Leaky membrane fusion: an ambivalent effect induced by antimicrobial polycations
Bibliographic record
Abstract
Both antimicrobial peptides and their synthetic mimics are potential alternatives to classical antibiotics. They can induce several membrane perturbations including permeabilization. Especially in model studies, aggregation of vesicles by such polycations is often reported. Here, we show that unintended vesicle aggregation or indeed fusion can cause apparent leakage in model studies that is not possible in most microbes, thus potentially leading to misinterpretations. The interactions of a highly charged and highly selective membrane-active polycation with negatively charged phosphatidylethanolamine/phosphatidylglycerol (PE/PG) vesicles are studied by a combination of biophysical methods. At low polycation concentrations, apparent vesicle aggregation was found to involve exchange of lipids. Upon neutralization of the negatively charged vesicles by the polycation, full fusion and leakage occurred and leaky fusion is suspected. To elucidate the interplay of leakage and fusion, we prevented membrane contacts by decorating the vesicles with PEG-chains. This inhibited fusion and also leakage activity. Leaky fusion is further corroborated by increased leakage with increasing likeliness of vesicle-vesicle contacts. Because of its similar appearance to other leakage mechanisms, leaky fusion is difficult to identify and might be overlooked and more common amongst polycationic membrane-active compounds. Regarding biological activity, leaky fusion needs to be carefully distinguished from other membrane permeabilization mechanisms, as it may be less relevant to bacteria, but potentially relevant for fungi. Furthermore, leaky fusion is an interesting effect that could help in endosomal escape for drug delivery. A comprehensive step-by-step protocol for membrane permeabilization/vesicle leakage using calcein fluorescence lifetime is provided in the ESI.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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) | 0.002 | 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 teacher head, 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".