Screening of reliable reference genes for the normalization of RT-qPCR in chicken liver tissues and LMH cells
Bibliographic record
Abstract
The liver plays a vital role in lipid synthesis and metabolism in poultry. To study the functional genes more effectively, it is essential to screen of reliable reference genes in the chicken liver, including females, males, embryos, as well as the Leghorn Male Hepatoma (LMH) cell line. Traditional reference gene screening involves selecting commonly used housekeeping genes (HKGs) for RT-qPCR experiments and using different algorithms to identify the most stable ones. However, this approach is limited in selecting the best reference gene from a small pool of HKGs. High-throughput sequencing technology may offer a solution to this limitation. This study aimed to identify the most consistently expressed genes by utilizing multiple published RNA-seq data of chicken liver and LMH cells. Subsequently, the stability of the newly identified reference genes was assessed in comparison to previously validated stable poultry liver expressed reference genes and the commonly employed HKGs using RT-qPCR. The findings indicated that there is a higher degree of similarity in stable expression genes between female and male liver (such as LSM14A and CDC40). In embryonic liver, the optimal new reference genes were SUDS3, TRIM33, and ERAL1. For LMH cells, the optimal new reference genes were ALDH9A1, UGGT1, and C21H1orf174. However, it is noteworthy that most HKGs did not exhibit stable expression across multiple samples, indicating potential instability under diverse conditions. Furthermore, RT-qPCR experiments proved that the stable expression genes identified from RNA-seq data outperformed commonly used HKGs and certain validated reference genes specific to poultry liver. Over all, this study successfully identified new stable reference genes in chicken liver and LMH cells using RNA-seq data, offering researchers a wider range of reference gene options for RT-qPCR in diverse situations.
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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 | 0.002 |
| 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 | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".