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김동혁

Kim, Donghyuk
Systems Biology and Machine Learning Lab.
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Integrated Tn-seq and MAGE-assisted rapid genome engineering targeting in Escherichia coli

Author(s)
Hwang, JaeseongHan, YongheeBang, InaPark, Joon-youngKim, DonghyukSung, JunyeongSeo, Sang WooJang, SunghoJung, Gyoo Yeol
Issued Date
2025-11
DOI
10.1016/j.tibtech.2025.10.009
URI
https://scholarworks.unist.ac.kr/handle/201301/90490
Citation
Trends in Biotechnology
Abstract
Improving microbial strains is essential for the economic feasibility of bio-based chemical production; however, the intricate nature of metabolic networks and gene interactions makes identifying effective genetic engineering targets challenging. We developed iTARGET, an integrated approach combining in situ transposon mutagenesis, biosensor-guided selection, and multiplex automated genome engineering (MAGE) to identify novel and synergistic genetic targets that are challenging to predict through rational design. In the first phase, in situ transposon mutagenesis generated genetic diversity within a single batch culture, allowing biosensor-driven enrichment of high-producing mutants. Transposon sequencing (Tn-seq) was then performed to identify critical genomic targets. In the second phase, MAGE enabled the creation of combinatorial knockout (KO) libraries, and high-throughput screening revealed synergistic gene interactions. Applying iTARGET to naringenin (NRN) production enriched high-producing mutants, achieving a population-level titer 1.7-fold higher than that in the control. Next-generation sequencing identified nine unpredictable genetic targets, achieving a 2.3-fold titer increase with single KOs. Further combinatorial KOs revealed synergistic effects, with a double-KO mutant producing a 2.8-fold improvement. By integrating mutagenesis and selection into a single batch, iTARGET accelerates the discovery of challenging genetic targets and enables the exploration of synergistic gene interactions through high-throughput identification of combinatorial KOs, enhancing bio-based chemical production. © 2025 Elsevier Ltd
Publisher
Elsevier Ltd
ISSN
0167-7799
Keyword (Author)
genome engineeringin vivo mutagenesisiTARGETmultiplex automated genome engineering (MAGE)biosensorbiosensor-assisted enrichmentTn-seqtransposon

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