Calcineurin Negatively Regulates TLR-Mediated Activation Pathways
Bibliographic record
Abstract
In innate immunity, microbial components stimulate macrophages to produce antimicrobial substances, cytokines, other proinflammatory mediators, and IFNs via TLRs, which trigger signaling pathways activating NF-kappaB, MAPKs, and IFN response factors. We show in this study that, in contrast to its activating role in T cells, in macrophages the protein phosphatase calcineurin negatively regulates NF-kappaB, MAPKs, and IFN response factor activation by inhibiting the TLR-mediated signaling pathways. Evidence for this novel role for calcineurin was provided by the findings that these signaling pathways are activated when calcineurin is inhibited either by the inhibitors cyclosporin A or FK506 or by small interfering RNA-targeting calcineurin, and that activation of these pathways by TLR ligands is inhibited by the overexpression of a constitutively active form of calcineurin. We further found that IkappaB-alpha degradation, MAPK activation, and TNF-alpha production by FK506 were reduced in macrophages from mice deficient in MyD88, Toll/IL-1R domain-containing adaptor-inducing IFN-beta (TRIF), TLR2, or TLR4, whereas macrophages from TLR3-deficient or TLR9 mutant mice showed the same responses to FK506 as those of wild-type cells. Biochemical studies indicate that calcineurin interacts with MyD88, TRIF, TLR2, and TLR4, but not with TLR3 or TLR9. Collectively, these results suggest that calcineurin negatively regulates TLR-mediated activation pathways in macrophages by inhibiting the adaptor proteins MyD88 and TRIF, and a subset of TLRs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| 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) | 0.000 | 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".