Identification of stable reference genes in Edwardsiella ictaluri for accurate gene expression analysis
Bibliographic record
Abstract
Edwardsiella ictaluri is a Gram-negative bacterium causing enteric septicemia of catfish (ESC), leading to significant economic losses in the catfish farming industry. RT-PCR analysis is a powerful technique for quantifying gene expression, but normalization of expression data is critical to control experimental errors. Using stable reference genes, also known as housekeeping genes, is a common strategy for normalization, yet reference gene selection often lacks proper validation. In this work, our goal was to determine the most stable reference genes in E. ictaluri during catfish serum exposure and various growth phases. To this goal, we evaluated the expression of 27 classical reference genes (16S rRNA, abcZ, adk, arc, aroE, aspA, atpA, cyaA, dnaG, fumC, g6pd, gdhA, glnA, gltA, glyA, grpE, gyrB, mdh, mutS, pgi, pgm, pntA, recA, recP, rpoS, tkt, and tpi) using five analytical programs (GeNorm, BestKeeper, NormFinder, Comparative ΔCT, and Comprehensive Ranking). Results showed that aspA, atpA, dnaG, glyA, gyrB, mutS, recP, rpoS, tkt, and tpi were the most stable reference genes during serum exposure, whereas fumC, g6pd, gdhA, glnA, and mdh were the least stable. During various growth phases, aspA, g6pd, glyA, gyrB, mdh, mutS, pgm, recA, recP, and tkt were the most stable, while 16S rRNA, atpA, grpE, and tpi were the least stable. At least four analysis methods confirmed the stability of aspA, glyA, gyrB, mutS, recP, and tkt during serum exposure and different growth stages. However, no consensus was found among the programs for unstable reference genes under both conditions.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".