Transcriptome profiling of bovine inner cell mass and trophectoderm derived from in vivo generated blastocysts
Bibliographic record
Abstract
BACKGROUND: This study describes the generation and analysis of the transcriptional profile of bovine inner cell mass (ICM) and trophectoderm (TE), obtained from in vivo developed embryos by using a bovine-embryo specific array (EmbryoGENE) containing 37,238 probes. RESULTS: A total of 4,689 probes were differentially expressed between ICM and TE, among these, 2,380 and 2,309 probes were upregulated in ICM and TE tissues, respectively (P ≤ 0.01, FC ≥ 2.0, FDR: 2.0). Ontological classification of the genes predominantly expressed in ICM emerged a range of functional categories with a preponderance of genes involved in basal and developmental signaling pathways including P53, TGFβ, IL8, mTOR, integrin, ILK, and ELF2 signalings. Cross-referencing of microarray data with two available in vitro studies indicated a marked reduction in ICM vs. TE transcriptional difference following in vitro culture of bovine embryos. Moreover, a great majority of genes that were found to be misregulated following in vitro culture of bovine embryos were known genes involved in epigenetic regulation of pluripotency and cell differentiation including DNMT1, GADD45, CARM1, ELF5 HDAC8, CCNB1, KDM6A, PRDM9, CDX2, ARID3A, IL6, GADD45A, FGFR2, PPP2R2B, and SMARCA2. Cross-species referencing of microarray data revealed substantial divergence between bovine and mouse and human in signaling pathways involved in early lineage specification. CONCLUSIONS: The transcriptional changes occur during ICM and TE lineages specification in bovine is greater than previously understood. Therefore, this array data establishes a standard to evaluate the in vitro imprint on the transcriptome and to hypothesize the cross-species differences that allow in vitro acquisition of pluripotent ICM in human and mice but hinder that process in bovine.
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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 | 0.000 | 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) | 0.001 | 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".