Diminazene aceturate (Berenil) modulates LPS induced pro-inflammatory cytokine production by inhibiting phosphorylation of MAPKs and STAT proteins
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 it