Reduction of pluripotent gene expression in murine embryonic stem cells exposed to mechanical loading or Cyclo RGD peptide
Bibliographic record
Abstract
BACKGROUND: Self-renewal and differentiation of embryonic stem cells (ESCs) is directed by biological and/or physical cues that regulate multiple signaling cascades. We have previously shown that mESCs seeded in a type I collagen matrix demonstrate a loss of pluripotent marker expression and differentiate towards an osteogenic lineage. In this study, we examined if this effect was mediated in part through Arginylglycylaspartic acid (RGD) dependent integrin activity and/or mechano-transduction. RESULTS: The results from this study suggest that mESC interaction with the local microenvironment through RGD dependent integrins play a role in the regulation of mESC core transcription factors (TF), Oct-4, Sox 2 and Nanog. Disruption of this interaction with a cyclic RGD peptide (cRGDfC) was sufficient to mimic the effect of a mechanical stimulus in terms of pluripotent gene expression, specifically, we observed that supplementation with cRGDfC, or mechanical stimulus, significantly influenced mESC pluripotency by down-regulating core transcription factors. Moreover, our results indicated that the presence of the cRGDfC peptide inhibited integrin expression and up-regulated early lineage markers (mesoderm and ectoderm) in a Leukemia inhibitory factor (LIF) dependent manner. When cRGDfC treated mESCs were injected in Severe combined immunodeficiency (SCID) mice, no tissue growth and/or teratoma formation was observed, suggesting that the process of mESC tumor formation in vivo is potentially dependent on integrin interaction. CONCLUSIONS: Overall, the disruption of cell-integrin interaction via cRGDfC peptide can mimic the effect of mechanical stimulation on mESC pluripotency gene expression and also inhibit the tumorigenic potential of mESCs in vivo.
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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".