A mutated cholera toxin without the ADP-ribosyltransferase activity induces cytokine production and inhibits apoptosis of splenocytes in mice possibly via toll-like receptor-4 signaling
Bibliographic record
Abstract
Native cholera toxin (CT) and its mutated form (CT-2*) without ADP-ribosyltransferase activity differ in their immunomodulatory effects on host cells, and the mechanisms of these differences are poorly understood. In this study, we demonstrated that CT-2* induced higher levels of cytokine production and down-regulated ex-vivo apoptosis of splenocytes from C57BL/6 mice. After exposure of the splenocytes ex-vivo to CT or CT-2* (2μg/ml) for 48h, CT-2* stimulated expression of the toll-like receptor (TLR-4) gene was much higher and the cells produced increased levels of interleukin (IL)-12, interferon (IFN)-γ, and tumor necrosis factor (TNF)-α, compared to splenocytes of mice exposed to native CT. We confirmed these findings by observing that CT-2*, induced much lower levels of IL-12, IFN-γ, and TNF-α in a TLR-4 knockout macrophage cell line derived from C57BL/6 mice. In addition, while CT is known to stimulate apoptosis in splenocytes, we observed that CT-2* significantly down-regulated apoptosis (4.2%), compared to splenocytes exposed to CT (18.7%) or PBS (negative control, 8.5%). On the contrary, we noted both native CT and CT-2* to exhibit similar levels of apoptosis in TLR-4(-/-) cell line. Overall, the evidence supports the conclusion that CT-2* modulated cytokine production and apoptosis in splenocytes of mice possibly through the TLR-4 signaling pathway.
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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".