Leonidou Nantia, Renz Alina, Winnerling Benjamin, Grekova Anastasiia, Grein Fabian, Dräger Andreas
Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karl University of Tübingen, Tübingen, Germany.
Department of Computer Science, Eberhard Karl University of Tübingen, Tübingen, Germany.
mSystems. 2025 Jun 17;10(6):e0041825. doi: 10.1128/msystems.00418-25. Epub 2025 May 21.
, 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, 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 . 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, Sep23, 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, Sep23 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 .
, 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 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 while mitigating its pathogenic potential.
，一种栖息于富含胶原蛋白区域（如人体皮肤）的共生细菌，因其在鼻腔微生物群中的益生菌潜力以及作为医院感染的主要原因而变得重要。虽然很少导致严重疾病，但 具有重大影响，特别是在其与植入物相关感染的密切关联以及作为经典机会性生物膜形成者的作用方面。了解其机会主义性质对于制定新的治疗策略、解决其有益和致病方面以及减轻其给患者和医疗系统带来的负担至关重要。在这里，我们采用基因组规模代谢建模作为一种强大工具来阐明 的代谢能力。通过重建和分析一个经过人工策划和实验验证的代谢模型，我们创建了一个全面的计算资源，用于了解该生物体在不同栖息地的生长条件。最终的网络Sep23包含1415个反应、1051个代谢物和705个基因，符合既定的社区标准和建模指南。使用代谢模型测试套件进行基准测试得到高分，表明该模型具有卓越的语义质量。遵循可查找、可访问、可互操作和可重用（FAIR）数据原则，Sep23成为后续研究的宝贵且可公开访问的资产。生长模拟和碳源利用预测与实验结果一致，展示了该模型的预测能力。最终，这项工作为未来旨在利用 的益生菌潜力并减轻其致病风险的研究提供了坚实基础。
，一种常见于人体皮肤的细菌，已在鼻腔微生物群中显示出益生菌作用，并且是医院获得性感染的显著病原体。虽然这些感染通常不会危及生命，但其经济影响相当大，在美国每年的成本达到数十亿美元。为了更好地了解其机会主义性质，我们采用基因组规模代谢建模来构建 的代谢能力的详细网络。这个模型包含一千多个反应、代谢物和基因，符合既定标准并展示出可靠的基准测试性能。遵循可查找、可访问、可互操作和可重用（FAIR）数据原则，该模型为未来研究提供了宝贵资源。生长模拟和预测与实验数据密切匹配，强调了该模型的预测准确性。总体而言，这项工作为未来旨在利用 的有益特性同时减轻其致病潜力的研究奠定了坚实基础。
