Chloroquine attenuates lipopolysaccharide-induced inflammatory responses through upregulation of USP25
Bibliographic record
Abstract
Lipopolysaccharide (LPS) is a key pathogenic factor in sepsis, and its recognition by toll-like receptor 4 (TLR4) can activate two district signaling pathways, leading to activation of transcription factors including NF-κB and interferon regulatory factor 3 (IRF3). Chloroquine (CQ) has been shown to affect LPS-TLR4 colocalization and inhibit both MyD88-dependent and TRAM/TRIF-dependent pathways, though the mechanism involved is still poorly understood. Here, we found that the ubiquitin-proteasome system might be involved in this process. CQ increased USP25, a deubiquitinating enzyme, as well as mRNA and protein expression in a dose-dependent manner, which might to some degree be involved in CQ attenuation of LPS-induced macrophage activation. Overexpression of USP25 decreased LPS-induced inflammatory cytokines like TNF-α, IL-6, and IFN-β, while specific siRNA-mediated USP25 silencing increased TNF-α, IL-6, and IFN-β production and secretion. In addition, USP25 deletion strengthened mitogen-activated protein kinase (MAPKs) phosphorylation and IκB degradation. Moreover, USP25 interference increased NF-κB and IRF3 nuclear translocation. Taken together, our data demonstrated a new possible regulator of LPS-induced macrophage activation mediated by CQ, through upregulation of USP25.
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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) | 0.001 | 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".