Vying for the control of inflammasomes: The cytosolic frontier of enteric bacterial pathogen–host interactions
Bibliographic record
Abstract
Enteric pathogen-host interactions occur at multiple interfaces, including the intestinal epithelium and deeper organs of the immune system. Microbial ligands and activities are detected by host sensors that elicit a range of immune responses. Membrane-bound toll-like receptors and cytosolic inflammasome pathways are key signal transducers that trigger the production of pro-inflammatory molecules, such as cytokines and chemokines, and regulate cell death in response to infection. In recent years, the inflammasomes have emerged as a key frontier in the tussle between bacterial pathogens and the host. Inflammasomes are complexes that activate caspase-1 and are regulated by related caspases, such as caspase-11, -4, -5 and -8. Importantly, enteric bacterial pathogens can actively engage or evade inflammasome signalling systems. Extracellular, vacuolar and cytosolic bacteria have developed divergent strategies to subvert inflammasomes. While some pathogens take advantage of inflammasome activation (e.g. Listeria monocytogenes, Helicobacter pylori), others (e.g. E. coli, Salmonella, Shigella, Yersinia sp.) deploy a range of virulence factors, mainly type 3 secretion system effectors, that subvert or inhibit inflammasomes. In this review we focus on inflammasome pathways and their immune functions, and discuss how enteric bacterial pathogens interact with them. These studies have not only shed light on inflammasome-mediated immunity, but also the exciting area of mammalian cytosolic immune surveillance.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".