Regulator of calcineurin 1 differentially regulates TLR-dependent MyD88 and TRIF signaling pathways
Bibliographic record
Abstract
Toll-like receptors (TLRs) recognize the conserved molecular patterns in microorganisms and trigger myeloid differentiation primary response 88 (MyD88) and/or TIR-domain-containing adapter-inducing interferon-β (TRIF) pathways that are critical for host defense against microbial infection. However, the molecular mechanisms that govern TLR signaling remain incompletely understood. Regulator of calcineurin-1 (RCAN1), a small evolutionarily conserved protein that inhibits calcineurin phosphatase activity, suppresses inflammation during Pseudomonas aeruginosa infection. Here, we define the roles for RCAN1 in P. aeruginosa lipopolysaccharide (LPS)-activated TLR4 signaling. We compared the effects of P. aeruginosa LPS challenge on bone marrow-derived macrophages from both wild-type and RCAN1-deficient mice and found that RCAN1 deficiency increased the MyD88-NF-κB-mediated cytokine production (IL-6, TNF and MIP-2), whereas TRIF-interferon-stimulated response elements (ISRE)-mediated cytokine production (IFNβ, RANTES and IP-10) was suppressed. RCAN1 deficiency caused increased IκBα phosphorylation and NF-κB activity in the MyD88-dependent pathway, but impaired ISRE activation and reduced IRF7 expression in the TRIF-dependent pathway. Complementary studies of a mouse model of P. aeruginosa LPS-induced acute pneumonia confirmed that RCAN1-deficient mice displayed greatly enhanced NF-κB activity and MyD88-NF-κB-mediated cytokine production, which correlated with enhanced pulmonary infiltration of neutrophils. By contrast, RCAN1 deficiency had little effect on the TRIF pathway in vivo. These findings demonstrate a novel regulatory role of RCAN1 in TLR signaling, which differentially regulates MyD88 and TRIF pathways.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".