Regulatory T cells inhibit CD34+ cell differentiation into NK cells by blocking their proliferation
Bibliographic record
Abstract
Graft versus Host Disease (GvHD) remains one of the main complications after hematopoietic stem cell transplantation (HSCT). Due to their ability to suppress effector cells, regulatory T cells (Tregs) have been proposed as a cellular therapy to prevent GvHD, however they also inhibit the functions of natural killer (NK) cells, key effectors of the Graft versus Leukemia effect. In this study, we have explored whether a Tregs therapy will also impact on NK cell differentiation. Using an in vitro model of hematopoietic stem cell (HSC) differentiation into NK cells, we found that activated Tregs led to a 90% reduction in NK cell numbers when added at the time of commitment to the NK cell lineage. This effect was contact dependent and was reversible upon Tregs depletion. The few NK cells that developed in these cultures were mature and exhibited normal functions. Furthermore, adoptive transfer of activated Tregs in rag(-/-) γc(-/-) mice abrogated HSC differentiation into NK cells thus confirming our in vitro findings. Collectively, these results demonstrate for the first time that activated Tregs can inhibit NK cell differentiation from HSC under specific conditions.
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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".