Ciprofloxacin and levofloxacin attenuate microglia inflammatory response via TLR4/NF-kB pathway
Bibliographic record
Abstract
BACKGROUND: Neuroinflammation is the response of the central nervous system to events that interfere with tissue homeostasis and represents a common denominator in virtually all neurological diseases. Activation of microglia, the principal immune effector cells of the brain, contributes to neuronal injury by release of neurotoxic products. Toll-like receptor 4 (TLR4), expressed on the surface of microglia, plays an important role in mediating lipopolysaccharide (LPS)-induced microglia activation and inflammatory responses. We have previously shown that curcumin and some of its analogues harboring an α,β-unsaturated 1,3-diketone moiety, able to coordinate the magnesium ion, can interfere with LPS-mediated TLR4-myeloid differentiation protein-2 (MD-2) signaling. Fluoroquinolone (FQ) antibiotics are compounds that contain a keto-carbonyl group that binds divalent ions, including magnesium. In addition to their antimicrobial activity, FQs are endowed with immunomodulatory properties, but the mechanism underlying their anti-inflammatory activity remains to be defined. The aim of the current study was to elucidate the molecular mechanism of these compounds in the TLR4/NF-κB inflammatory signaling pathway. METHODS: The putative binding mode of five FQs [ciprofloxacin (CPFX), levofloxacin (LVFX), moxifloxacin, ofloxacin, and delafloxacin] to TLR4-MD-2 was determined using molecular docking simulations. The effect of CPFX and LVFX on LPS-induced release of IL-1β and TNF-α and NF-κB activation was investigated in primary microglia by ELISA and fluorescence staining. The interaction of CPFX and LVFX with TLR4-MD-2 complex was assessed by immunoprecipitation followed by Western blotting using Ba/F3 cells. RESULTS: CPFX and LVFX bound to the hydrophobic region of the MD-2 pocket and inhibited LPS-induced secretion of pro-inflammatory cytokines and activation of NF-κB in primary microglia. Furthermore, these FQs diminished the binding of LPS to TLR4-MD-2 complex and decreased the resulting TLR4-MD-2 dimerization in Ba/F3 cells. CONCLUSIONS: These results provide new insight into the mechanism of the anti-inflammatory activity of CPFX and LVFX, which involves, at least in part, the activation of TLR4/NF-κB signaling pathway. Our findings might facilitate the development of new molecules directed at the TLR4-MD-2 complex, a potential key target for controlling neuroinflammation.
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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) | 0.003 | 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".