PP2Cδ (<i>Ppm1d</i>, WIP1), an endogenous inhibitor of p38 MAPK, is regulated along With <i>Trp53</i> and <i>Cdkn2a</i> following p38 MAPK inhibition during mouse preimplantation development
Bibliographic record
Abstract
Preimplantation embryos utilize mitogen-activated protein kinase signaling (MAPK) pathways to relay signals from the external environment to prepare appropriate responses and adaptations to a changing milieu. It is therefore important to investigate how MAPK pathways are regulated during preimplantation development. This study was conducted to investigate whether PP2Cdelta (Ppm1d, WIP1) is expressed during mouse preimplantation development and to determine the influences of p38 MAPK inhibition on expression of Trp53 (p53), Ppm1d, (WIP1), and Cdkn2a (p16) during mouse preimplantation development. Our results indicate that Trp53, Ppm1d, and Cdkn2a mRNAs and TRP53 and PP2Cdelta proteins are expressed throughout mouse preimplantation development. Treatment of 2-cell embryos with SB220025 (potent inhibitor of p38 MAPK alpha/beta/MAPK 14/11) significantly increased Trp53, Ppm1d and Cdkn2a and Mapk14 mRNA levels at 12 and 24 hr. Treatment of 8-cell embryos with SB220025 for 12 hr increased Trp53, Ppm1d, and Cdkn2a mRNA levels, but not Mapk14 mRNA levels. Treatment of 8-cell embryos for 24 hr increased Trp53, and Ppm1d mRNA levels, but decreased Cdkn2a and Mapk14 mRNA levels. Therefore, blockade of p38 MAPK activity is associated with embryo stage specific influences on Trp53, Ppm1d, Cdkn2a, and Mapk14 expression during mouse preimplantation development. These results define downstream targets of p38 MAPK during preimplantation development and indicate that the p38 MAPK pathway regulates Trp53, Ppm1d, and Cdkn2a expression. This study increases our understanding of the mechanisms controlling preimplantation development and of the interactions between preimplantation embryos and their culture environments.
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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 | 0.001 |
| 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".