Reference Gene Selection for Accurate RT-qPCR Normalization in Four Tissues and Whole-Body Samples of <i>Acheta domesticus</i>
Bibliographic record
Abstract
House crickets (Acheta domesticus) are increasingly recognized as a sustainable protein source for food and feed systems. However, despite their growing relevance, molecular research on this species remains extremely limited, particularly concerning robust normalization strategies for gene expression analysis. This study is the first to identify and validate suitable reference genes for RT-qPCR analysis in A. domesticus across different tissues, an essential step for accurate quantification of host and pathogen target gene expression. Six candidate reference genes commonly used in insects (AdoNEOPT, EF2, 18S rRNA, EF1α, Histone H3, and GAPDH) were evaluated for expression stability in five tissue types (abdomen, legs, wings, head, and whole body). Gene stability was assessed using five computational tools: BestKeeper, geNorm, NormFinder, Delta Ct, and the integrated platform RefFinder. Additional validation was performed using the R statistical software. The results revealed tissue-specific variation in the ranking of reference genes across different algorithms. However, despite these differences in ranking, several candidate genes consistently met established stability criteria, indicating stable expression across tissues. In particular, EF1α, AdoNEOPT, EF2, and 18SrRNA demonstrated reliable stability, whereas GAPDH and HisH3 showed higher variability and were generally unsuitable, except for GAPDH in head tissue. Our data show that differences in gene ranking across tissues and analytical methods reflect biological and methodological variability rather than true instability. This study provides the first validated set of reference genes for A. domesticus, supporting accurate gene expression analysis and the development of reliable RT-qPCR-based diagnostic tools for improved health monitoring and biosafety in insect farming.
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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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".