Neonatal T Helper 17 Responses Are Skewed Towards an Immunoregulatory Interleukin-22 Phenotype
Bibliographic record
Abstract
Newborns are frequently affected by mucocutaneous candidiasis. Th17 cells essentially limit mucosal invasion by commensal Candida spp. Here, we sought to understand the molecular basis for the developmental lack of Th17 cell responses in circulating blood neonatal T cells. Naive cord blood CD4 T cells stimulated in Th17-differentiating conditions inherently produced high levels of the interleukin-22 immunoregulatory cytokine, particularly in the presence of neonatal antigen-presenting cells. A genome-wide transcriptome analysis comparing neonatal and adult naïve CD4 T cells ex vivo revealed major developmental differences in gene networks regulating Small Drosophila Mothers Against Decapentaplegic (SMAD) and Signal Transducer and Activator of Transcription 3 (STAT3) signaling. These changes were functionally validated by experiments showing that the requirement for TGF-β in human Th17 cell differentiation is age-dependent. Moreover, STAT3 activity was profoundly diminished while overexpression of the STAT3 gene restored Th17 cell differentiation capacity in neonatal T cells. These data reveal that Th17 cell responses are developmentally regulated at the gene expression level in human neonates. These developmental changes may protect newborns against pathological Th17 cell responses, at the same time increasing their susceptibility to mucocutaneous candidiasis.
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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) | 0.002 | 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".