生物生产
大肠杆菌
碳源
丙酮
甘油
碳纤维
化学
拉伤
生物
计算生物学
生物化学
计算机科学
基因
发酵
算法
复合数
解剖
作者
Kangsan Kim,Donghui Choe,Minjeong Kang,Sang-Hyeok Cho,Suhyung Cho,Ki Jun Jeong,Bernhard Ø. Palsson,Byung‐Kwan Cho
标识
DOI:10.1016/j.ymben.2024.04.004
摘要
Microbes have inherent capacities for utilizing various carbon sources, however they often exhibit sub-par fitness due to low metabolic efficiency. To test whether a bacterial strain can optimally utilize multiple carbon sources, Escherichia coli was serially evolved in L-lactate and glycerol. This yielded two end-point strains that evolved first in L-lactate then in glycerol, and vice versa. The end-point strains displayed a universal growth advantage on single and a mixture of adaptive carbon sources, enabled by a concerted action of carbon source-specialists and generalist mutants. The combination of just four variants of glpK, ppsA, ydcI, and rph-pyrE, accounted for more than 80% of end-point strain fitness. In addition, machine learning analysis revealed a coordinated activity of transcriptional regulators imparting condition-specific regulation of gene expression. The effectiveness of the serial adaptive laboratory evolution (ALE) scheme in bioproduction applications was assessed under single and mixed-carbon culture conditions, in which serial ALE strain exhibited superior productivity of acetoin compared to ancestral strains. Together, systems-level analysis elucidated the molecular basis of serial evolution, which hold potential utility in bioproduction applications.
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