Assessment of Requirements for IL-15 and IFN Regulatory Factors in Uterine NK Cell Differentiation and Function During Pregnancy
Bibliographic record
Abstract
In mouse and human, precursors of NK cell lineage home to decidualizing uteri. To assess the requirement for IL-15, an essential cytokine for NK differentiation in lymphoid tissue, on uterine NK (uNK) cell differentiation, implantation sites from IL-15(-/-) mice were analyzed histologically. IL-15(-/-) implantation sites had no uNK cells, no spiral-artery modification, and lacked the decidual integrity found in normal mice. IL-15(-/-) recipients of C57BL/6 marrow displayed similar pathology. However, implantation sites from recombination-activating gene-2(-/-)gamma(c)(-/-) (alymphoid) recipients of IL-15(-/-) marrow showed normal uNK cells, modified spiral arteries, and well-developed decidua basalis. Deletion of the IFN-regulatory factor (IRF)-1, but not IRF-2 (factors important in peripheral NK cell differentiation) limited but did not prevent uNK cell development. In situ hybridization localized IRF-1 largely to placental trophoblast cells. IRF-1(-/-) marrow transplanted into recombination-activating gene-2(-/-)gamma(c)(-/-) displayed competence for full uNK cell differentiation. IL-15 mRNA expression at implantation sites of IRF-1(-/-) and C57BL/6 was similar, suggesting that, unlike in bone marrow and spleen, IRF-1 does not regulate IL-15 in the pregnant uterus. Terminal differentiation of uNK cells was not promoted in pregnant IRF-1(-/-) mice by 5-day infusion of murine rIL-15, suggesting that IRF-1 deficiency rather than IL-15 deficiency limits uNK cell differentiation in these mice. Further, IRF-1 regulates placental growth, birth weight, and postnatal growth of offspring. These studies indicate that uNK cell development and maturation share some aspects with NK cell development in other tissues, but also display distinctive tissue-specific regulation.
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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".