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The sequencing results of biosensor-guided genome-wide mutagenesis using a transposon

ABSTRACT: The economic viability of bio-based chemical production relies on microbial strain improvement, which optimizes titer, yield, and productivity of the process. This improvement necessitates redirecting cellular metabolism towards target molecule synthesis, requiring not only heterologous gene introduction but also endogenous genomic modifications to reconfigure metabolic networks originally optimized for cellular growth. However, identifying engineering targets is complex due to the intricate nature of metabolic networks and the cumulative, interactive effects of multiple genes on compound production. Consequently, there is a critical need for technologies capable of elucidating unpredictable engineering targets and synergistic gene combinations that enhance product formation in microbial strains. This study presents an innovative, integrated methodology named Tn-MAGE for rapidly identifying unpredictable engineering targets and synergistic gene combinations to enhance microbial strain performance in compound production. Our approach synergistically combines two key strategies: (1) integration of Tn-seq with biosensor-assisted ALE in a single batch culture, and (2) utilization of MAGE for combinatorial knockout library creation coupled with biosensor-assisted high-throughput screening. This integrated Tn-MAGE methodology offers a powerful tool for discovering unpredictable targets across the genome and their combinations, facilitating the development of high-performing strains for bio-based production of valuable compounds. The versatility of this approach suggests its potential applicability in various metabolic engineering contexts, promising significant advancements in strain improvement strategies.

ORGANISM(S): Escherichia coli

PROVIDER: GSE279638 | GEO | 2025/11/22

REPOSITORIES: GEO

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Project description:Our group has evaluated the productivity of the alcohol isoprenol (3-methyl-3-buten-1-ol), platform commodity chemical with attractive properties specifically as a diesel fuel blendstock and precursor for the bio-jetfuel DMCO, across a number of microbial hosts. While there are strengths and drawbacks to producing isoprenol in any one microbe, Pseudomonas putida KT2440 has a naturally versatile catabolic profile competent of consuming isoprenol. The pathway for this catabolism was revealed using functional genomics data (RB-TnSeq - Thompson et al) and we speculated that this data could also provide a path to developing an isoprenol biosensor. Refactoring knowledge of the isoprenol catabolism signaling system could provide a readout for intracellular isoprenol, in turn enabling an alternative approach to strain engineering that did not rely on a mechanistic understanding of how a heterologous pathway modulates native metabolism. We define a biosensor as the system where a ligand is recognized by a transcriptional activator, which in turn binds to a cognate DNA sequence driving transcription of a downstream reporter gene. The resulting gene expression linearly increases in response to the initial ligand concentration. There are undoubtedly other biosensor modalities (such as directly converting the ligand into a colored molecule) but the advantage of a biosensor that activates a genetic circuit is that the response can also be integrated back into cellular physiology for high throughput selection with growth based assay. For example, strains can be devised where cell growth is concomitant with the increased production of the ligand. Prokaryotic systems are also advantaged over eukaryotes for biosensor development as the determinants of ribosome translation efficiency are dictated by simple RBS sequences adjacent to the start codon as opposed to complex transcriptional regulators, enabling fine protein level expression tuning while maintaining inducibility.

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The sequencing results of biosensor-guided genome-wide mutagenesis using a transposon

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