostella, for normalization of miRNA expression in developmental stages and tissues and in response to a change of food source from artificial diet to host plant Arabidopsis thaliana. We first compared the expression levels and stability of eight small RNAs using qRT-PCR, and found that miR11 was the most suitable reference gene for expression quantification of the miRNAs. We then confirmed this finding using digital droplet PCR and further validated with a well-studied cross-kingdom miRNA derived from A. thaliana (ath-miR159a). However, none of the reference genes was applicable for all experimental conditions, and multiple reference genes were sometimes required within the same experiment. Our work provides a method for the selection of reference genes for quantification of plant-derived miRNAs, which paves the way for unveiling their roles in the insect-plant coevolution.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".