Inhibition of Phosphoinositide-3-Kinase Signaling Promotes the Stem Cell State of Trophoblast
Bibliographic record
Abstract
Trophoblast stem cells (TSCs) are a heterogeneous cell population despite the presence of fibroblast growth factor (FGF) and transforming growth factor β (TGFB) as key growth factors in standard culture conditions. To understand what other signaling cascades control the stem cell state of mouse TSCs, we performed a kinase inhibitor screen and identified several novel pathways that cause TSC differentiation. Surprisingly, inhibition of phosphoinositide-3-kinase (PI3K) signaling increased the mRNA and protein expression of stem cell markers instead, and resulted in a tighter epithelial colony morphology and fewer differentiated cells. PI3K inhibition could not substitute for FGF or TGFB and did not affect phosphorylation of extracellular signal-regulated kinase, and thus acts independently of these pathways. Upon removal of PI3K inhibition, TSC transcription factor levels reverted to normal TSC levels, indicating that murine TSCs can reversibly switch between these two states. In summary, PI3K inhibition reduces the heterogeneity and seemingly heightens the stem cell state of TSCs as indicated by the simultaneous upregulation of multiple key marker genes and cell morphology. Stem Cells 2019;37:1307-1318.
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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".