Reference Gene Selection for Gene Expression Analysis of Oocytes Collected from Dairy Cattle and Buffaloes during Winter and Summer
Bibliographic record
Abstract
Oocytes from dairy cattle and buffaloes have severely compromised developmental competence during summer. While analysis of gene expression is a powerful technique for understanding the factors affecting developmental hindrance in oocytes, analysis by real-time reverse transcription PCR (RT-PCR) relies on the correct normalization by reference genes showing stable expression. Furthermore, several studies have found that genes commonly used as reference standards do not behave as expected depending on cell type and experimental design. Hence, it is recommended to evaluate expression stability of candidate reference genes for a specific experimental condition before employing them as internal controls. In acknowledgment of the importance of seasonal effects on oocyte gene expression, the aim of this study was to evaluate the stability of expression levels of ten well-known reference genes (ACTB, GAPDH, GUSB, HIST1H2AG, HPRT1, PPIA, RPL15, SDHA, TBP and YWHAZ) using oocytes collected from different categories of dairy cattle and buffaloes during winter and summer. A normalization factor was provided for cattle (RPL15, PPIA and GUSB) and buffaloes (YWHAZ, GUSB and GAPDH) based on the expression of the three most stable reference genes in each species. Normalization of non-reference target genes by these reference genes was shown to be considerably different from normalization by less stable reference genes, further highlighting the need for careful selection of internal controls. Therefore, due to the high variability of reference genes among experimental groups, we conclude that data normalized by internal controls can be misleading and should be compared to not normalized data or to data normalized by an external control in order to better interpret the biological relevance of gene expression analysis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".