Transcriptome-Integrated Metabolic Modeling Identifies Candidate Metabolic Adjuvants in Antibiotic-Resistant...

Background/Objectives: Antimicrobial resistance (AMR) poses a major global health challenge, particularly in opportunistic pathogens such as Pseudomonas aeruginosa. This study aimed to identify metabolic adaptations associated with antibiotic resistance by integrating transcriptomic data from drug-resistant clinical isolates with a genome-scale metabolic model (GEM) of P. aeruginosa under four antibiotic treatments: ceftazidime (CAZ), ciprofloxacin (CIP), meropenem (MEM), and tobramycin (TOB). Methods: Transcriptomic data from 414 clinical isolates were integrated with the iPau21 genome-scale metabolic model (GEM) of P. aeruginosa. Differential gene expression analysis was performed using DESeq2, and differentially expressed genes (DEGs) were identified using a false discovery rate (FDR)-adjusted p-value < 0.05 and a fold-change threshold of ≥2 or ≤0.5. Reporter metabolites (RMs) were identified using the Reporter Metabolite algorithm with an FDR-adjusted p-value < 0.05. Pathway enrichment analysis was performed to characterize condition-specific metabolic alterations, and pathway significance was determined using the Benjamini-Hochberg procedure with an adjusted p-value < 0.05. Results: The analysis revealed predominantly antibiotic-specific transcriptional responses, with limited overlap in DEGs across treatment conditions. Reporter metabolite and pathway enrichment analyses identified distinct metabolic adaptations associated with biofilm formation, virulence, and stress response pathways. Several metabolites, including propionic acid, acetic acid, L-inositol, glutamine, glutarate, fumarate, and melatonin, were computationally prioritized candidate metabolites for future metabolite-based adjuvant strategies aimed at enhancing antibiotic efficacy. Conclusions: This systems biology approach provides a comprehensive framework for identifying metabolic vulnerabilities associated with AMR in P. aeruginosa. The identified metabolites represent candidate antibiotic adjuvant molecules generated through computational prioritization and should be regarded as hypotheses for future experimental validation rather than validated therapeutic interventions. These findings provide a foundation for future studies exploring metabolism-based strategies to improve antibiotic efficacy and combat antimicrobial resistance.