Genome analysis of Streptococcus pyogenes associated with infections in North-West Italy And Development of a high-throughput phenotypic screening framework and its application to Staphylococcus aureus isolates from cystic fibrosis patients

Respiratory infections constitute a major global health burden and are caused by a wide range of bacterial pathogens. The clinical behaviour of those pathogens is strongly influenced by strain-level genetic variation and host-associated environments. Among these, Gram-positive cocci such as Streptococcus pyogenes and Staphylococcus aureus represent two pathogens responsible for a diverse range of human infections, from mild skin and throat infections to respiratory infections. Understanding how respiratory pathogens adapt, persist, and cause disease requires integrated analyses that combine genomic approaches with clinically relevant models. The first part of this work presents a comparative genomic analysis of five strains of Streptococcus pyogenes isolated from diverse clinical sources after an outbreak in in the North-West of Italy. With the result of the Whole Genome Sequencing of these strains, their genomic diversity, virulence-associated genes, and strain-specific features were analysed, and a phylogenetic tree was assembled to see if there was any relationship between the samples. This study provides an insight into molecular determinants that may influence pathogenic potential and the progression from localized upper respiratory infection to more severe disease. The second part of the dissertation examines Staphylococcus aureus in patients with cystic fibrosis. The aim of this study is to investigate the behaviour of Staphylococcus aureus isolates obtained from patients with and without cystic fibrosis by evaluating their growth and adaptation across different conditions relevant for colonization of the respiratory tract. To have further information about their behaviour, they were compared with laboratory reference strains. To this end, the high-throughput capabilities of a robotic platform were leveraged to enable a standardised, reproducible, and systematic assessment of bacterial responses across multiple growth conditions. This work seeks to examine, from complementary perspectives, how bacterial pathogens adapt to the human host, persist over time, and ultimately contribute to disease, integrating insights derived from both genomic analyses and host-associated models.