TRAF1 negatively regulates C-type lectin receptor-induced proinflammatory response to fungal infection
Bibliographic record
Abstract
Abstract Fungal infections pose serious health threat worldwide, causing severe mucosal and systemic candidiasis in elderly people, AIDS patients and organ recipients. Through sensing fungal cell-wall components b-glucan and mannan, C-type lectin receptors (CLRs) dectin-1 and dectin-2/3 play pivotal role in the induction of anti-fungal innate and adaptive immune responses. However, the regulatory mechanisms of CLR signaling remain to be better understood. Indeed, our previous work demonstrated that the protein tyrosine phosphatase SHP-2 acts as a positive regulator of CLR-induced signaling, and thus plays a critical role in DCs to promote anti-fungal Th17 response. In this study, we found that fungus-elicited CLR signals are also stringently controlled by negative regulation. Upon C. albicans infection, TRAF1 was highly induced in skin, lung and kidney. Elevated TRAF1 expression was also detected in macrophages and DCs stimulated by dectin-1 and dectin-2/3 ligands, respectively. Mechanistically, TRAF1 acted as a feed-back negative regulator critically controlling the induction of proinflammatory genes, such as Cxcl1 and Tnf, in response to fungal infection. Consistently, TRAF1-deficient mice exhibited increased neutrophil-infiltration, highly efficient fungal eradication and ameliorated tissue damage, culminating on improved host defense and better survival. Taken together, this study identified a new feed-back regulatory mechanism by which CLRs regulate anti-fungal proinflammatory response.
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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".