SELECTIVE USE OF MEMBRANE CD14, BUT NOT SOLUBLE CD14 ANDINHIBITION BY SERUM IN THE INFLAMMATORY CYTOKINE RESPONSE INDUCED BY A NOVELTLR LIGAND (P. AERUGINOSA EXOENZYME S)
Bibliographic record
Abstract
Background/Purpose: Soluble andmembrane CD14 (sCD14 and mCD14) bind microbial products, are toll-like receptor(TLR) coreceptors, and induce inflammation. Clinical relevance is demonstrated by a correlation of sCD14 levels with morbidity and mortality in inflammatory diseases. Moreover, P. aeruginosa induces damaging inflammation in the lungs of cysticfibrosis patients and its virulence is often attributed to the TLR2 and TLR4ligand exoenzyme S (ExoS). Because of the importance of the response of CD14 toTLR ligands and its potential for therapeutic manipulation, its role inExoS-induced inflammatory cytokines was investigated. Methods: The ability of ExoS to induce TNFand IL-6 cytokine production in cell lines that express TLR and either CD14 positive or negative was assessed (THP-1 or U373 cells, respectively). Recombinant CD14 or CD14 blocking agents were used to test the contribution ofmCD14 and sCD14. Results: Enhancing expression of mCD14 on THP-1 cells increased TNF production, which was abrogated by blocking or removing mCD14. Transfecting mCD14 into U373 cells demonstrated that mCD14 was required for binding ExoS and subsequent IL-6 production. Unlike TLR ligands that only stimulate one TLR, such as lipopolysaccharide, neither sCD14 nor serum enhanced production of cytokine in response to ExoS. Uniquely, serum inhibited ExoS induced TNF. Conclusion: This work demonstrates a fundamental difference in the requirement of ExoS and other TLR ligands for CD14, which must be considered when designing therapies to block microbe-induced inflammation. The presence of a potential therapeutic molecule in serum could help in the development of an ExoS neutralizing agent.
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) | 0.001 | 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".