Modifications in Perfringolysin O Domain 4 Alter the Cholesterol Concentration Threshold Required for Binding
Bibliographic record
Abstract
Changes in the cholesterol content of cell membranes affect many physiological and pathological events, including the formation of arterial plaques, the entry of virus into cells, and receptor organization. Measuring the trafficking and distribution of cholesterol is essential to understanding how cells regulate sterol levels in membranes. Perfringolysin O (PFO) is a cytolysin secreted by Clostridium perfringens that requires cholesterol in the target membrane for binding. The specificity of PFO for high levels of cholesterol makes the toxin an attractive tool for studying the distribution and trafficking of cholesterol in cells. However, the use of the native toxin is limited given that binding is triggered only above a determined cholesterol concentration. To this end, we have identified mutations in PFO that altered the threshold for how much cholesterol is required to trigger binding. The cholesterol threshold among different PFO derivatives varied up to 10 mol % sterol, and these variations were not dependent on the lipid composition of the membrane. We characterized the binding of these PFO derivatives on murine macrophage-like cells whose cholesterol content was reduced or augmented. Our findings revealed that engineered PFO derivatives differentially associated with these cells in response to changes in cholesterol levels in the plasma membrane.
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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".