Genome-scale metabolic model of Staphylococcus epidermidis ATCC 12228 matches in vitro conditions

Abstract

Staphylococcus epidermidis, a commensal bacterium inhabiting collagen-rich areas, like human skin, has gained significance due to its probiotic potential in the nasal microbiome and as a leading cause of nosocomial infections. While infrequently leading to severe illnesses, S. epidermidis exerts a significant influence, particularly in its close association with implant-related infections and its role as a classic opportunistic biofilm former. Understanding its opportunistic nature is crucial for developing novel therapeutic strategies, addressing both its beneficial and pathogenic aspects, and alleviating the burdens it imposes on patients and healthcare systems. Here, we employ genome-scale metabolic modeling as a powerful tool to elucidate the lifestyle and capabilities of S. epidermidis. We created a comprehensive computational resource for understanding the organisms growth conditions within diverse habitats by reconstructing and analyzing a manually curated and experimentally validated metabolic model. The final network, iSep23, incorporates 1,415 reactions, 1,051 metabolites, and 705 genes, adhering to established community standards and modeling guidelines. Benchmarking with the MEMOTE test suite yields a high score, highlighting the models high semantic quality. Following the FAIR data principles, iSep23 becomes a valuable and publicly accessible asset for subsequent studies. Growth simulations and carbon source utilization predictions align with experimental results, showcasing the models predictive power. This metabolic model advances our understanding of S. epidermidis as a commensal and potential probiotic and enhances insights into its opportunistic pathogenicity against other microorganisms. Author summaryStaphylococcus epidermidis, a bacterium commonly found on human skin, has shown probiotic effects in the nasal microbiome and is a notable causative agent of hospital-acquired infections. While typically causing non-life-threatening diseases, the economic ramifications of S. epidermidis infections are substantial, with annual costs reaching billions of dollars in the United States. To unravel its opportunistic nature, we utilized genome-scale metabolic modeling, creating a detailed mathematical network that elucidates S. epidermidiss lifestyle and capabilities. This model, encompassing over a thousand reactions, metabolites, and genes, adheres rigorously to established standards and guidelines, evident in its commendable benchmarking scores. Adhering to the FAIR data principles (Findable, Accessible, Interoperable, and Reusable), the model stands as a valuable resource for subsequent investigations. Growth simulations and predictions align closely with experimental results, showcasing the models predictive accuracy. This metabolic model not only enhances our understanding of S. epidermidis as a skin commensal and potential probiotic but also sheds light on its opportunistic pathogenicity, particularly in competition with other microorganisms.