Validation of endogenous reference genes in Buglossoides arvensis for normalizing RT-qPCR-based gene expression data
Bibliographic record
Abstract
Selection of a stably expressed reference gene (RG) is an important step for generating reliable and reproducible quantitative real-time reverse transcription polymerase chain reaction (RT-qPCR) gene expression data. We, in this study, have sought to validate RGs for Buglossoides arvensis, a high nutraceutical value plant whose refined seed oil is entering the market under the commercial trade name Ahiflower™. This weed plant has received attention for its natural ability to significantly accumulate the poly-unsaturated fatty acid (PUFA) stearidonic acid (SDA, C18:4n-3) in its seeds, which is uncommon for most plant species. Ten candidate RGs (β-Act, 18S rRNA, EF-1a, α-Tub, UBQ, α-actin, CAC, PP2a, RUBISCO, GAPDH) were isolated from B. arvensis and TaqMan™ compliant primers/probes were designed for RT-qPCR analysis. Abundance of these gene transcripts was analyzed across different tissues and growth regimes. Two of the most widely used algorithms, geNorm and NormFinder, showed variation in expression levels of these RGs. However, combinatorial analysis of the results clearly identified CAC and α-actin as the most stable and unstable RG candidates, respectively. This study has for the first time identified and validated RGs in the non-model system B. arvensis, a weed plant projected to become an important yet sustainable source of dietary omega-3 PUFA.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".