Shiga Toxin/Lipopolysaccharide Activates Caspase-4 and Gasdermin D to Trigger Mitochondrial Reactive Oxygen Species Upstream of the NLRP3 Inflammasome
Bibliographic record
Abstract
The non-canonical caspase-4 and canonical NLRP3 inflammasomes are both activated by intracellular lipopolysaccharide (LPS), but the crosstalk between these two pathways remains unclear. Shiga toxin 2 (Stx2)/LPS complex, from pathogenic enterohemorrhagic Escherichia coli, activates caspase-4, gasdermin D (GSDMD), and the NLRP3 inflammasome in human THP-1 macrophages, but not mouse macrophages that lack the Stx receptor CD77. Stx2/LPS-mediated IL-1β secretion and pyroptosis are dependent on mitochondrial reactive oxygen species (ROS) downstream of the non-canonical caspase-4 inflammasome and cleaved GSDMD, which is enriched at the mitochondria. Blockade of caspase-4 activation and ROS generation as well as GSDMD deficiency significantly reduces Stx2/LPS-induced IL-1β production and pyroptosis. The NLRP3 inflammasome plays a significant role in amplifying Stx2/LPS-induced GSDMD cleavage and pyroptosis, with significant reduction of these responses in NLRP3-deficient THP-1 cells. Together, these data show that Stx2/LPS complex activates the non-canonical inflammasome and mitochondrial ROS upstream of the NLRP3 inflammasome to promote cytokine maturation and pyroptosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".