A subclass of acylated anti-inflammatory mediators usurp Toll-like receptor 2 to inhibit neutrophil recruitment through peroxisome proliferator-activated receptor γ
Bibliographic record
Abstract
Toll-like receptors are host sentinel receptors that signal the presence of infectious nonself and initiate protective immunity. One of the primary immune defense mechanisms is the recruitment of neutrophils from the bloodstream into the infected tissue. Although neutrophils are important in host defense, they can also be responsible for damaging pathologies associated with excessive inflammation. Here, we report that the di-acylated TLR2 ligand lipoteichoic acid can directly inhibit neutrophil recruitment in vivo. This discovery allowed us to test the concept that conventional proinflammatory TLR2 ligands can be made to act as inhibitors through specific structural modifications. Indeed, lipopeptide TLR2 ligands, when modified at their acyl chains to contain linoleate, lose their capacity to induce inflammation and yield ligands that can directly inhibit the in vivo neutrophil recruitment initiated by a wide range of proinflammatory stimuli. The inhibitory capacity of LTA and these modified ligands requires the expression of TLR2, but is independent of the TLR2 signaling adaptor, MyD88. Instead, this inhibitory effect requires functional activity of the fatty acid and nuclear hormone receptor peroxisome proliferator-activated receptor γ (PPARγ). Therefore, these data support a model in TLR2 biology where structural modifications of these ligands can profoundly influence host-microbial interactions. These inhibitory TLR2 ligands also have broader implications with respect to their potential use in various inflammatory disease settings.
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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".