Both mitogen-activated protein kinase and phosphatidylinositol 3-kinase signalling are required in epidermal growth factor-induced human trophoblast migration
Bibliographic record
Abstract
Adequate extravillous trophoblast (EVT) invasion is an essential step for placental formation. The aim of this study was to examine the possible role of phosphatidylinositol 3-kinase (PI3K) and mitogen-activated protein kinase (MAPK) signalling in epidermal growth factor (EGF)-induced EVT migration and to determine if the 70 kDa ribosomal S6 kinase (p70S6K) is involved in this process. In this study, EGF significantly stimulated HTR8/SVneo cell migration and the phosphorylation of AKT, ERK1/2 and p70S6K in a concentration-dependent manner. The MAPK inhibitor U0126 decreased cell migration and ERK phosphorylation, but it did not influence p70S6K phosphorylation in response to EGF. In the presence of PI3K inhibitors (Wortmannin), EGF-stimulated trophoblast migration and phosphorylation of AKT and P70S6K (Thr(389) and Thr(421)/Ser(424)) were decreased, while EGF-induced ERK phosphorylation was not affected. Expression of an activated AKT (Myr-AKT2) increased basal phospho-p70S6K (Thr(389) and Thr(421)/Ser(424)) content, but failed to stimulate cell migration. However, it induced cell migration in the presence of EGF and Wortmannin, in which both AKT and MAPK pathways were activated. In addition, there was a concentration-dependent inhibition of cell migration and p70S6K phosphorylation (Thr(389) and Thr(421)/Ser(424)) in the presence of Rapamycin, a specific inhibitor of the mammalian target of rapamycin (mTOR, a downstream of AKT). Taken together, our data suggest that EGF-induced trophoblast migration involves the coordinated regulation of both PI3K/AKT and MAPK signalling pathways. mTOR/p70S6K is important in PI3K- but not MAPK-mediated trophoblast migration in response to EGF.
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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".