Appropriateness of reference genes for normalizing messenger RNA in mouse 2,4-dinitrobenzene sulfonic acid (DNBS)-induced colitis using quantitative real time PCR
Bibliographic record
Abstract
2,4-Dinitrobenzene sulfonic acid (DNBS)-induced colitis is an experimental model that mimics Crohn's disease. Appropriateness of reference genes is crucial for RT-qPCR. This is the first study to determine the stability of reference gene expression (RGE) in mice treated with DNBS. DNBS experimental Colitis was induced in male C57BL/6 mice. RNA was extracted from colon tissue and comprehensive analysis of 13 RGE was performed according to predefined criteria. Relative colonic TNF-α and IL-1β mRNA levels were calculated. 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 fluctuation within the inflamed and control groups. Conversely, ribosomal protein large P0 (Rplp0), non-POU domain containing (Nono), TATA-box-binding protein (Tbp) and eukaryotic translation elongation factor 2 (Eef2) were not affected by inflammation and were the most stable genes. TNF-α and IL-1β mRNA levels was dependent on the reference gene used and varied from significant when the most stable genes were used to non-significant when the least stable genes were used. The appropriate choice of RGE is critical to guarantee satisfactory normalization of RT-qPCR data when using DNBS-Model. We recommend using Rplp0, Nono, Tbp, Hprt and Eef2 instead of common 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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".