IL-27 Induced by Select <i>Candida</i> spp. via TLR7/NOD2 Signaling and IFN-β Production Inhibits Fungal Clearance
Bibliographic record
Abstract
Candida spp. elicit cytokine production downstream of various pathogen recognition receptors, including C-type lectin-like receptors, TLRs, and nucleotide oligomerization domain (NOD)-like receptors. IL-12 family members IL-12p70 and IL-23 are important for host immunity against Candida spp. In this article, we show that IL-27, another IL-12 family member, is produced by myeloid cells in response to selected Candida spp. We demonstrate a novel mechanism for Candida parapsilosis-mediated induction of IL-27 in a TLR7-, MyD88-, and NOD2-dependent manner. Our data revealed that IFN-β is induced by C. parapsilosis, which in turn signals through the IFN-α/β receptor and STAT1/2 to induce IL-27. Moreover, IL-27R (WSX-1)-deficient mice systemically infected with C. parapsilosis displayed enhanced pathogen clearance compared with wild-type mice. This was associated with increased levels of proinflammatory cytokines in the serum and increased IFN-γ and IL-17 responses in the spleens of IL-27R-deficient mice. Thus, our data define a novel link between C. parapsilosis, TLR7, NOD2, IFN-β, and IL-27, and we have identified an important role for IL-27 in the immune response against C. parapsilosis Overall, these findings demonstrate an important mechanism for the suppression of protective immune responses during infection with C. parapsilosis, which has potential relevance for infections with other fungal pathogens.
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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".