Different Domains of <i>Pseudomonas aeruginosa</i> Exoenzyme S Activate Distinct TLRs
Bibliographic record
Abstract
Some bacterial products possess multiple immunomodulatory effects and thereby complex mechanisms of action. Exogenous administration of an important Pseudomonas aeruginosa virulence factor, exoenzyme S (ExoS) induces potent monocyte activation leading to the production of numerous proinflammatory cytokines and chemokines. However, ExoS is also injected directly into target cells, inducing cell death through its multiple effects on signaling pathways. This study addresses the mechanisms used by ExoS to induce monocyte activation. Exogenous administration resulted in specific internalization of ExoS via an actin-dependent mechanism. However, ExoS-mediated cellular activation was not inhibited if internalization was blocked, suggesting an alternate mechanism of activation. ExoS bound a saturable and specific receptor on the surface of monocytic cells. ExoS, LPS, and peptidoglycan were all able to induce tolerance and cross-tolerance to each other suggesting the involvement of a TLR in ExoS-recognition. ExoS activated monocytic cells via a myeloid differentiation Ag-88 pathway, using both TLR2 and the TLR4/MD-2/CD14 complex for cellular activation. Interestingly, the TLR2 activity was localized to the C-terminal domain of ExoS while the TLR4 activity was localized to the N-terminal domain. This study provides the first example of how different domains of the same molecule activate two TLRs, and also highlights the possible overlapping pathophysiological processes possessed by microbial toxins.
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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".