Coptisine Improves Liver Inflammation in Sepsis by Regulating <scp>STAT1</scp>/<scp>IRF1</scp>/<scp>GPX4</scp> Signaling‐Mediated Kupffer Cells Ferroptosis
Bibliographic record
Abstract
Sepsis is a life-threatening condition characterized by organ dysfunction, with the liver being particularly vulnerable due to inflammation triggered by Kupffer cell activation. Ferroptosis, an iron-dependent form of regulated cell death associated with macrophages, has emerged as a key pathogenic mechanism. This study aimed to investigate the protective effects of coptisine (COP), a natural alkaloid, against sepsis-induced hepatic ferroptosis and injury using in vivo and in vitro models. Sepsis was induced in mice via cecal ligation and puncture (CLP) or lipopolysaccharide (LPS) challenge, followed by treatment with COP, ferrostatin-1 (Fer-1, a ferroptosis inhibitor), or 2-NP. In vitro, Kupffer cells were stimulated with LPS + IFN-γ and erastin to induce inflammation and ferroptosis, then treated with COP or Fer-1. Multiple techniques were employed, including histopathology, enzyme-linked immunosorbent assay (ELISA), quantitative PCR (qPCR), Western Blot, immunofluorescence (IF), molecular docking, bio-layer interferometry (BLI), and cellular thermal shift assay (CETSA), to evaluate the STAT1/IRF1/GPX4 signaling axis. Additionally, serum markers from sepsis patients were analyzed. In septic mice, COP significantly attenuated liver injury, inflammation, and ferroptosis. In Kupffer cells, COP suppressed erastin-induced ferroptosis. Mechanistically, COP directly bound to STAT1, inhibiting its phosphorylation and subsequent IRF1 activation, while restoring GPX4 expression. Overexpression of STAT1 abolished the protective effects of COP. Clinical data revealed elevated p-STAT1 and IRF1 levels alongside reduced GPX4 in sepsis patients. COP exerts hepatoprotective effects in sepsis by inhibiting ferroptosis through the STAT1/IRF1/GPX4 pathway, highlighting its potential as a therapeutic agent for sepsis-associated liver injury.
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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 | 0.001 |
| 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".