Differential Display and Suppressive Subtractive Hybridization Used to Identify Granulosa Cell Messenger RNA Associated with Bovine Oocyte Developmental Competence1
Bibliographic record
Abstract
The main objective of this study was to identify mRNA expressed in the granulosa cells characterizing differentiated follicles bearing developmentally competent bovine oocytes. Analytical comparisons were made on mRNA pools of granulosa cells using differential display reverse transcription polymerase chain reaction (DDRT) analysis and suppressive subtractive hybridization (SSH). With DDRT, mRNA patterns of granulosa cells from small (< 4 mm) and large (> 8 mm) follicles cultured in the presence or absence of LH were compared to identify mRNA associated with follicular size or with the LH response. Nine clones were sequenced, and two were identified. One of the clones, DRAK 1, was associated with the presence of LH in the medium. Other comparisons directed toward the identification of mRNA associated with the presence of a competent oocyte were done on granulosa cells collected in vivo from superstimulated heifers. With the DDRT analysis, four clones associated with the oocyte developmental competence status were identified. With the SSH analysis, four clones specific to the presence of an incompetent oocyte were sequenced and none were identified, whereas 49 clones specific to the presence of a competent oocyte were sequenced and 18 were identified. Among these clones, early growth response 1, sprouty 2, cytochrome C oxidase, matrix metalloproteinase inducer, matrix metalloproteinase, epiregulin, prostaglandin receptor, and progesterone receptor were the most relevant to the ovarian physiology being examined.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".