The Impact of Antimicrobial Peptides on the <i>Acinetobacter baumannii</i> Inner Membrane Is Modulated by Lipid Polyunsaturation
Bibliographic record
Abstract
The Gram-negative pathogen Acinetobacter baumannii is a primary contributor to nosocomial multi-drug-resistant (MDR) infections. To combat the rise of MDR infections, novel features of A. baumannii need to be considered for the development of new treatment options. One such feature is the preferential scavenging of exogenous lipids, including host-derived polyunsaturated fatty acids (PUFAs), for membrane phospholipid synthesis. These alterations in membrane composition impact both the lipid chemistry and the membrane biophysical properties. In this work we examine how antimicrobial peptides (AMPs) interact with the inner membranes of A. baumannii in the presence and absence of polyunsaturated phospholipids. Using coarse-grained molecular dynamics simulations of complex A. baumannii inner membrane models derived from lipidomes of bacteria grown in the presence and absence of PUFAs, we examine the impact of the adsorption of four prototypical AMPs (CAMEL, LL-37, pexiganan, and magainin-2) on the membrane biophysical properties. Our simulations reveal that the impact of AMP adsorption on the membrane biophysical properties was dependent on both the membrane composition and the specific AMP involved. Both lipid headgroup charge and tail unsaturation played important roles in driving the interactions that occurred both within the membrane and between the membrane and AMPs. The changes to the membrane biophysical properties also showed a complex relationship with the AMP’s physical properties, such as AMP charge, chain length, and charge-to-mass ratio. Cumulatively, this work highlights the importance of studying AMPs using a complex membrane environment and provides insights into the mechanistic action of AMPs in polyunsaturated lipid-rich bacterial membranes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".