Genomic epidemiology of emerging and re-emerging pathogens

The application of Next-Generation Sequencing technologies and advanced bioinformatics is becoming increasingly widespread in clinical microbiology to enhance epidemiological surveillance and optimize patient management. This PhD thesis explores its integration in practice and is articulated through three levels of investigation: genomic surveillance, outbreak investigation, and translational clinical applications. The first level (Chapters 3 and 4) explores the long- and short-term epidemiology of MDR pathogens. Chapter 3 presents nine-year genomic surveillance of carbapenem-resistant Klebsiella pneumoniae which revealed a dynamic population structure. The population was characterized by the replacement of historical lineages with emerging high-risk clones, whose evolutionary success is sustained by high plasmid plasticity. Chapter 4 extends the analysis to all carbapenem-resistant Enterobacterales isolated during one year, highlighting the role of low-prevalence species as cryptic resistance reservoirs. The second level (Chapters 5 and 6) applies high-resolution genomics to outbreak investigation. Chapter 5 describes the emergence of the NDM-1-producing ST6668 clone, marking a critical local epidemiological shift towards the dissemination of metallo-beta-lactamases. Chapter 6 presents the investigation of an Acinetobacter baumannii outbreak during the COVID-19 pandemic. This analysis showed that transmission was not driven by intrinsic bacterial characteristics, but rather by the extrinsic pressure exerted by the health emergency on infection control practices. Finally, the third level (Chapters 7 and 8) shifts the focus from epidemiology to precision medicine. In Chapter 7 is evaluated the clinical impact of reorganizing the laboratory into a 24/7 workflow, demonstrating how reduced Turnaround Times facilitate timely antimicrobial stewardship. In Chapter 8, targeted metagenomics was applied to study the gut microbiota in hematological patients, identifying taxonomic signatures associated with therapeutic response, suggesting the potential of the microbiota as a non-invasive predictive biomarker.