Comparative Genomics of Antibiotic Resistance in Hospital vs. Community Bacteria

Thesis Abstract

This study investigates the differential genomic determinants of antibiotic resistance in hospital-associated and community-acquired bacterial isolates, addressing the rising concern that hospital-driven selective pressures amplify distinct resistance architectures compared with community ecosystems. The aim is to characterize and compare resistome, virulome, and mobile genetic element (MGE) dynamics across settings to identify context-specific drivers of resistance dissemination. Specific objectives include (1) sequencing and annotating whole genomes from 200 hospital-derived and 200 community-derived Gram-negative and Gram-positive strains; (2) profiling antibiotic resistance genes (ARGs), virulence factors, plasmids, transposons, integrons, and other MGEs; (3) assessing phylogenetic relationships and population structure to infer transmission potential; (4) evaluating associations between clinical metadata (prior antibiotic exposure, ward type, patient comorbidities) and genomic resistance patterns; and (5) integrating results within a theoretical framework of stewardship and ecological theory to propose targeted interventions. A cross-sectional, comparative genomics design will be employed. A total of 400 isolates will be collected from tertiary-care hospital microbiology laboratories and community clinics over 12 months, encompassing multiple species with clinical relevance (Escherichia coli, Klebsiella pneumoniae, Staphylococcus aureus, Pseudomonas aeruginosa). Genomic DNA will be sequenced using Illumina short-read technology, complemented by long-read sequencing (Oxford Nanopore) for representative isolates to resolve plasmids and MGEs. Bioinformatic pipelines will perform de novo assembly, annotation via the RAST and Prokka platforms, and resistome/virulome profiling using CARD, ResFinder, and VFDB databases. Population structure will be inferred through core-genome single nucleotide polymorphism (SNP) analysis and multilocus sequence typing (MLST). Plasmid content and MGE networks will be characterized with tools such as PlasmidFinder, MOB-suite, and graph-based pangenome analyses. Statistical associations between metadata and genomic features will be tested using multivariable logistic regression and canonical correspondence analysis, with adjustments for multiple testing (Benjamini–Hochberg). A Bayesian hierarchical model will be employed to quantify setting-specific effects on ARG enrichment, controlling for species, lineage, and sampling period. The theoretical lens will integrate the ecological network theory and the One Health framework, with regression analyses anchored by the Theory of Antimicrobial Resistance Evolution to interpret selective pressures in hospital versus community contexts. Expected findings include higher diversity and abundance of ARGs and MGEs in hospital isolates, with greater prevalence of plasmid-borne carbapenemases and ESBLs in Enterobacterales, and distinctive virulence-resistance cassettes associated with healthcare-associated lineages. Community isolates are anticipated to exhibit a broader but less intense resistome, with chromosomal mutations contributing to resistance in endemic community lineages. Phylogenomic analyses are expected to reveal tighter clustering of hospital strains within wards indicative of nosocomial transmission, contrasted with more diffuse population structures in the community. The study will identify specific ARGs and mobile elements significantly associated with prior antibiotic exposure and ward type, informing stewardship priorities. The study contributes to knowledge by elucidating context-dependent resistome architecture and MGE dynamics, enabling precise risk stratification of resistance dissemination between hospital and community environments, and informing targeted infection control and antibiotic stewardship policies. The conclusions will emphasize the need for integrated surveillance that combines genomic monitoring with clinical metadata, advocating routine sequencing of hospital and community isolates to detect emergent high-risk clones and MGEs early. Recommendations include strengthening hospital-wide infection control measures, tailoring empiric therapy guidelines to local resistome profiles, and extending One Health surveillance to interconnect hospital, community, and environmental reservoirs to curb the spread of antibiotic resistance.