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酵母9:由社区策划的S. cerevisiae的共识基因组规模代谢模型

Chengyu Zhang^1,2, Benjamín J Sánchez^3,4, Feiran Li⁵

¹State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 200240, Shanghai, China.

Molecular systems biology

|August 12, 2024

概括

新的酵母9模型改善了在各种条件下对酵母代谢的预测. 来自菌株特定模型的流体学准确预测基因功能,推进系统生物学研究.

科学领域:

系统生物学系统生物学
代谢工程是代谢工程.
计算生物学计算生物学

背景情况:

基因组规模的代谢模型 (GEMs) 对于酵母中的多组组合至关重要.
细菌菌模型Saccharomyces cerevisiae,酵母8,已经经历了持续的社区更新.
最新的代,酵母9,为代谢研究提供了更好的质量和范围.

研究的目的:

为了评估更新的酵母GEM的预测性能,Yeast9.
分析在透应激下酵母中的代谢适应.
调查与源偏好相关的代谢重新连接.
评估菌株特异性GEMs (ssGEMs) 对预测突变表型的有用性.

主要方法:

使用单细胞转录组学生成163种特定条件的GEM.
在奥斯莫斯压力和参考条件下进行比较流量分析.
酵母9与蛋白质组学数据的整合.
为突变菌株构建1229个转录组学受约束的ssGEM.
机器学习模型的应用来比较流量学和转录学用于基因功能预测.

主要成果:

酵母适应高透压升调中央碳代谢中的流量.
蛋白质组学数据与酵母9相结合,揭示了源利用中的代谢重新连接.
ssGEMs准确地预测了1229个突变菌株的生长率.
来自ssGEMs的流动组学数据在通过机器学习预测基因功能类别方面超过了转录组学.

结论:

酵母9显示了酵母新陈代谢的预测能力的提高.
从大规模的ssGEM中基于流体学的预测提供了与单独的转录学相比更好的功能洞察力.
酵母9准备在酵母代谢中显著推进系统生物学研究.

关键词:

这种植物是Saccharomyces cerevisiae.基因组规模的代谢模型机器学习机器学习多主题整合多主题整合

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