Abstract The emergence of multidrug-resistant (MDR) bacterial pathogens is an alarming global health threat that demands new therapeutic strategies beyond conventional antibiotics. Here, we present a rationally designed antimicrobial peptide (AMP) derived from mammalian cathelicidins and defensins that selectively targets bacterial membranes with low cytotoxicity toward mammalian cells. Circular dichroism spectroscopy revealed that the peptide adopts an α-helical conformation upon membrane interaction, a key feature of its mechanism. Surface plasmon resonance and isothermal titration calorimetry demonstrated high-affinity and selective binding to bacterial lipid membranes. Functionally, the peptide was strongly bactericidal against clinical MDR Escherichia coli ( E. coli ) and clinically important ESKAPE pathogens ( Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.). Compared with the parent peptide LL-37, our AMP exhibited lower minimum inhibitory concentrations (MICs) and faster bactericidal kinetics across both Gram-negative and Gram-positive strains. Calcein leakage assays, showing effective membrane disruption. Importantly, cytotoxicity experiments with human epithelial (Caco-2) and immune (THP-1) cells indicated low cytotoxicity at concentrations exceeding bactericidal levels, supporting a favorable therapeutic window. ELISA quantifications of cytokines (IL-6, TNF-α) further suggested immunomodulatory effects at bactericidal concentrations. Transcriptomic profiling of E. coli treated with sub-lethal concentrations of the peptide exhibited upregulation of bacterial stress response pathways and downregulation of vital metabolic processes, reflecting the complex antimicrobial action of the peptide. Collectively, these findings highlight this LL-37-derived AMP as a promising candidate for treating MDR bacterial infections caused by E. coli and ESKAPE pathogens and for guiding the development of next-generation antimicrobial agents.
Omar Eladl (2025) studied this question.