Blastocyst transfer in mice alters the placental transcriptome and growth
Bibliographic record
Abstract
Assisted reproduction technologies (ARTs) are becoming increasingly common. Therefore, how these procedures influence gene regulation and foeto-placental development are important to explore. Here, we assess the effects of blastocyst transfer on mouse placental growth and transcriptome. C57Bl/6 blastocysts were transferred into uteri of B6D2F1 pseudopregnant females and dissected at embryonic day 10.5 for analysis. Compared to non-transferred controls, placentas from transferred conceptuses weighed less even though the embryos were larger on average. This suggested a compensatory increase in placental efficiency. RNA sequencing of whole male placentas revealed 543 differentially expressed genes (DEGs) after blastocyst transfer: 188 and 355 genes were downregulated and upregulated, respectively. DEGs were independently validated in male and female placentas. Bioinformatic analyses revealed that DEGs represented expression in all major placental cell types and included genes that are critical for placenta development and/or function. Furthermore, the direction of transcriptional change in response to blastocyst transfer implied an adaptive response to improve placental function to maintain foetal growth. Our analysis revealed that CpG methylation at regulatory regions of two DEGs was unchanged in female transferred placentas and that DEGs had fewer gene-associated CpG islands (within ~20 kb region) compared to the larger genome. These data suggested that altered methylation at proximal promoter regions might not lead to transcriptional disruption in transferred placentas. Genomic clustering of some DEGs warrants further investigation of long-range, cis-acting epigenetic mechanisms including histone modifications together with DNA methylation. We conclude that embryo transfer, a protocol required for ART, significantly impacts the placental transcriptome and growth.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".