Integrated FTIR and Whole-Genome Sequencing Reveal Scale-Dependent Genotype–Phenotype Relationships in Multidrug-Resistant Pseudomonas aeruginosa

Multidrug-resistant Pseudomonas aeruginosa is a major cause of healthcare-associated infections, particularly in high-burden clinical settings where rapid tools to capture clinically relevant resistance and virulence phenotypes are needed. In this study, we applied an integrated whole-genome sequencing (WGS) and Fourier-transform infrared (FTIR) spectroscopy approach to evaluate genotype–phenotype relationships in multidrug-resistant P. aeruginosa isolates collected during the COVID-19 pandemic. High-quality WGS data were used to characterize antimicrobial resistance determinants, mobile genetic elements, and virulence gene repertoires, while FTIR spectroscopy provided culture-based phenotypic fingerprints reflecting cell envelope composition. Genomic analyses revealed a conserved efflux-centered intrinsic resistance backbone, variably supplemented by acquired β-lactamases and aminoglycoside-modifying enzymes, alongside a largely conserved core virulome with heterogeneity driven primarily by type III secretion system effector profiles. Comparison of FTIR- and WGS-derived distance matrices revealed a weak but statistically significant global association, indicating a non-linear relationship between genomic relatedness and phenotypic similarity. Cluster-level concordance was strongly scale-dependent, with high agreement emerging only at finer clustering resolutions, consistent with FTIR capturing phenotypic variation linked to regulatory, metabolic, and cell envelope adaptations rather than deep phylogenetic structure. Together, these findings show that multidrug resistance and virulence in P. aeruginosa are shaped by a modular genomic architecture that manifests as distinct, measurable phenotypic states. The observed scale-dependent concordance supports FTIR spectroscopy as a rapid, cost-effective phenotypic screening tool for outbreak-oriented surveillance, complementing WGS in integrated antimicrobial resistance monitoring workflows.