MicroRNA regulation of murine trophoblast stem cell self-renewal and differentiation
Bibliographic record
Abstract
Proper placentation is fundamental to successful pregnancy. Placenta arises from differentiation of trophoblast stem (TS) cells during development. Despite being recognized as the counterpart of ES cells in placental development, the role of regulatory miRNAs in TS cell differentiation remains inadequately explored. Here, we have identified complete repertoire of microRNAs present in mouse trophoblast cells in proliferative and differentiated state. We demonstrated that two miRNA clusters, -290 and -322, displayed reciprocal expression during trophoblast differentiation. Loss of miR-290 cluster members or gain in miR-322 cluster members led to differentiation of TS cells. The trophoblast stemness factor, CDX2, transactivated the miR-290 cluster andCyclin D1. MiR-290 cluster members repressed cell cycle repressors, P21, P27, WEE1, RBL2, and E2F7, in TS cells. MiR-322 cluster members repressed the cell cycle activators, CYCLIN D1, CYCLIN E1, CDC25B, and CDX2, to induce differentiation. Taken together, our findings highlight the importance of posttranscriptional regulation by conserved miRNA clusters that form a regulatory network with CDX2, cell cycle activators, and repressors in equipoising TS cell self-renewal and differentiation.
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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".