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Cystic fibrosis (CF) is a life-threatening genetic disease characterised by chronic lung infections sustained by opportunistic pathogens such as Pseudomonas aeruginosa. During the chronic long-lasting lung infections, P. aeruginosa adapts to the host environment. Hypermutability, mainly due to defects in the DNA repair system, resulting in an increased spontaneous mutation rate, represents a way to boost the rapid adaptation frequently encountered in CF P. aeruginosa isolates. We selected 609 isolates from 51 patients with CF chronically colonised by P. aeruginosa to study, by full-length genome sequencing, the longitudinal evolution of the bacterium. We recovered at least one hypermutable (mutator) isolate in 57% of patients. By combining genomic information and phenotypic analyses, we followed the evolutionary pathways of the P. aeruginosa mutator strains, identifying their contribution to multi-drug resistance and the emergence of new sub-lineages. By implementing patient clinical data, we observed that mutators preferentially follow a specific evolutionary trajectory in patients with a negative clinical outcome and that maintenance antibiotic polytherapy, based on alternating molecules, apparently reduces the occurrence of hypermutability. Finally, we draw attention to the possibility that modulator-induced changes in the pulmonary environment may be associated with the onset of hypermutability.

The opportunistic pathogen Pseudomonas aeruginosa is often involved in airway infections of cystic fibrosis (CF) patients. It persists in the hostile CF lung environment, inducing chronic infections due to the production of several virulence factors. In this regard, the ability to form a biofilm plays a pivotal role in CF airway colonization by P. aeruginosa. Bacterial virulence mitigation and bacterial cell adhesion hampering and/or biofilm reduced formation could represent a major target for the development of new therapeutic treatments for infection control. Essential oils (EOs) are being considered as a potential alternative in clinical settings for the prevention, treatment, and control of infections sustained by microbial biofilms. EOs are complex mixtures of different classes of organic compounds, usually used for the treatment of upper respiratory tract infections in traditional medicine. Recently, a wide series of EOs were investigated for their ability to modulate biofilm production by different pathogens comprising S. aureus, S. epidermidis, and P. aeruginosa strains. Machine learning (ML) algorithms were applied to develop classification models in order to suggest a possible antibiofilm action for each chemical component of the studied EOs. In the present study, we assessed the biofilm growth modulation exerted by 61 commercial EOs on a selected number of P. aeruginosa strains isolated from CF patients. Furthermore, ML has been used to shed light on the EO chemical components likely responsible for the positive or negative modulation of bacterial biofilm formation.