Development of highly characterized genetic bioparts for efficient gene expression in CO2-fixing Eubacterium limosum

生物 真细菌 基因 计算生物学 代谢工程 遗传学 细菌
作者
Yoseb Song,Jiyun Bae,Sangrak Jin,Hyeonsik Lee,Seulgi Kang,Jin Soo Lee,Jongoh Shin,Suhyung Cho,Byung‐Kwan Cho
出处
期刊:Metabolic Engineering [Elsevier BV]
卷期号:72: 215-226 被引量:15
标识
DOI:10.1016/j.ymben.2022.03.016
摘要

Acetogenic bacteria demonstrate industrial potential for utilizing carbon dioxide (CO 2 ) for biochemical production using the Wood-Ljungdahl pathway. However, the metabolic engineering of acetogenic bacteria has been hampered by the limited number of available genetic bioparts for gene expression. Here, we integrated RNA sequencing, ribosome profiling, differential RNA sequencing, and RNA 3′-end sequencing results of Eubacterium limosum to establish genetic bioparts, such as promoters, 5′ untranslated regions, and transcript terminators, to regulate transcriptional and translational expression of genes composing of biosynthetic pathways. In addition, a transformation method for the strain was developed to efficiently deliver the obtained genetic bioparts into cells, resulting in a transformation efficiency of 2.5 × 10 5 CFU/μg DNA. Using this method, the genetic bioparts were efficiently introduced, and their strengths were measured, which were then applied to optimize the heterologous expression of acetolactate synthase and acetolactate decarboxylase for non-native biochemical acetoin production. The strategy developed in this study is the first report on integrating multi-omics data for biopart development of CO 2 or syngas utilizing acetogenic bacteria, which lays a foundation for the efficient production of biochemicals from CO 2 or syngas as a carbon feedstock under autotrophic growth conditions. • Development of genetic tools for CO2-fixing Eubacterium limosum . • Unraveling transcript architecture using transcript start sites and 3՛ end sites . • Utilizing transcript 3՛ end position as metabolic valves to regulate gene expression. • Autotrophic acetoin production using bioparts designed by the multi-omics dataset.
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