Kisspeptin/KISS1R signaling potentiates extravillous trophoblast adhesion to type‐I collagen in a PKC‐ and ERK1/2‐dependent manner
Bibliographic record
Abstract
During the first trimester of human pregnancy, cytotrophoblasts proliferate within the tips of the chorionic villi to form cell columns that anchor the placenta to the uterus. This migration coincides with a widespread change in the adhesion molecule repertoire of these trophoblasts. Kisspeptin and its receptor, KISS1R, are best known as potent triggers of gonadotropin-releasing hormone secretion. The kisspeptin/KISS1R signaling system is also highly expressed in the human placenta, where it was demonstrated to inhibit extra-villous trophoblast (EVT) migration and invasion in vitro. Here we show that kisspeptin, in a dose- and time-dependent manner, induces increased adhesion of human EVTs to type-I collagen, a major component of the human placenta. This increased adhesion was both rapid and transient, suggesting that it likely occurred through the activation of KISS1R secondary effectors such as PKC and ERK, which underwent rapid and transient kisspeptin-dependent activation in EVTs. We then showed that inhibition of both PKC and ERK1/2 attenuated the kisspeptin-dependent increase in EVT adhesion, suggesting that these molecules are key positive regulators of trophoblast adhesion. We therefore propose that kisspeptin/KISS1R signaling potentiates EVT adhesion to type-I collagen via "inside-out signaling." Furthermore, kisspeptin treatment increased mouse blastocyst adhesion to collagen I, suggesting that kisspeptin signaling is a key regulator of trophoblast function during implantation as well as early placentation.
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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".