机器学习识别细菌在不同碳源上生长的关键代谢反应。

IF 8.5 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Molecular Systems Biology Pub Date : 2024-03-01 Epub Date: 2024-01-30 DOI:10.1038/s44320-024-00017-w
Hyunjae Woo, Youngshin Kim, Dohyeon Kim, Sung Ho Yoon
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引用次数: 0

摘要

依赖碳源控制细菌生长是细菌生理和生存的基础。然而,由于细胞网络的复杂性,精确定位对细胞生长重要的代谢步骤具有挑战性。在这里,我们构建了弹性网模型和多层感知模型,它们整合了全基因组基因缺失数据和模拟通量分布,用于识别对生长在 30 种不同碳源上的大肠杆菌有利或有害的代谢反应。这两个模型不仅能识别出促进生长的基本反应,还能识别出促进生长的非基本反应,其表现优于传统的硅学方法。它们成功地预测了有利于细胞生长的代谢反应,而且模型之间的趋同性很高。模型显示,生物合成途径通常能促进各种碳源的生长,而能量生成途径的影响则因碳源而异。对实验训练数据以外的发现以及各种碳源对乙醛酸分流、丙酮酸脱氢酶反应和冗余嘌呤生物合成反应的影响进行了实验验证,得出了耐人寻味的预测结果。这些都凸显了模型在理解和工程微生物代谢方面的实际意义和预测能力。
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Machine learning identifies key metabolic reactions in bacterial growth on different carbon sources.

Carbon source-dependent control of bacterial growth is fundamental to bacterial physiology and survival. However, pinpointing the metabolic steps important for cell growth is challenging due to the complexity of cellular networks. Here, the elastic net model and multilayer perception model that integrated genome-wide gene-deletion data and simulated flux distributions were constructed to identify metabolic reactions beneficial or detrimental to Escherichia coli grown on 30 different carbon sources. Both models outperformed traditional in silico methods by identifying not just essential reactions but also nonessential ones that promote growth. They successfully predicted metabolic reactions beneficial to cell growth, with high convergence between the models. The models revealed that biosynthetic pathways generally promote growth across various carbon sources, whereas the impact of energy-generating pathways varies with the carbon source. Intriguing predictions were experimentally validated for findings beyond experimental training data and the impact of various carbon sources on the glyoxylate shunt, pyruvate dehydrogenase reaction, and redundant purine biosynthesis reactions. These highlight the practical significance and predictive power of the models for understanding and engineering microbial metabolism.

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来源期刊
Molecular Systems Biology
Molecular Systems Biology 生物-生化与分子生物学
CiteScore
18.50
自引率
1.00%
发文量
62
审稿时长
6-12 weeks
期刊介绍: Systems biology is a field that aims to understand complex biological systems by studying their components and how they interact. It is an integrative discipline that seeks to explain the properties and behavior of these systems. Molecular Systems Biology is a scholarly journal that publishes top-notch research in the areas of systems biology, synthetic biology, and systems medicine. It is an open access journal, meaning that its content is freely available to readers, and it is peer-reviewed to ensure the quality of the published work.
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