Perfringolysin O Theta Toxin as a Tool to Monitor the Distribution and Inhomogeneity of Cholesterol in Cellular Membranes
Bibliographic record
Abstract
Cholesterol is an essential structural component of cellular membranes in eukaryotes. Cholesterol in the exofacial leaflet of the plasma membrane is thought to form membrane nanodomains with sphingolipids and specific proteins. Additionally, cholesterol is found in the intracellular membranes of endosomes and has crucial functions in membrane trafficking. Furthermore, cellular cholesterol homeostasis and regulation of de novo synthesis rely on transport via both vesicular and non-vesicular pathways. Thus, the ability to visualize and detect intracellular cholesterol, especially in the plasma membrane, is critical to understanding the complex biology associated with cholesterol and the nanodomains. Perfringolysin O (PFO) theta toxin is one of the toxins secreted by the anaerobic bacteria Clostridium perfringens and this toxin forms pores in the plasma membrane that causes cell lysis. It is well understood that PFO recognizes and binds to cholesterol in the exofacial leaflets of the plasma membrane, and domain 4 of PFO (D4) is sufficient for the binding of cholesterol. Recent studies have taken advantage of this high-affinity cholesterol-binding domain to create a variety of cholesterol biosensors by using a non-toxic PFO or the D4 in isolation. This review highlights the characteristics and usefulness of, and the principal findings related to, these PFO-derived cholesterol biosensors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".