Membrane CD14, but not soluble CD14, is used by exoenzyme S from<i>P. aeruginosa</i>to signal proinflammatory cytokine production
Bibliographic record
Abstract
Recognition of TLR agonists involves a complex interplay among a variety of serum and cell membrane molecules, including mCD14 and sCD14 that is not fully understood. TLR activation results in downstream signaling that induces inflammatory cytokine production in response to pathogenic molecules, such as ExoS, which is a TLR2 and TLR4 agonist produced by the opportunistic pathogen Pseudomonas aeruginosa. We reasoned that responses to ExoS, a protein, might differ from canonical TLR agonists such as LPS. Stimulating the expression of mCD14 with vitamin D3 enhanced the response to ExoS and LPS. Also, blocking anti-CD14 antibody or removing mCD14 using PLC reduced responses to ExoS and LPS. Furthermore, CD14-deficient cells were unable to bind and respond to ExoS, which was restored by stable transfection of mCD14, indicating that mCD14 was required for the response to ExoS. However, addition of sCD14 to culture enhanced responsiveness to LPS but not ExoS. Moreover, the addition of serum did not alter the response to ExoS but enhanced the response to LPS. Despite differences of adaptor molecule use between ExoS and LPS, lipid antagonists that compete for LPS binding to CD14 also inhibited the response to ExoS. These results highlight a fundamental difference between TLR agonists in their requirements for CD14 and serum components. These results suggest that understanding the dissimilarities and targeting overlapping sites of interaction on CD14 may yield a synergistic, clinical benefit during infections where a variety of TLR agonists are present.
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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".