Rho Guanosine 5′-Triphosphatases Differentially Regulate Insulin-Like Growth Factor I (IGF-I) Receptor-Dependent and -Independent Actions of IGF-II on Human Trophoblast Migration
Bibliographic record
Abstract
Both IGF-I and IGF-II stimulate migration of human extravillous trophoblast (EVT) cells. Although IGF-I is known to signal through IGF type 1 receptor (IGF1R), IGF-II signals through IGF1R as well as in an IGF1R-independent manner. The purpose of this study was to investigate the roles of Rho GTPases in IGF1R-independent and -dependent actions of IGF-II on EVT cell migration. To distinguish IGF1R-dependent and -independent actions, we used picropodophyllin, a selective inhibitor of IGF1R tyrosine kinase, and IGF analogs with differential affinities for IGF1R, IGF-II/cation-independent mannose 6-phosphate receptor, and IGF-binding proteins. IGF1R-dependent actions of IGF-II were confirmed by showing the effects of IGF1R-selective agonist Des1-3 IGF-I. We used pharmacological inhibitors or selective small interfering RNAs to investigate the roles of RhoA, RhoC, Rac1, Cdc42, and Rho effector kinases called ROCK-I and -II in IGF-induced EVT cell migration. Although basal migration of EVT cells required each member of the Rho GTPase family studied, IGF1R-dependent and -independent EVT cell migration exhibited differential requirements for these enzymes. IGF1R-mediated EVT cell migration was found to depend on RhoA and RhoC but not on Rac1 or Cdc42. However, IGF1R-independent effect of IGF-II on EVT cell migration required ROCKs but not RhoA, RhoC, Rac1, or Cdc42. Most importantly, IGF1R-independent action of IGF-II was found to be exaggerated when RhoA or RhoC was down-regulated. Thus, different members of the Rho GTPase family regulate IGF-II-mediated EVT cell migration differentially, depending upon whether it signals through IGF1R or in an IGF1R-independent manner.
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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".