Smurf2 Participates in Human Trophoblast Cell Invasion by Inhibiting TGF-β Type I Receptor
Bibliographic record
Abstract
Successful embryo implantation depends on the ability of the trophoblast cells to invade the endometrium and the receptivity of the endometrium. Unlike tumor invasion, trophoblast invasion is spatio-temporaly restricted. Transforming growth factor (TGF)-beta is a key inhibitory factor in the invasion of early trophoblast cells. Smad ubiquitination regulatory factor 2 (Smurf2), a HECT type E3 ubiquitin ligase, is an important regulator of the TGF-beta signaling pathway, targeting TGF-beta receptors and various Smads for proteasome-mediated degradation. In this context, we wished to determine whether Smurf2 has a physiological role during embryo implantation, especially in trophoblast invasion. We examined the spatio-temporal expression of Smurf2 in human placental villi and the function of Smurf2 in trophoblast cell migration and invasion in a model system involving a human extravillous trophoblast cell line, HTR-8/SVneo. Results from RT-PCR and immunohistochemical studies showed that expression of Smurf2 in placental villi was the highest during the first trimester and decreased as the pregnancy progressed. Overexpression of Smurf2 in HTR-8/SVneo cells reduced TGF-beta type I receptor levels, and enhanced cell migration and invasion. Conversely, RNA interference-mediated downregulation of Smurf2 resulted in a significant increase in TGF-beta type I receptor protein levels. However, the levels of Smad2, another potential target of Smurf2, remained unchanged. In conclusion, the present study suggests that Smurf2 promotes trophoblast cell migration and invasion, and this function may involve downregulation of TGF-beta type I receptor.
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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".