Global analysis of FSH‐regulated gene expression and histone modification in mouse granulosa cells
Bibliographic record
Abstract
Follicle-stimulating hormone (FSH) regulates ovarian follicular development through a specific gene expression program. We analyzed FSH-regulated transcriptome and histone modification in granulosa cells during follicular development. We used super-stimulated immature mice and collected granulosa cells before and 48 h after stimulation with equine chorionic gonadotropin (eCG). We profiled the transcriptome using RNA-sequencing (N = 3/time-point) and genome-wide trimethylation of lysine 4 of histone H3 (H3K4me3; an active transcription marker) using chromatin immunoprecipitation and sequencing (ChIP-Seq; N = 2/time-point). Across the mouse genome, 14,583 genes had an associated H3K4me3 peak and 63-66% of these peaks were observed within ≤1 kb promoter region. There were 72 genes with differential H3K4me3 modification at 48 h eCG (absolute log fold change > 1; false discovery rate [FDR] < 0.05) relative to 0 h eCG. Transcriptome data analysis showed 1463 differentially expressed genes at 48 h eCG (absolute log fold change > 1; FDR < 0.05). Among the 20 genes with differential expression and altered H3K4me3 modification, Lhcgr had higher H3K4me3 abundance and expression, while Nrip2 had lower H3K4me3 abundance and expression. Using ChIP-qPCR, we showed that FSH-regulated expression of Lhcgr, Cyp19a1, Nppc, and Nrip2 through regulation of H3K4me3 at their respective promoters. Transcript isoform analysis using Kallisto-Sleuth tool revealed 875 differentially expressed transcripts at 48 h eCG (b > 1; FDR < 0.05). Pathway analysis of RNA-seq data demonstrated that TGF-β signaling and steroidogenic pathways were regulated at 48 h eCG. Thus, FSH regulates gene expression in granulosa cells through multiple mechanisms namely altered H3K4me3 modification and inducing specific transcripts. These data form the basis for further studies investigating how these specific mechanisms regulate granulosa cell functions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".