Renz Alina, Widerspick Lina, Dräger Andreas
Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, 72076 Tübingen, Germany.
Department of Computer Science, University of Tübingen, 72076 Tübingen, Germany.
Metabolites. 2021 Apr 9;11(4):232. doi: 10.3390/metabo11040232.
is a quite recently discovered Gram-positive coccus. It has gained increasing attention due to its negative correlation with , which is one of the most successful modern pathogens causing severe infections with tremendous morbidity and mortality due to its multiple resistances. As the possible mechanisms behind its inhibition of remain unclear, a genome-scale metabolic model (GEM) is of enormous interest and high importance to better study its role in this fight. This article presents the first GEM of , which was curated using automated reconstruction tools and extensive manual curation steps to yield a high-quality GEM. It was evaluated and validated using all currently available experimental data of . With this model, already predicted auxotrophies and biosynthetic pathways could be verified. The model was used to define a minimal medium for further laboratory experiments and to predict various carbon sources' growth capacities. This model will pave the way to better understand 's role in the fight against .
是一种最近才发现的革兰氏阳性球菌。由于它与[某种病原体]呈负相关，它越来越受到关注。[某种病原体]是最成功的现代病原体之一，因其多重耐药性导致严重感染，发病率和死亡率极高。由于其抑制[某种病原体]背后的可能机制尚不清楚，基因组规模代谢模型（GEM）对于更好地研究其在这场斗争中的作用极具意义且至关重要。本文展示了首个[该球菌名称]的GEM，它是使用自动化重建工具和广泛的人工编辑步骤精心策划而成，以产生高质量的GEM。它使用了[该球菌名称]所有当前可用的实验数据进行评估和验证。利用该模型，已经预测的营养缺陷型和生物合成途径得以验证。该模型用于定义用于进一步实验室实验的基本培养基，并预测各种碳源的生长能力。该模型将为更好地理解[该球菌名称]在对抗[某种病原体]斗争中的作用铺平道路。
请注意，原文中部分关键名称缺失，以上译文以[某种病原体]、[该球菌名称]示意需补充具体信息处。
