Validation of Reference Genes for Accurate RT-qPCR Normalization in Aeluropus littoralis Under Drought, Cold, and ABA Treatments
Bibliographic record
Abstract
Halophyte plants, with genes responsive to abiotic stress, are promising candidates to enhance crop stress tolerance, but reliable RT-qPCR analysis requires the precise selection of candidate reference genes (CRGs) due to their inconsistent expression across tissues and stress conditions. In this study eight CRGs of A. littoralis, AlEF1A, AlRPS3, AlGTFC, AlRPS12, AlUBQ2, AlTUB6, AlACT7, and AlGAPDH1, were analyzed to assess their stability for the normalization of RT-qPCR data under polyethylene glycol (PEG, 20% w/v for drought simulation), abscisic acid (ABA, 100 μM), and cold stress (4 °C) treatments. The result of the algorithms suggested different CRGs for different treatments or tissue types. However the comprehensive analysis indicates that AlEF1A is the most stable CRG for PEG-treated leaf tissue, but AlTUB6 is preferable for PEG-treated root tissue, while for PEG-treated leaf and root tissues, AlEF1A can be suggested. For cold-stressed leaf and/or root samples, AlRPS3 was the most stable. For ABA-treated leaf and root tissues, AlGTFC and AlEF1A were the most stable CRGs, respectively, whereas AlTUB6 was suggested for ABA-treated leaf and root tissues. Collectively, for all stresses combined (PEG, ABA, and cold), AlGTFC was the most stable CRG in leaf samples, while AlRPS3 was the most stable in root samples and combined leaf and root samples. The validation analysis indicates a statistically significant difference (p value < 0.05) between normalization with the most and least stable CRGs. This research suggests reliable tissue-specific RGs for A. littoralis under abiotic stresses that can enhances the accuracy of gene expression quantification.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".