CD161 contributes to prenatal immune suppression of IFN-γ–producing PLZF+ T cells
Bibliographic record
Abstract
BACKGROUND: While the human fetal immune system defaults to a program of tolerance, there is concurrent need for protective immunity to meet the antigenic challenges encountered after birth. Activation of T cells in utero is associated with the fetal inflammatory response with broad implications for the health of the fetus and of the pregnancy. However, the characteristics of the fetal effector T cells that contribute to this process are largely unknown. METHODS: We analyzed primary human fetal lymphoid and mucosal tissues and performed phenotypic, functional, and transcriptional analysis to identify T cells with pro-inflammatory potential. The frequency and function of fetal-specific effector T cells was assessed in the cord blood of infants with localized and systemic inflammatory pathologies and compared to healthy term controls. RESULTS: We identified a transcriptionally distinct population of CD4+ T cells characterized by expression of the transcription factor Promyelocytic Leukemia Zinc Finger (PLZF). PLZF+ CD4+ T cells were specifically enriched in the fetal intestine, possessed an effector memory phenotype, and rapidly produced pro-inflammatory cytokines. Engagement of the C-type lectin CD161 on these cells inhibited TCR-dependent production of IFNγ in a fetal-specific manner. IFNγ-producing PLZF+ CD4+ T cells were enriched in the cord blood of infants with gastroschisis, a natural model of chronic inflammation originating from the intestine, as well as in preterm birth, suggesting these cells contribute to fetal systemic immune activation. CONCLUSION: Our work reveals a fetal-specific program of protective immunity whose dysregulation is associated with fetal and neonatal inflammatory pathologies.
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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) | 0.003 | 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".