Negatively charged phospholipids accelerate the membrane fusion activity of the plant-specific insert domain of an aspartic protease
Bibliographic record
Abstract
Various plants use antimicrobial proteins/peptides to resist phytopathogens. In the potato, Solanum tuberosum, the plant-specific insert (PSI) domain of an aspartic protease performs this role by disrupting phytopathogen plasma membranes. However, the mechanism by which PSI selects target membranes has not been elucidated. Here, we studied PSI-induced membrane fusion, focusing on the effects of lipid composition on fusion efficiency. Membrane fusion by the PSI involves an intermediate state whereby adjacent liposomes share their bilayers. We found that increasing the concentration of negatively charged phosphatidylserine (PS) phospholipids substantially accelerated PSI-mediated membrane fusion. NMR data demonstrated that PS did not affect the binding between the PSI and liposomes but had seminal effects on the dynamics of PSI interaction with liposomes. In PS-free liposomes, the PSI underwent significant motion, which was suppressed on PS-contained liposomes. Molecular dynamics simulations showed that the PSI binds to PS-containing membranes with a dominant angle ranging from -31° to 30°, with respect to the bilayer, and is closer to the membrane surfaces. In contrast, PSI is mobile and exhibits multiple topological states on the surface of PS-free membranes. Taken together, our data suggested that PS lipids limit the motion of the anchored PSI, bringing it closer to the membrane surface and efficiently bridging different liposomes to accelerate fusion. As most phytopathogens have a higher content of negatively charged lipids as compared with host cells, these results indicate that the PSI selectively targets negatively charged lipids, which likely represents a way of distinguishing the pathogen from the host.
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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".