Phenolic 1,3‐diketones attenuate lipopolysaccharide‐induced inflammatory response by an alternative magnesium‐mediated mechanism
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Toll-like receptor 4 (TLR4) plays a key role in the induction of inflammatory responses both in peripheral organs and the CNS. Curcumin exerts anti-inflammatory functions by interfering with LPS-induced dimerization of TLR4-myeloid differentiation protein-2 (MD-2) complex and suppressing pro-inflammatory mediator release. However, the inhibitory mechanism of curcumin remains to be defined. EXPERIMENTAL APPROACH: Binding of bis-demethoxycurcumin (GG6) and its cyclized pyrazole analogue (GG9), lacking the 1,3-dicarbonyl function, to TLR4-MD-2 was determined using molecular docking simulations. The effects of these compounds on cytokine release and NF-κB activation were examined by ELISA and fluorescence staining in LPS-stimulated primary microglia. Interference with TLR4 dimerization was assessed by immunoprecipitation in Ba/F3 cells. KEY RESULTS: Both curcumin analogues bound to the hydrophobic region of the MD-2 pocket. However, only curcumin and GG6, both possessing the 1,3-diketone moiety, inhibited LPS-induced TLR4 dimerization, activation of NF-κB and secretion of pro-inflammatory cytokines in primary microglia. Consistent with the ability of 1,3-diketones to coordinate divalent metal ions, LPS stimulation in a low magnesium environment decreased pro-inflammatory cytokine release and NF-κB p65 nuclear translocation in microglia and decreased TLR4-MD-2 dimerization in Ba/F3 cells. Curcumin and GG6 also significantly reduced cytokine output in contrast to the pyrazole analogue GG9. CONCLUSIONS AND IMPLICATIONS: These results indicate that phenolic 1,3-diketones, with a structural motif able to coordinate magnesium ions, can modulate LPS-mediated TLR4-MD-2 signalling. Taken together, these studies identify a previously uncharacterized mechanism involving magnesium, underlying the inflammatory responses to LPS.
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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) | 0.000 | 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) | 0.002 | 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".