Emergence of <i>Staphylococcus aureus</i> Resistance to Antimicrobial Peptides Nisin, NZ2114 and Bacitracin Involves Multiple Phenotypic Changes
Bibliographic record
Abstract
ABSTRACT The rise of antibiotic‐resistant bacteria has intensified global interest in antimicrobial peptides (AMPs) as promising feed additives. Although AMPs were initially considered less prone to resistance due to their broad‐spectrum activity, recent studies have revealed an alarming increase in bacterial resistance to AMPs, though the mechanisms remain poorly understood. In this study, we demonstrate that Staphylococcus aureus can develop stable resistance to the plectasin‐derived AMP NZ2114, as well as nisin and bacitracin, after 35 consecutive days of exposure. Comparative genomic analysis identified five candidate genes associated with resistance, with functional assays revealing significant mutations in ndh (Gln287*), lytD (Ala138Thr), and braS (Asn130Asp) as key contributors. Knockout studies showed that Δ ndh strains exhibited increased resistance to NZ2114, bacitracin, and nisin, alongside reduced intracellular ROS levels and rifampicin mutation rates. In contrast, Δ lytD and Δ braS mutants displayed diminished resistance to NZ2114 and bacitracin, with enhanced biofilm formation in Δ lytD and reduced biofilm capacity in Δ braS . To further investigate these mutations, we generated in situ complementation strains ∆ :: lytD‐ A138T and ∆:: braS‐ N130D, both of which showed heightened resistance compared to wild type, indicating that functional alterations, rather than gene loss, mediate resistance. Notably, resistance phenotypes correlated inversely with bacterial surface anion levels, emphasizing the importance of electrostatic interactions between cationic AMPs and bacterial surface anions in antimicrobial efficacy. These findings provide novel insights into the mechanisms of AMP resistance in S. aureus , highlighting the risk of cross‐resistance and underscoring the need for stringent control of AMP use to mitigate the emergence of resistance.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".