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Record W2960015172 · doi:10.1186/s12974-019-1538-9

Ciprofloxacin and levofloxacin attenuate microglia inflammatory response via TLR4/NF-kB pathway

2019· review· en· W2960015172 on OpenAlexafffund
Morena Zusso, Valentina Lunardi, Davide Franceschini, Andrea Pagetta, Rita Lo, Stefano Stifani, Anna Chiara Frigo, Pietro Giusti, Stefano Moro

Bibliographic record

VenueJournal of Neuroinflammation · 2019
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmune Response and Inflammation
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersCanadian Institutes of Health ResearchUniversità degli Studi di PadovaNvidia
KeywordsMicrogliaNeuroinflammationTLR4ChemistryPharmacologySignal transductionInflammationBiochemistryImmunologyBiology

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.032
GPT teacher head0.283
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreReview

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".

Quick stats

Citations524
Published2019
Admission routes2
Has abstractyes

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