Microbiological and genomic approaches to study and characterize co- and cross-resistance to antimicrobial peptides in Listeria monocytogenes ATCC 19,111 variants

Abstract

Abstract Background Antimicrobial peptides are considered a promising alternative to conventional antibiotics for controlling Listeria monocytogenes , a major foodborne pathogen. However, the emergence of antimicrobial resistance, including co- and cross-resistance between structurally and functionally distinct peptides, remains a critical and insufficiently explored challenge. This study aimed to investigate and characterize the mechanisms underlying co- and cross-resistance among five distinct antimicrobial peptides: nisin Z, pediocin PA-1, plantaricin S, brevibacillin, and its synthetic analog brevibacillin Thr1, selected for their diverse structures and modes of action. Results All tested peptides exhibited strong anti-listerial activity, with minimum inhibitory concentrations ranging from 0.5 to 15 µM and minimum bactericidal concentrations from 1 to 60 µM. Resistant variants were generated through stepwise adaptive evolution by exposing Listeria monocytogenes ATCC 19,111 to gradually increasing peptide concentrations. These variants were characterized based on resistance levels, growth rates, and resistance stability. No cross-resistance was observed between antimicrobial peptides and conventional antibiotics. However, co-resistance and cross-resistance were identified among certain peptides, particularly those sharing structural or functional similarities, suggesting overlapping or convergent resistance mechanisms. Whole-genome sequencing and metabolic analyses revealed distinct genetic mutations associated with resistance phenotypes, along with specific alterations in metabolic pathways. Conclusion This study demonstrates that Listeria monocytogenes ATCC 19,111 can develop co- and cross-resistance to antimicrobial peptides, especially among structurally or functionally related compounds. The findings highlight the complexity of resistance mechanisms and emphasize the importance of integrating genomic and metabolic approaches to better understand adaptive responses to antimicrobial pressure. These insights may inform the development of more effective antimicrobial strategies and support the rational design of peptide-based interventions to control foodborne pathogens.