Mitogen-activated protein kinase pathways defend against bacterial pore-forming toxins
Bibliographic record
Abstract
Cytolytic pore-forming toxins are important for the virulence of many disease-causing bacteria. How target cells molecularly respond to these toxins and whether or not they can mount a defense are poorly understood. By using microarrays, we demonstrate that the nematode Caenorhabditis elegans responds robustly to Cry5B, a member of the pore-forming Crystal toxin family made by Bacillus thuringiensis. This genomic response is distinct from that seen with a different stressor, the heavy metal cadmium. A p38 mitogen-activated protein kinase (MAPK) kinase and a c-Jun N-terminal-like MAPK are both transcriptionally up-regulated by Cry5B. Moreover, both MAPK pathways are functionally important because elimination of either leads to animals that are (i) hypersensitive to a low, chronic dose of toxin and (ii) hypersensitive to a high, brief dose of toxin such that the animal might naturally encounter in the wild. These results extend to mammalian cells because inhibition of p38 results in the hypersensitivity of baby hamster kidney cells to aerolysin, a pore-forming toxin that targets humans. Furthermore, we identify two downstream transcriptional targets of the p38 MAPK pathway, ttm-1 and ttm-2, that are required for defense against Cry5B. Our data demonstrate that cells defend against pore-forming toxins by means of conserved MAPK pathways.
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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 | 0.000 |
| 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".