Ligand/Receptor Signaling Threshold (LIST) Model Accounts for gp130‐Mediated Embryonic Stem Cell Self‐Renewal Responses to LIF and HIL‐6
Bibliographic record
Abstract
We previously demonstrated that embryonic stem (ES) cell self-renewal required sustained signaling by leukemia inhibitory factor (LIF) in a concentration-dependent manner, allowing us to hypothesize that thresholds in ligand-receptor signaling modulate stem cell differentiation control. To test this hypothesis, we have experimentally and computationally compared the abilities of two gp130-signaling cytokines (LIF and Hyper-interleukin-6 [HIL-6]) to sustain ES cell self-renewal. Quantitative measurements of ES cell phenotypic markers (stage-specific embryonic antigen-1 and E-cadherin), functional assays (alkaline phosphatase activity and embryoid body formation efficiency), and transcription factor (Oct-4) expression over a range of LIF and HIL-6 concentrations demonstrated a superior ability of LIF to maintain ES cell pluripotentiality at higher concentrations (> or =500 pM). Additionally, we observed distinct qualitative differences in the ES cell self-renewal dose response profiles between the two cytokines. A computational model permitted calculation of the number of signaling complexes as a function of receptor expression, ligand concentration, and ligand/receptor-binding properties, generating predictions for the degree of self-renewal as a function of cytokine concentration by comparison of these calculated complex numbers to experimentally determined threshold cytokine concentrations. Model predictions, consistent with experimental data, indicated that differences in the potencies of these two cytokines were based primarily on differences in receptor-binding stoichiometries and properties. These results support a ligand/receptor signaling threshold model of ES cell fate modulation through appropriate types and levels of cytokine stimulation. Insights from these results may be more generally applicable to tissue-specific stem cells and could aid in the development of stem cell-based technologies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".