Blood stem cell fate regulation by Delta-1–mediated rewiring of IL-6 paracrine signaling
Bibliographic record
Abstract
Increasing evidence supports the importance of cell extrinsic regulation in stem cell fate control. Hematopoietic stem cells (HSC) are responsive to local signals from their niche and to systemic feedback from progenitors and mature cells. The Notch ligand Delta-1 (DL1), a key component of the stem cell niche, regulates human hematopoietic lineage development in a dose-dependent manner and has been used clinically for primitive progenitor expansion. How DL1 acts to regulate HSC fate and whether these actions are related to its lineage skewing effects are poorly understood. Here we demonstrate that, although DL1 activates signal transducer and activator of transcription 3 signaling similarly to the gp130-activating cytokine interleukin-6 (IL-6), it has opposite effects on myeloid cell production. Mechanistically, these different outcomes are attributable to a DL1-mediated reduction in membrane (m)-bound IL-6 receptor (R) expression, converting progenitor cells from being directly IL-6 responsive to requiring both IL-6 and soluble (s) IL-6R for activation. Concomitant reduction of both mIL-6R (by DL1 supplementation) and sIL-6R (using dynamically fed cultures) reduced myeloid cell production and led to enhanced outputs of human HSCs. This work describes a new mode of cytokine action in which DL1 changes cytokine receptor distributions on hematopoietic cells, altering feedback networks and their impact on stem cell fate.
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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".