Targeting the Pseudomonas aeruginosa Virulence Factor Phospholipase C With Engineered Liposomes
Bibliographic record
Abstract
Engineered liposomes composed of the naturally occurring lipids sphingomyelin (Sm) and cholesterol (Ch) have been demonstrated to efficiently neutralize toxins secreted by Gram-positive bacteria such as Streptococcus pneumoniae and Staphylococcus aureus. Here, we hypothesized that liposomes are capable of neutralizing cytolytic virulence factors secreted by the Gram-negative pathogen Pseudomonas aeruginosa. We used the highly virulent cystic fibrosis P. aeruginosa Liverpool Epidemic Strain LESB58 and showed that sphingomyelin (Sm) and a combination of sphingomyelin with cholesterol (Ch:Sm; 66 mol/% Ch and 34 mol/% Sm) liposomes reduced lysis of human bronchial and red blood cells upon challenge with the Pseudomonas secretome. Mass spectrometry of liposome-sequestered Pseudomonas proteins identified the virulence-promoting hemolytic phospholipase C (PlcH) as having been neutralized. Pseudomonas aeruginosa supernatants incubated with liposomes demonstrated reduced PlcH activity as assessed by the p-nitrophenylphosphorylcholine (NPPC) assay. Testing the in vivo efficacy of the liposomes in a murine cutaneous abscess model revealed that Sm and Ch:Sm, as single dose treatments, attenuated abscesses by >30%, demonstrating a similar effect to that of a mutant lacking plcH in this infection model. Thus, sphingomyelin-containing liposome therapy offers an interesting approach to treat and reduce virulence of complex infections caused by P. aeruginosa and potentially other Gram-negative pathogens expressing PlcH.
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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) | 0.000 | 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".