Biomolecular Condensate-Based Artificial Organelle for Driving Compartmentalized Flux Control

Microbial cell factories have emerged as versatile bioreactors capable of orchestrating complex metabolic networks to convert renewable feedstocks into high-value biochemicals. Nevertheless, the diffusion-mediated dispersion of metabolic intermediates often compromises biosynthesis efficiency, primarily attributable to the absence of artificial subcellular compartments for spatiotemporal organization of catalytic enzymes. Herein, we established a synthetic biology platform leveraging engineered biomolecular condensates to achieve precise flux control via a modular pathway compartmentalization. First, the fused sarcoma low complexity domain (FUSLCD) was designed to combine the GCN4 to rationally integrate with GCN4 scaffold proteins to create programmable artificial organelles. Second, the protein recruitment and assembly functions of artificial organelles were identified by a short peptide pair or directly fusing with the FUSLCD protein in a spatial organization way. Third, using the 2'-fucosyllactose (2'-FL) de novo biosynthesis pathway as a model system, we demonstrated enhanced pathway efficiency by colocalizing critical enzymes within artificial organelles in engineered <i>E. coli</i>, yielding a significant improvement in 2'-FL titer through flux compartmentalization. This study not only overcome diffusion-limited reactions via engineered spatial organization but also offer a versatile toolkit for optimizing compartmentalized biosynthesis pathways.

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Publication Details

Journal
ACS Synthetic Biology
Published
2025-05-08
DOI
https://doi.org/10.1021/acssynbio.4c00871
Primary Topic
RNA Research and Splicing
Type
article
Field-Weighted Citation Impact
0.00

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article

Biomolecular Condensate-Based Artificial Organelle for Driving Compartmentalized Flux Control

Yaning Zhao, Yongzhong Wang, Jingyi Wu, Xinyue Su et al.
ACS Synthetic Biology
RNA Research and Splicing
article

Biomolecular Condensate-Based Artificial Organelle for Driving Compartmentalized Flux Control

Yaning Zhao, Yongzhong Wang, Jingyi Wu, Xinyue Su, Mengqi Ji, Buhan Yao, Jingyu Zhou, Qiang Ding
article en

Abstract

Microbial cell factories have emerged as versatile bioreactors capable of orchestrating complex metabolic networks to convert renewable feedstocks into high-value biochemicals. Nevertheless, the diffusion-mediated dispersion of metabolic intermediates often compromises biosynthesis efficiency, primarily attributable to the absence of artificial subcellular compartments for spatiotemporal organization of catalytic enzymes. Herein, we established a synthetic biology platform leveraging engineered biomolecular condensates to achieve precise flux control via a modular pathway compartmentalization. First, the fused sarcoma low complexity domain (FUSLCD) was designed to combine the GCN4 to rationally integrate with GCN4 scaffold proteins to create programmable artificial organelles. Second, the protein recruitment and assembly functions of artificial organelles were identified by a short peptide pair or directly fusing with the FUSLCD protein in a spatial organization way. Third, using the 2'-fucosyllactose (2'-FL) de novo biosynthesis pathway as a model system, we demonstrated enhanced pathway efficiency by colocalizing critical enzymes within artificial organelles in engineered <i>E. coli</i>, yielding a significant improvement in 2'-FL titer through flux compartmentalization. This study not only overcome diffusion-limited reactions via engineered spatial organization but also offer a versatile toolkit for optimizing compartmentalized biosynthesis pathways.

ACS Synthetic BiologyVol. 14(6)
Anhui University (CN)
National Natural Science Foundation of China, Anhui University, Natural Science Foundation of Anhui Province
Affordable and clean energy
Openalex Percentile: Top 91%
RNA Research and Splicing
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Biomolecular Condensate-Based Artificial Organelle for Driving Compartmentalized Flux Control — Yaning Zhao, Yongzhong Wang, et al. · ACS Synthetic Biology (2025) | TGRS Research Map | TGRS