Antimicrobial and Antibiofilm Activity and Machine Learning Classification Analysis of Essential Oils from Different Mediterranean Plants against Pseudomonas aeruginosa

Marco Artini, Alexandros Patsilinakos, Rosanna Papa, Mijat Božović, Manuela Sabatino, Stefania Garzoli, Gianluca Vrenna, Marco Tilotta, Federico Pepi, Rino Ragno, Laura Selan
2018 Molecules  
Pseudomonas aeruginosa is a ubiquitous organism and opportunistic pathogen that can cause persistent infections due to its peculiar antibiotic resistance mechanisms and to its ability to adhere and form biofilm. The interest in the development of new approaches for the prevention and treatment of biofilm formation has recently increased. The aim of this study was to seek new non-biocidal agents able to inhibit biofilm formation, in order to counteract virulence rather than bacterial growth and
more » ... void the selection of escape mutants. Herein, different essential oils extracted from Mediterranean plants were analyzed for their activity against P. aeruginosa. Results show that they were able to destabilize biofilm at very low concentration without impairing bacterial viability. Since the action is not related to a bacteriostatic/bactericidal activity on P. aeruginosa, the biofilm change of growth in presence of the essential oils was possibly due to a modulation of the phenotype. To this aim, application of machine learning algorithms led to the development of quantitative activity-composition relationships classification models that allowed to direct point out those essential oil chemical components more involved in the inhibition of biofilm production. The action of selected essential oils on sessile phenotype make them particularly interesting for possible applications such as prevention of bacterial contamination in the community and in healthcare environments in order to prevent human infections. We assayed 89 samples of different essential oils as P. aeruginosa anti-biofilm. Many samples inhibited P. aeruginosa biofilm at concentrations as low as 48.8 µg/mL. Classification of the models was developed through machine learning algorithms.
doi:10.3390/molecules23020482 pmid:29473844 fatcat:6gmpkhvebfc4rigmvwswyt6u5i