Gene Expression Profiling of Differentially Expressed Genes in Granulosa Cells of Bovine Dominant Follicles Using Suppression Subtractive Hybridization1
Bibliographic record
Abstract
Development of antral follicles beyond 3 to 4 mm in cattle appears as a wave pattern that occurs two to three times during the estrous cycle. Each wave presents a cyclic recruitment of multiple follicles at the 3- to 4-mm stage, followed by the selection of a single follicle that becomes the dominant follicle (DF). The molecular determinants involved in the follicular dominance process remain poorly understood. The objective of the current study was to compare gene expression in granulosa cells (GCs) between growing dominant follicles from Day 5 of the estrous cycle and nonselected small follicles (≤4 mm) using the suppression subtractive hybridization (SSH) approach to identify candidate genes differentially expressed in GCs of the DF. Small follicle cDNAs were subtracted from DF cDNAs (DF-SF) and used to establish a DF GC-subtracted cDNA library. A total of 42 nonredundant cDNAs were identified. Detection of previously identified genes such as CX43, CYP19, INHBA, and SERPINE2 supported the validity of our experimental model and the use of SSH as the method of analysis. For selected genes such as ApoER2, CPD, CSPG2, 14-3-3 epsilon, NR5A2/SF2, RGN/SMP30, and SERPINE2, gene expression profiles were compared by virtual Northern blot or reverse transcriptase-polymerase chain reaction, and results confirmed an increase or induction of their mRNA in GCs of dominant follicles compared with that of small follicles. We conclude that we have identified novel genes (known and unknown) that are up-regulated in bovine GCs that may affect follicular growth, dominance, or both.
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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 | 0.000 |
| 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 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".