Nanoparticles of Short Cationic Peptidopolysaccharide Self-Assembled by Hydrogen Bonding with Antibacterial Effect against Multidrug-Resistant Bacteria
Bibliographic record
Abstract
Cationic antimicrobial peptides (AMPs) and polymers are active against many multidrug-resistant (MDR) bacteria, but only a limited number of these compounds are in clinical use due to their unselective toxicity. The typical strategy for achieving selective antibacterial efficacy with low mammalian cell toxicity is through balancing the ratio of cationicity to hydrophobicity. Herein, we report a cationic nanoparticle self-assembled from chitosan-graft-oligolysine (CSM5-K5) chains with ultralow molecular weight (1450 Da) that selectively kills bacteria. Further, hydrogen bonding rather than the typical hydrophobic interaction causes the polymer chains to be aggregated together in water into small nanoparticles (with about 37 nm hydrodynamic radius) to concentrate the cationic charge of the lysine. When complexed with bacterial membrane, these cationic nanoparticles synergistically cluster anionic membrane lipids and produce a greater membrane perturbation and antibacterial effect than would be achievable by the same quantity of charge if dispersed in individual copolymer molecules in solution. The small zeta potential (+15 mV) and lack of hydrophobicity of the nanoparticles impedes the insertion of the copolymer into the cell bilayer to improve biocompatibility. In vivo study (using a murine excisional wound model) shows that CSM5-K5 suppresses the growth of methicillin-resistant Staphylococcus aureus (MRSA) bacteria by 4.0 orders of magnitude, an efficacy comparable to that of the last resort MRSA antibiotic vancomycin; it is also noninflammatory with little/no activation of neutrophils (CD11b and Ly6G immune cells). This study demonstrates a promising new class of cationic polymers-short cationic peptidopolysaccharides-that effectively attack MDR bacteria due to the synergistic clustering of, rather than insertion into, bacterial anionic lipids by the concentrated polymers in the resulting hydrogen-bonding-stabilized cationic nanoparticles.
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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".