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 ex...
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| Language: | English |
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American Society for Microbiology
2025-06-01
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| Series: | mSystems |
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| Online Access: | https://journals.asm.org/doi/10.1128/msystems.00418-25 |
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| author | Nantia Leonidou Alina Renz Benjamin Winnerling Anastasiia Grekova Fabian Grein Andreas Dräger |
| author_facet | Nantia Leonidou Alina Renz Benjamin Winnerling Anastasiia Grekova Fabian Grein Andreas Dräger |
| author_sort | Nantia Leonidou |
| collection | DOAJ |
| description | 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 metabolic capabilities of S. epidermidis. We created a comprehensive computational resource for understanding the organism’s 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 Metabolic Model Testing suite yields a high score, indicating the model’s remarkable semantic quality. Following the findable, accessible, interoperable, and reusable (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 model’s predictive power. Ultimately, this work provides a robust foundation for future research aimed at both exploiting the probiotic potential and mitigating the pathogenic risks posed by S. epidermidis.IMPORTANCEStaphylococcus 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 these infections are typically non-life-threatening, their economic impact is considerable, with annual costs reaching billions of dollars in the United States. To better understand its opportunistic nature, we employed genome-scale metabolic modeling to construct a detailed network of S. epidermidis’s metabolic capabilities. This model, comprising over a thousand reactions, metabolites, and genes, adheres to established standards and demonstrates solid benchmarking performance. Following the findable, accessible, interoperable, and reusable (FAIR) data principles, the model provides a valuable resource for future research. Growth simulations and predictions closely match experimental data, underscoring the model’s predictive accuracy. Overall, this work lays a solid foundation for future studies aimed at leveraging the beneficial properties of S. epidermidis while mitigating its pathogenic potential. |
| format | Article |
| id | doaj-art-c53fd5036f3044e3a54b265cd5c20f2b |
| institution | DOAJ |
| issn | 2379-5077 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | American Society for Microbiology |
| record_format | Article |
| series | mSystems |
| spelling | doaj-art-c53fd5036f3044e3a54b265cd5c20f2b2025-08-20T02:40:14ZengAmerican Society for MicrobiologymSystems2379-50772025-06-0110610.1128/msystems.00418-25Genome-scale metabolic model of Staphylococcus epidermidis ATCC 12228 matches in vitro conditionsNantia Leonidou0Alina Renz1Benjamin Winnerling2Anastasiia Grekova3Fabian Grein4Andreas Dräger5Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karl University of Tübingen, Tübingen, GermanyInstitute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karl University of Tübingen, Tübingen, GermanyInstitute for Pharmaceutical Microbiology, University of Bonn, Bonn, North Rhine-Westphalia, GermanyStructural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Baden-Württemberg, GermanyInstitute for Pharmaceutical Microbiology, University of Bonn, Bonn, North Rhine-Westphalia, GermanyInstitute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karl University of Tübingen, Tübingen, GermanyABSTRACT 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 metabolic capabilities of S. epidermidis. We created a comprehensive computational resource for understanding the organism’s 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 Metabolic Model Testing suite yields a high score, indicating the model’s remarkable semantic quality. Following the findable, accessible, interoperable, and reusable (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 model’s predictive power. Ultimately, this work provides a robust foundation for future research aimed at both exploiting the probiotic potential and mitigating the pathogenic risks posed by S. epidermidis.IMPORTANCEStaphylococcus 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 these infections are typically non-life-threatening, their economic impact is considerable, with annual costs reaching billions of dollars in the United States. To better understand its opportunistic nature, we employed genome-scale metabolic modeling to construct a detailed network of S. epidermidis’s metabolic capabilities. This model, comprising over a thousand reactions, metabolites, and genes, adheres to established standards and demonstrates solid benchmarking performance. Following the findable, accessible, interoperable, and reusable (FAIR) data principles, the model provides a valuable resource for future research. Growth simulations and predictions closely match experimental data, underscoring the model’s predictive accuracy. Overall, this work lays a solid foundation for future studies aimed at leveraging the beneficial properties of S. epidermidis while mitigating its pathogenic potential.https://journals.asm.org/doi/10.1128/msystems.00418-25genome-scale metabolic modelingS. epidermidisGram positiveskin mcirobiotanasal microbiotasystems biology |
| spellingShingle | Nantia Leonidou Alina Renz Benjamin Winnerling Anastasiia Grekova Fabian Grein Andreas Dräger Genome-scale metabolic model of Staphylococcus epidermidis ATCC 12228 matches in vitro conditions mSystems genome-scale metabolic modeling S. epidermidis Gram positive skin mcirobiota nasal microbiota systems biology |
| title | Genome-scale metabolic model of Staphylococcus epidermidis ATCC 12228 matches in vitro conditions |
| title_full | Genome-scale metabolic model of Staphylococcus epidermidis ATCC 12228 matches in vitro conditions |
| title_fullStr | Genome-scale metabolic model of Staphylococcus epidermidis ATCC 12228 matches in vitro conditions |
| title_full_unstemmed | Genome-scale metabolic model of Staphylococcus epidermidis ATCC 12228 matches in vitro conditions |
| title_short | Genome-scale metabolic model of Staphylococcus epidermidis ATCC 12228 matches in vitro conditions |
| title_sort | genome scale metabolic model of staphylococcus epidermidis atcc 12228 matches in vitro conditions |
| topic | genome-scale metabolic modeling S. epidermidis Gram positive skin mcirobiota nasal microbiota systems biology |
| url | https://journals.asm.org/doi/10.1128/msystems.00418-25 |
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