Diminazene aceturate (Berenil) modulates LPS induced pro-inflammatory cytokine production by inhibiting phosphorylation of MAPKs and STAT proteins
Bibliographic record
Abstract
Although diminazene aceturate (Berenil) is widely used as a trypanolytic agent in livestock, its mechanisms of action remain poorly understood. We previously showed that Berenil treatment suppresses pro-inflammatory cytokine production by splenic and liver macrophages leading to a concomitant reduction in serum cytokine levels in mice infected with Trypanosoma congolense or challenged with LPS. Here, we investigated the molecular mechanisms through which Berenil alters pro-inflammatory cytokine production by macrophages. We show that pre-treatment of macrophages with Berenil dramatically suppressed IL-6, IL-12 and TNF-α production following LPS, CpG and Poly I:C stimulation without altering the expression of TLRs. Instead, it significantly down-regulated phosphorylation of mitogen-activated protein kinases (p38, extracellular signal-regulated kinase and c-Jun N-terminal kinases), signal transducer and activator of transcription (STAT) proteins (STAT1 and STAT3) and NF-кB p65 activity both in vitro and in vivo. Interestingly, Berenil treatment up-regulated the phosphorylation of STAT5 and the expression of suppressor of cytokine signaling 1 (SOCS1) and SOCS3, which are negative regulators of innate immune responses, including MAPKs and STATs. Collectively, these results show that Berenil down-regulates macrophage pro-inflammatory cytokine production by inhibiting key signaling pathways associated with cytokine production and suggest that this drug may be used to treat conditions caused by excessive production of inflammatory cytokines.
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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".