Stability of Reference Genes for Messenger RNA Quantification by Real-Time PCR in Mouse Dextran Sodium Sulfate Experimental Colitis
Bibliographic record
Abstract
BACKGROUND: Many animal models have been developed to characterize the complexity of colonic inflammation. In dextran sodium sulfate (DSS) experimental colitis in mice the choice of reference genes is critical for accurate quantification of target genes using quantitative real time PCR (RT-qPCR). No studies have addressed the performance of reference genes in mice DSS-experimental colitis. This study aimed to determine the stability of reference genes expression (RGE) in DSS-experimental murine colitis. METHODS: Colitis was induced in male C57BL/6 mice using DSS5% for 5 days, control group received water. RNA was extracted from inflamed and non-inflamed colon. Using RT-qPCR, comparative analysis of 13 RGE was performed according to predefined criteria and relative colonic TNF-α and IL-1β gene expression was determined by calculating the difference in the threshold cycle. RESULTS: Colitis significantly altered the stability of mucosal RGE. Commonly used glyceraldehyde-3-phosphate dehydrogenase (Gapdh), β-actin (Actb), or β2-microglobulin (β2m) showed the highest variability within the inflamed and control groups. Conversely, TATA-box-binding protein (Tbp) and eukaryotic translation elongation factor 2 (Eef2) were not affected by inflammation and were the most stable genes. Normalization of colonic TNF-α and IL-1β mRNA levels was dependent on the reference gene used. Depending on the genes used to normalize the data, statistical significance varied from significant when TBP / Eef2 were used to non-significant when Gapdh, Actb or β2m were used. CONCLUSIONS: This study highlights the appropriate choice of RGE to ensure adequate normalization of RT-qPCR data when using this model. Suboptimal RGE may explain controversial results from published studies. We recommend using Tbp and Eef2 instead of Gapdh, Actb or β2m as reference genes.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".