P247 Advances in cystic fibrosis lung infection modeling: profiling microbial interactions at micro-scale distances

Intra- and interspecies diversities characterize the mixed bacterial populations usually observed in Cystic Fibrosis (CF) sputum samples. Intra-clonal diversity of specific Pseudomonas aeruginosa (Pa) strains in CF lungs, as identified by mutations across the genome is driven by interactions with other microbes in local micro-niches. As an example of intra-clonal interactions, co-existence of Quorum Sensing (QS) positive and negative variants in the Pa population may arise from the generation of variants that benefit from signal molecules produced/excreted by nonmutated bacteria. Whilst for interspecies interaction, the Type VI secretion system of Pa exhibits an antagonist effect against Staphylococcus aureus, proposed as an explanation of Pa’s late dominance in late stages of CF lung infections. However, conclusive evidence supporting these suggestions is lacking.

We have developed a CF lung infection model using Air-Liquid-Interphase (ALI) epithelial cultures, which mimics human CF lung tissue. Furthermore, we apply 3D bioprinting, to precisely print bacteria onto ALI cultures to study microbial interactions and analyse the ecological impacts of altering spatial configurations. This innovative approach allows investigations of complementation, competition, antibiotic tolerance, QS molecules, and Type VI secretion toxins at predefined micron-scale distances.

We have optimized 3D bacterial bioprinting of relevant fluorescently stained bacterial populations on ALI cultures. Current results include printing of uniform and reproducible bioink droplets, successful biocompatibility with PAO1, a reference strain of Pa and airway epithelial cells, and printing of two different PAO1 strains at micro-scale distances.
Employing our innovative CF lung infection model and 3D bioprinting technology, we aim to profile microbe-microbe interactions impacting on the infection process to direct our understandings towards more effective and personalized infection control.