Gata3 regulates the maturation program required for bone marrow exit and proliferation in Natural Killer cells (INM1P.435)
Bibliographic record
Abstract
Abstract Natural killer (NK) cells are innate lymphocytes capable of killing virally infected cells or transformed cells. NK cells differentiate in the bone marrow before they migrate out to peripheral organs. The developmental program leading to functionally mature NK cells has been studied in the context of several transcription factors. However, the role of GATA-3 in NK cell development has not been completely understood. Using NK-cell specific Gata3 knockout mice (NK-Gata3-/-), we demonstrated that GATA-3 is required for the NK cell maturation beyond the CD27 single positive stage and for efficient IFN-g production. The frequencies of NK cells from NK-Gata3-/- mice were found higher in the bone marrow but lower in peripheral organs compared to control littermates, indicating that GATA-3 controls the maturation program required for bone marrow egress. Despite this, upon MCMV infection, NK cells from NK-Gata3-/- mice were able to expand vigorously, achieving NK cell frequencies surpassing those in controls, and therefore provided comparable protection. The enhanced proliferation of GATA-3-deficient NK cells was associated with enhanced upregulation of CD25 expression. Thus, the NK-Gata3-/- mice serve as a model to study the NK cell developmental process linked to cell migration, effector functions and proliferation.
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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) | 0.002 | 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".