Andrea M. Smania

This study shows that antibiotic selection in a human airway tissue model of Pseudomonas aeruginosa leads to evolution of less virulent, tissue-preserving infections with enhanced survival.

This study disrupts all 32 GGDEF domain-containing proteins in P. aeruginosa to analyze c-di-GMP signaling, revealing residual c-di-GMP levels and the importance of functional overlap in bacterial signaling.

The paper develops a machine learning workflow for auto-deconvolution of GC-MS data within GNPS, enabling molecular networking and applications in breathomics and volatilome mapping.