将基因表达数据集成到基因组规模的代谢模型中,以识别适应性进化过程中的重编程.
Shaghayegh Yazdanpanah1, Ehsan Motamedian1, Seyed Abbas Shojaosadati1
1Faculty of Chemical Engineering, Department of Biotechnology, Tarbiat Modares University, Tehran, Iran.
PloS one
|October 3, 2023
概括
一种新方法,即代谢重编程标识符 (MRI),可以识别从适应性实验室进化的基因表达数据中的代谢重编程. 它突出了关键的基因,并揭示了大肠杆菌适应新碳来源时内膜的重要性.
科学领域:
- 系统生物学 系统生物学
- 代谢工程是代谢工程.
- 基因组学就是基因组学.
背景情况:
- 在适应性实验室进化 (ALE) 期间,从基因表达数据中识别代谢重编程是具有挑战性的.
- 了解细胞适应机制需要强大的分析方法.
- 以前的方法缺乏与代谢模型进行综合分析的整合.
研究的目的:
- 开发一种新的方法,即代谢重编程标识符 (MRI),用于检测潜在的代谢重编程.
- 为了确定驱动对干扰反应的代谢适应的关键基因.
- 评估基因表达模式与最大资源利用的兼容性.
主要方法:
- 综合基因表达数据与基因组规模的代谢模型.
- 制定了一个混合整数线性编程 (MILP) 问题,以确定关键的重编程基因.
- 定义了基于基因表达资源利用的适应得分.
- 将MRI方法应用于大肠杆菌适应性进化实验.
主要成果:
- 通过最大化适应得分,确定了参与代谢重编程的关键基因.
- 选择的基因具有完整的表达用途和野生类型和进化菌株之间的显著差异.
- 发现 cyoC 和 cydB 基因在从葡萄糖转换为乳酸时对大肠杆菌重编程至关重要.
- 预测E. coli在糖醇上的演变过程中没有显著的重编程.
结论:
- 开发的MRI方法有效地识别了代谢重编程和关键基因.
- 内膜成分 (cyoC, cydB) 在大肠杆菌适应新碳来源方面发挥着至关重要的作用.
- 该方法提供了关于细胞适应策略和代谢利用模式的见解.
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