Optimisation of Reference Genes for Gene-Expression Analysis in a Rabbit Model of Left Ventricular Diastolic Dysfunction
Bibliographic record
Abstract
Left ventricular diastolic dysfunction (LVDD) is characterized by the disturbance of ventricle's performance due to its abnormal relaxation or to its increased stiffness during the diastolic phase. The molecular mechanisms underlying LVDD remain unknown. We aimed to identify normalization genes for accurate gene-expression analysis of LVDD using quantitative real-time PCR (RT-PCR) in a new rabbit model of LVDD. Eighteen rabbits were fed with a normal diet (n = 7) or a 0.5% cholesterol-enriched diet supplemented with vitamin D2 (n = 11) for an average of 14.5 weeks. We validated the presence of LVDD in this model using echocardiography for diastolic function assessment. RT-PCR was performed using cDNA derived from left ventricle samples to measure the stability of 10 genes as candidate reference genes (Gapdh, Hprt1, Ppia, Sdha, Rpl5, Actb, Eef1e1, Ywhaz, Pgk1, and G6pd). Using geNorm analysis, we report that Sdha, Gapdh and Hprt1 genes had the highest stability (M <0.2). By contrast, Hprt1 and Rpl5 genes were found to represent the best combination for normalization when using the Normfinder algorithm (stability value of 0.042). Comparison of both normalization strategies highlighted an increase of natriuretic peptides (Bnp and Anp), monocytes chemotactic protein-1 (Mcp-1) and NADPH oxidase subunit (Nox-2) mRNA expressions in ventricle samples of the hypercholesterolemic rabbits compared to controls (P<0.05). This increase correlates with LVDD echocardiographic parameters and most importantly it molecularly validates the presence of the disease in our model. This is the first study emphasizing the selection of stable reference genes for RT-PCR normalization in a rabbit model of LVDD.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".