T cell intrinsic Nod2 controls Th17 immunity to <i>Candida albicans</i> infection
Bibliographic record
Abstract
Abstract We recently discovered a role for Nod2 within T cells in controlling Th17-immunity, such that deletion of Nod2 exacerbated experimental autoimmune arthritis and uveitis. Protection against opportunistic Candida albicans infection relies on intact Th17-responses. Thus, we considered whether the T cell intrinsic Nod2 might control antifungal Th17 responses at the cost of increasing host susceptibility to autoimmunity. Nod2−/− mice infected with 105 (LD50) Candida albicans had increased survival and reduced fungal burden in the kidney within 24–72 h post-infection, suggesting a critical role for Nod2 in C. albicans immunity. Rag−/− mice reconstituted with Nod2fl/fl/CD4-cre CD4+ T cells and infected with C. albicans had decreased fungal burden compared to control CD4-Cre T cell recipients, and IL-17 depletion reverted the phenotype. After 48h infection, Nod2fl/fl/CD4-cre CD4+ T cells in the kidney had increased activation (CD69), increased proportion of T effector cells, and decreased Tregs compared to controls, suggesting endogenous Nod2 negatively regulates fungal-triggered Teff/Th17 cell responses. We next asked how Nod2 might function in SKG mice that are genetically susceptible to arthritis. Whereas Nod2−/− SKG mice developed an exacerbated form of arthritis compared to Nod2+/+ SKG controls, these same mice cleared C. albicans infection better than Nod2+/+ SKG mice. Cumulatively our data indicate a critical role for T cell intrinsic Nod2 in antifungal Th17 immunity. Given these studies we posit that human NOD2 genetic variants may offer enhanced ability to fight fungal infection via a T cell intrinsic mechanism, at the cost of triggering autoimmunity. Supported by grants from VA (CDA-2 IK2BX004523 and Merit I01BX002180) and NIH (R01 EY025250).
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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) | 0.001 | 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".