Selection of reliable reference genes for quantitative real-time RT-PCR in alfalfa
Bibliographic record
Abstract
Real-time quantitative RT-PCR (qRT-PCR) is the most commonly used method for accurately detecting gene expression patterns. As part of qRT-PCR analysis, normalization of the data requires internal control gene(s) that display uniform expression under different biological conditions. However, no invariable internal control gene exists, and therefore more than one reference gene is needed to normalize RT-PCR results. In this study, we assessed the expression of eight candidate internal control genes, namely 18S ribosomal RNA (18S rRNA), elongation factor-1alpha, β-Actin, E2 ubiquitin-conjugating enzyme, β-Tubulin (TUB), ACTIN2, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and Msc27 of unknown function, in a diverse set of 16 alfalfa (Medicago sativa) samples representing different tissues and abiotic stress challenges, using geNorm and BestKeeper software. The results revealed that the eight candidate genes are inconsistently expressed under different experimental conditions. Msc27 and 18S rRNA are suitable reference genes for comparing different tissue types. Under different abscisic acid and NaCl conditions, three reference genes are necessary. Finally, GAPDH, TUB and β-Actin are unsuitable for normalization of qRT-PCR data under these given conditions in alfalfa. The relative expression level of MsWRKY33 was analyzed using selected reference genes. These results provide an experimental guideline for future research on gene expression in alfalfa using qRT-PCR.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".