Selection and validation of reliable reference genes for gene expression studies from Monilinia vaccinii-corymbosi infected wild blueberry phenotypes
Bibliographic record
Abstract
Monilinia blight disease caused by Monilinia vaccinii-corymbosi (Reade) Honey (M.vc) causes severe damage and economic losses in wild blueberry growing regions. Molecular mechanisms regulating defence responses of wild blueberry phenotypes towards this causal fungus are not yet fully known. A reliable quantification of gene expression using quantitative real time PCR (qPCR) is fundamental for measuring changes in target gene expression. A crucial aspect of accurate normalisation is the choice of appropriate reference genes. This study evaluated the expression stability of seven candidate reference genes (GAPDH, UBC9, UBC28, TIP41, CaCSa, PPR and RH8) in floral tissues of diploid and tetraploid wild blueberry phenotypes challenged with M.vc. The expression stability was calculated using five algorithms: geNorm, NormFinder, BestKeeper, deltaCt and RefFinder. The results indicated that UBC9 and GAPDH were the most stable reference genes, while RH8 and PPR were the least stable ones. To further validate the suitability of the analyzed reference genes, the expression level of a pathogenesis related protein gene (i.e., PR3) was analysed for both phenotypes at four time points of infection. Our results may be beneficial for future studies involving the quantification of relative gene expression levels in wild blueberry species.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".