Sialic acid facilitates binding and cytotoxic activity of the pore-forming Clostridium perfringens NetF toxin to host cells
Bibliographic record
Abstract
NetF-producing type A Clostridium perfringens is an important cause of canine and foal necrotizing enteritis. NetF, related to the β-sheet pore-forming Leukocidin/Hemolysin superfamily, is considered a major virulence factor for this disease. The main purpose of this work is to demonstrate the pore-forming activity of NetF and characterize the chemical nature of its binding site. Electron microscopy using recombinant NetF (rNetF) confirmed that NetF is able to oligomerize and form large pores in equine ovarian (EO) cell membranes and sheep red blood cells. These oligomeric pores appear to be about 4-6 nm in diameter, and the number of oligomer subunits to vary from 6 to 9. Sodium periodate treatment rendered EO cells non-susceptible to NetF, suggesting that NetF binding requires cell surface carbohydrates. NetF cytotoxicity was also inhibited by a lectin that binds sialic acid, by sialidase, and by free sialic acid in excess, all of which clearly implicate sialic acid-containing membrane carbohydrates in NetF binding and/or toxicity for EO cells. Binding of NetF to sheep red blood cells was not inhibited by the gangliosides GM1, GM2 and GM3, nor did the latter promote membrane permeabilization in liposomes, suggesting that they do not constitute the cellular receptors. In contrast, treatment of EO cells with different proteases reduced their susceptibility to NetF, suggesting that the NetF receptor is a sialic acid-containing glycoprotein.
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".