RNA interference as a tool to study gene function in bovine oocytes
Bibliographic record
Abstract
RNA interference (RNAi) has become a well-established technique to study gene function in several species. Our objective was to develop a RNAi approach to study gene function in bovine oocytes. In the first experiment, three different treatments including a 20 min exposure to cytochalasin B, a 6 hr maturation in cycloheximide, and a combination of these two treatments were tested to improve oocyte survival following microinjection. The cycloheximide/cytochalasin B treatment greatly increased (P<0.02) the survival rate of the microinjected oocytes. In the second experiment, we assessed the effect of both cyclin B1 and GFP dsRNA on cyclin B1 mRNA and protein expression. The injection of cyclin B1 dsRNA resulted in a decrease in cyclin B1 mRNA and protein, while the cyclin B2 mRNA remained unaffected. Furthermore, the injection of GFP dsRNA did not interfere with cyclin B1 mRNA or protein nor with the ability of the oocyte to mature properly. In addition, the lack of cyclin B1 in the oocyte led to activation in 10% of the oocytes as evidenced by the presence of a pronucleus. However, the use of an additional 10 hr of maturation in the presence of 6-dimethylaminopurine (6-DMAP) prevented germinal vesicle breakdown and allowed a longer exposure to dsRNA. This procedure increased the percentage of activated oocytes to 33% and is likely to result from an increased length of time for dsRNA processing and for degradation of the cyclin B1 mRNA to occur. In conclusion, RNAi represents a useful technique to study gene function in the bovine oocyte.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".