Fungal sensing by dectin-1 directs the non-pathogenic polarization of TH17 cells through balanced type I IFN responses in human DCs
Bibliographic record
Abstract
Abstract The non-pathogenic T H 17 subset of helper T cells clears fungal infections, whereas pathogenic T H 17 cells cause inflammation and tissue damage; however, the mechanisms controlling these distinct responses remain unclear. Here we found that fungi sensing by the C-type lectin dectin-1 in human dendritic cells (DCs) directed the polarization of non-pathogenic T H 17 cells. Dectin-1 signaling triggered transient and intermediate expression of interferon (IFN)-β in DCs, which was mediated by the opposed activities of transcription factors IRF1 and IRF5. IFN-β-induced signaling led to integrin αvβ8 expression directly and to the release of the active form of the cytokine transforming growth factor (TGF)-β indirectly. Uncontrolled IFN-β responses as a result of IRF1 deficiency induced high expression of the IFN-stimulated gene BST2 in DCs and restrained TGF-β activation. Active TGF-β was required for polarization of non-pathogenic T H 17 cells, whereas pathogenic T H 17 cells developed in the absence of active TGF-β. Thus, dectin-1-mediated modulation of type I IFN responses allowed TGF-β activation and non-pathogenic T H 17 cell development during fungal infections in humans.
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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".