Inhibition of mitogen-activated protein kinase signalling by Bacillus anthracis lethal toxin causes destabilization of interleukin-8 mRNA
Bibliographic record
Abstract
Bacillus anthracis must overcome host innate immune defences to establish a systemic anthrax infection. This is facilitated in part by lethal toxin (LT), a secreted virulence factor that consists of a cell-binding moiety, protective antigen (PA), and an enzymatic moiety, lethal factor (LF). PA binds cells through protein receptors and mediates the delivery of LF to the cytosol. LF is a protease that cleaves amino-terminal fragments from mitogen-activated protein kinase kinases (MAPKKs), preventing phosphorylation of their downstream targets. Here we report that LT reduces the amount of interleukin (IL)-8 produced and secreted by human endothelial cells. The reduction of IL-8 levels by LT was not attributable to reduced expression from the IL-8 promoter, but resulted from destabilization of IL-8 mRNA. Destabilization by LT was mediated through the 3' untranslated region of the IL-8 transcript and could be mimicked by pharmacological inhibitors of MAPK pathways. LT diminished the induction of IL-8 mRNA and protein by lipopolysaccharide, indicating that the toxin can impair the ability of these cells to initiate an immune response. Destabilization of a cytokine transcript represents a new interference strategy used by either a bacterial or viral pathogen to reduce cytokine expression and may help B. anthracis to evade host immune defences.
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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".