Selection of Reference Genes for qPCR Analyses of Gene Expression in Ramie Leaves and Roots across Eleven Abiotic/Biotic Treatments
Bibliographic record
Abstract
Quantitative real-time PCR (qPCR) is commonly used for deciphering gene functions. For effective qPCR analyses, suitable reference genes are needed for normalization. The objective of this study is to identify the appropriate reference gene(s) for qPCR analyses of the leaves and roots of ramie (Boehmeria nivea L.), an important natural fiber crop. To accomplish this goal, we investigated the expression patterns of eight common plant qPCR reference genes in ramie leaves and roots under five abiotic stresses, five hormonal treatments, and one biotic stress. The relative expression stabilities of the eight genes were evaluated using four common but different approaches: geNorm, NormFinder, BestKeeper, and RefFinder. Across the 11 tested conditions, ACT1 was the most stably expressed among the eight genes while GAPDH displayed the biggest variation. Overall, while variations in the suggested reference genes were found for different tissue x treatment combinations, our analyses revealed that together, genes ACT1, CYP2, and UBQ can provide robust references for gene expression studies of ramie leaves under most conditions, while genes EF-1α, TUB, and ACT1 can be used for similar studies of ramie roots. Our results should help future functional studies of the genes in ramie genome across tissues and environmental conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".