Identification of appropriate reference genes for qPCR studies in Staphylococcus pseudintermedius and preliminary assessment of icaA gene expression in biofilm-embedded bacteria
Bibliographic record
Abstract
BACKGROUND: Quantitative PCR is rapidly becoming the standard method for analyzing gene expression in a wide variety of biological samples however it can suffer from significant error if stably expressed reference genes are not identified on which to base the analysis. Suitable reference genes for qPCR experiments on Staphylococcus pseudintermedius have yet to be identified. RESULTS: Three reference genes in S. pseudintermedius were identified and validated from a set of eight potential genes (proC, gyrB, rplD, rho, rpoA, ftsZ, recA, sodA). Two strains of S. pseudintermedius were used, and primer specificity and efficiency were confirmed and measured. Ranking of the genes with respect to expression stability revealed gyrB, rho and recA as the best reference genes. This combination was used to quantify expression of a single biofilm associated gene, icaA, in logarithmic, stationary and biofilm growth phases, revealing that expression was significantly upregulated in the biofilm growth phase in both strains. CONCLUSION: Three reference genes, gyrB, rho and recA, were identified and validated for use as reference genes for quantitative PCR experiments in S. pseudintermedius. Also, the biofilm associated gene icaA was shown to be significantly upregulated in biofilm samples, consistent with its role in biofilm production.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".