Lipid A in outer membrane vesicles shields bacteria from polymyxins.

The continuous emergence of multidrug-resistant bacterial pathogens poses a major global healthcare challenge, with Klebsiella pneumoniae being a prominent threat. We conducted a comprehensive study on K. pneumoniae's antibiotic resistance mechanisms, focusing on outer membrane vesicles (OMVs) and polymyxin, a last-resort antibiotic. Our research demonstrates that OMVs protect bacteria from polymyxins. OMVs derived from Polymyxin B (PB)-stressed K. pneumoniae exhibited heightened protective efficacy due to increased vesiculation, compared to OMVs from unstressed Klebsiella. OMVs also shield bacteria from different bacterial families. This was validated ex vivo and in vivo using precision cut lung slices (PCLS) and Galleria mellonella. In all models, OMVs protected K. pneumoniae from PB and reduced the associated stress response on protein level. We observed significant changes in the lipid composition of OMVs upon PB treatment, affecting their binding capacity to PB. The altered binding capacity of single OMVs from PB stressed K. pneumoniae could be linked to a reduction in the lipid A amount of their released vesicles. Although the amount of lipid A per vesicle is reduced, the overall increase in the number of vesicles results in an increased protection because the sum of lipid A and therefore PB binding sites have increased. This unravels the mechanism of the altered PB protective efficacy of OMVs from PB stressed K. pneumoniae compared to control OMVs. The lipid A-dependent protective effect against PB was confirmed in vitro using artificial vesicles. Moreover, artificial vesicles successfully protected Klebsiella from PB ex vivo and in vivo. The findings indicate that OMVs act as protective shields for bacteria by binding to polymyxins, effectively serving as decoys and preventing antibiotic interaction with the cell surface. Our findings provide valuable insights into the mechanisms underlying antibiotic cross-protection and offer potential avenues for the development of novel therapeutic interventions to address the escalating threat of multidrug-resistant bacterial infections.

Cell-free biosynthesis combined with deep learning accelerates de novo-development of antimicrobial peptides.

Bioactive peptides are key molecules in health and medicine. Deep learning holds a big promise for the discovery and design of bioactive peptides. Yet, suitable experimental approaches are required to validate candidates in high throughput and at low cost. Here, we established a cell-free protein synthesis (CFPS) pipeline for the rapid and inexpensive production of antimicrobial peptides (AMPs) directly from DNA templates. To validate our platform, we used deep learning to design thousands of AMPs de novo. Using computational methods, we prioritized 500 candidates that we produced and screened with our CFPS pipeline. We identified 30 functional AMPs, which we characterized further through molecular dynamics simulations, antimicrobial activity and toxicity. Notably, six de novo-AMPs feature broad-spectrum activity against multidrug-resistant pathogens and do not develop bacterial resistance. Our work demonstrates the potential of CFPS for high throughput and low-cost production and testing of bioactive peptides within less than 24 h.