Validating reference genes within a mouse model system of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) toxicity
Bibliographic record
Abstract
BACKGROUND: Quantitative real-time PCR (qPCR) is the "gold-standard" technique for measuring mRNA abundances. To correctly compare samples and generate biologically valid results, qPCR data usually require comprehensive normalization to account for sample content variation between reactions. The most common normalization approaches use one or more endogenous controls (reference or house-keeping genes) to adjust the measured levels of experimental genes appropriately. Ideal reference genes are those that display minimal variation across experimental conditions, and thus can vary widely across different biological systems. In particular, toxicogenomic studies of transcriptionally-disruptive toxins, like 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), require careful consideration of reference genes. RESULTS: We examined seven candidate reference genes in 199 mice varying in genotype and time/dose of TCDD exposure. We assessed gene-stability in four ways: (1) the variance of the raw Cq values across biological replicates, (2) the fold-change from basal mRNA levels following treatment, (3) the inter- and intra-group stability evaluated using the NormFinder algorithm, (4) the comparative ΔCq method for each candidate gene. Univariate analyses showed Hprt and Eef1a1 are the two most stable individual reference genes. It has been suggested that using multiple genes would produce a more consistent normalization factor; multivariate analysis was performed using NormFinder. In general, stability increased with the number of genes used, but specific gene-combinations synergized. CONCLUSIONS: We have validated seven reference genes for use in analyzing mRNA abundances in mouse models of TCDD toxicity. The use of multiple reference genes increases stability, providing more consistent normalization and more reliable results. The number of reference genes used should be maximized, based on experimental capabilities (platform, sample availability, etc.). Our results show the benefit of validating reference genes using multiple methods prior to generating large biological datasets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".