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

Abstract

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 iDR478, a comprehensive manually curated genome-scale metabolic model (GEM) to serve as a digital twin for E. faecium TX0016 (strain DO). The reconstruction was based on 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 accurately predicted 100% of amino acid auxotrophies and 85% of 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 guide future experimental characterization. Additionally, we conducted gene essentiality analysis to evaluate the predictive capacity of iDR478. 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 unconstrained uptake conditions, iDR478 achieved 84.2% concordance with the experimental essentiality results of E. faecalis MMH594. iDR478 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.