A synthetic peptide mimic kills Candida albicans and synergistically prevents infection.
Sebastian SchaeferRaghav VijJakob L SpragueSophie AustermeierHue DinhPeter R JudzewitschSven Müller-LoenniesTaynara Lopes SilvaEric SeemannBritta QualmannChristian HertweckKirstin ScherlachThomas GutsmannAmy K CainNathaniel CorriganMark S GresnigtCyrille A BoyerMegan D LenardonSascha BrunkePublished in: Nature communications (2024)
More than two million people worldwide are affected by life-threatening, invasive fungal infections annually. Candida species are the most common cause of nosocomial, invasive fungal infections and are associated with mortality rates above 40%. Despite the increasing incidence of drug-resistance, the development of novel antifungal formulations has been limited. Here we investigate the antifungal mode of action and therapeutic potential of positively charged, synthetic peptide mimics to combat Candida albicans infections. Our data indicates that these synthetic polymers cause endoplasmic reticulum stress and affect protein glycosylation, a mode of action distinct from currently approved antifungal drugs. The most promising polymer composition damaged the mannan layer of the cell wall, with additional membrane-disrupting activity. The synergistic combination of the polymer with caspofungin prevented infection of human epithelial cells in vitro, improved fungal clearance by human macrophages, and significantly increased host survival in a Galleria mellonella model of systemic candidiasis. Additionally, prolonged exposure of C. albicans to the synergistic combination of polymer and caspofungin did not lead to the evolution of tolerant strains in vitro. Together, this work highlights the enormous potential of these synthetic peptide mimics to be used as novel antifungal formulations as well as adjunctive antifungal therapy.
Keyphrases
- candida albicans
- cell wall
- endoplasmic reticulum stress
- biofilm formation
- endothelial cells
- risk factors
- induced apoptosis
- cancer therapy
- cardiovascular disease
- stem cells
- risk assessment
- big data
- electronic health record
- human health
- machine learning
- mouse model
- mesenchymal stem cells
- small molecule
- drug resistant
- staphylococcus aureus
- smoking cessation