GET THE APP

Deciphering the therapeutic code of cationic peptides to ove | 56110

Jornal de Microbiologia e Imunologia

Abstrato

Deciphering the therapeutic code of cationic peptides to overcome bacterial resistance to traditional antibiotics: the tryptophan impact

Wenyu Xiang, Jessie Klousnitzer, Patrice Clemenza, Berthony Deslouches

Infections associated with multidrug-resistant (MDR) bacteria constitute an imminent health crisis (WHO, 2014). Antimicrobial peptides (AMPs) are essential components of the innate immunity of most multicellular organisms with the potential to overcome bacterial resistance mechanisms to traditional antibiotics. The contextual activity of AMPs constitutes a major challenge to their development for systemic administration, particularly for hospitalized patients requiring intravenous therapy. To address these challenges, we have developed a rational framework for de novo-designed helical AMPs to titrate the structure-function relationships under different physiologically-relevant conditions. Our lead Trp-rich peptides from multiple libraries remained active against most MDR clinical isolates from the Center for Disease Control and Prevention. In resistance selection assays under antimicrobial selective pressure, S. aureus developed resistance to cell wall inhibitor antibiotics in contrast to our select lead AMPs with no emergence of resistance phenotypes. These peptides interact with negatively charged lipids on the bacterial surface prior to rapid disruption of the membranes. More importantly, systemic in vivo efficacy was demonstrated in an otherwise lethal bacterial sepsis model in mice with 100% survival. Lessons learned from these systematic structure-function and pre-clinical studies indicate the need for continuously enhancing the cationic amphipathic structure using our iterative rational framework for increased therapeutic index and safety to overcome resistance by incessantly evolving bacterial pathogens.

Isenção de responsabilidade : Este resumo foi traduzido utilizando ferramentas de inteligência artificial e ainda não foi revisado ou verificado.