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代谢重排使微生物生长速率能够适应温度变化。

Metabolic rearrangement enables adaptation of microbial growth rate to temperature shifts.

作者信息

Knapp Benjamin D, Willis Lisa, Gonzalez Carlos, Vashistha Harsh, Jammal-Touma Joanna, Tikhonov Mikhail, Ram Jeffrey, Salman Hanna, Elias Josh E, Huang Kerwyn Casey

机构信息

Biophysics Program, Stanford University, Stanford, CA, USA.

Department of Bioengineering, Stanford University, Stanford, CA, USA.

出版信息

Nat Microbiol. 2025 Jan;10(1):185-201. doi: 10.1038/s41564-024-01841-4. Epub 2024 Dec 13.

Abstract

Temperature is a key determinant of microbial behaviour and survival in the environment and within hosts. At intermediate temperatures, growth rate varies according to the Arrhenius law of thermodynamics, which describes the effect of temperature on the rate of a chemical reaction. However, the mechanistic basis for this behaviour remains unclear. Here we use single-cell microscopy to show that Escherichia coli exhibits a gradual response to temperature upshifts with a timescale of ~1.5 doublings at the higher temperature. The response was largely independent of initial or final temperature and nutrient source. Proteomic and genomic approaches demonstrated that adaptation to temperature is independent of transcriptional, translational or membrane fluidity changes. Instead, an autocatalytic enzyme network model incorporating temperature-sensitive Michaelis-Menten kinetics recapitulates all temperature-shift dynamics through metabolome rearrangement, resulting in a transient temperature memory. The model successfully predicts alterations in the temperature response across nutrient conditions, diverse E. coli strains from hosts with different body temperatures, soil-dwelling Bacillus subtilis and fission yeast. In sum, our model provides a mechanistic framework for Arrhenius-dependent growth.

摘要

温度是环境中以及宿主体内微生物行为和生存的关键决定因素。在中等温度下,生长速率根据热力学的阿伦尼乌斯定律而变化,该定律描述了温度对化学反应速率的影响。然而,这种行为的机制基础仍不清楚。在这里,我们使用单细胞显微镜来表明,大肠杆菌对温度升高表现出逐渐的响应,在较高温度下的时间尺度约为1.5代。这种响应在很大程度上与初始或最终温度以及营养源无关。蛋白质组学和基因组学方法表明,对温度的适应与转录、翻译或膜流动性变化无关。相反,一个包含温度敏感米氏动力学的自催化酶网络模型通过代谢组重排概括了所有温度变化动力学,从而产生了短暂的温度记忆。该模型成功预测了不同营养条件下温度响应的变化、来自不同体温宿主的多种大肠杆菌菌株、土壤芽孢杆菌和裂殖酵母的温度响应变化。总之,我们的模型为依赖阿伦尼乌斯的生长提供了一个机制框架。

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