TLR activation enhances C5a‐induced pro‐inflammatory responses by negatively modulating the second C5a receptor, C5L2
Bibliographic record
Abstract
TLR and complement activation ensures efficient clearance of infection. Previous studies documented synergism between TLRs and the receptor for the pro-inflammatory complement peptide C5a (C5aR/CD88), and regulation of TLR-induced pro-inflammatory responses by C5aR, suggesting crosstalk between TLRs and C5aR. However, it is unclear whether and how TLRs modulate C5a-induced pro-inflammatory responses. We demonstrate a marked positive modulatory effect of TLR activation on cell sensitivity to C5a in vitro and ex vivo and identify an underlying mechanistic target. Pre-exposure of PBMCs and whole blood to diverse TLR ligands or bacteria enhanced C5a-induced pro-inflammatory responses. This effect was not observed in TLR4 signalling-deficient mice. TLR-induced hypersensitivity to C5a did not result from C5aR upregulation or modulation of C5a-induced Ca(2+) mobilization. Rather, TLRs targeted another C5a receptor, C5L2 (acting as a negative modulator of C5aR), by reducing C5L2 activity. TLR-induced hypersensitivity to C5a was mimicked by blocking C5L2 and was not observed in C5L2KO mice. Furthermore, TLR activation inhibited C5L2 expression upon C5a stimulation. These findings identify a novel pathway of crosstalk within the innate immune system that amplifies innate host defense at the TLR-complement interface. Unravelling the mutually regulated activities of TLRs and complement may reveal new therapeutic avenues to control inflammation.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".