The first digital twin of Enterococcus faecium metabolism reproduces high-throughput phenotyping data

Enterococci are Gram-positive opportunistic pathogens responsible for a wide range of nosocomial infections. One enterococcocal species, Enterococcus faecium, is steadily increasing in prevalence and has been listed among major multidrug-resistant ESKAPE pathogens. To gain systems-level insights into its metabolism and support discovery of potential therapeutic targets, we constructed iDR479, a comprehensive manually curated genome-scale metabolic model (GEM) to serve as a digital twin for E. faecium TX0016 (strain DO). The reconstruction was curated through extensive homology searches and literature evidence, and further refined and gap-filled through experimental validation. Phenotypic profiling using Biolog microarrays enabled assessment of carbon source utilization, while amino acid leave-out growth assays allowed the evaluation of auxotrophies. The final refined model is 100% accurate in predicting amino acid auxotrophy and 85% accurate in predicting growth on sole carbon sources. Discrepancies between model predictions and experimental phenotypes identified specific knowledge gaps across metabolic pathways, including unresolved carbon source utilization phenotypes, e.g., psicose, sorbitol, and palatinose utilization. Those gaps will guided future experimental characterization. Additionally, gene essentiality analysis was conducted to evaluate the predictive capacity of iDR479 model. Since no experimental gene essentiality data are currently available for E. faecium, model predictions were compared against Tn-seq experimental results from E. faecalis MMH594. Under simulated rich medium conditions, iDR479 achieved 86.7% concordance with the experimental essentiality results of E. faecalis MMH594. iDR479 thus provides a framework for studying E. faecium, offers insights into its metabolic network, and serves as a source for guiding future research and identification of therapeutic targets.