Presentation Information
[P04-508]Yeast GPCR sensors for microbial production
○Ririka Asama1, Takuya Tabata1, Yasuyuki Nakamura1, Tomomi Nakamura1, Akihiko Kondo1, Jun Ishii1 (1. Kobe University (Japan))
Keywords:
G-protein-coupled receptor,Yeast,biosensor,Microfluidics
[Purpose]
In synthetic biology, microbial cell factory engineering is advanced through iterative Design–Build–Test–Learn (DBTL) cycles. However, the “Test” phase remains a major bottleneck due to the lack of methods capable of rapidly evaluating large-scale strain libraries. To address this challenge, we developed a biosensor-based high-throughput screening platform. Yeast GPCR-based biosensors were engineered to selectively monitor target metabolites, allowing rapid evaluation of production performance and efficient extraction of production-relevant factors from large strain libraries.
[Method]
A yeast-based GPCR biosensor platform was constructed by integrating human GPCRs into Saccharomyces cerevisiae, enabling quantitative fluorescence-based detection of aromatic metabolites. For high-throughput evaluation, the system was integrated with a droplet-based screening platform. Yeast cells were encapsulated at the single-cell level, allowing massively parallel analysis. An autocrine sensing configuration linked metabolite secretion with GPCR-mediated signal detection. A machine learning-based workflow was used to analyze droplet images and identify high-performing variants.
[Results]
The GPCR-based biosensor enabled rapid and quantitative evaluation of intracellular metabolite production across combinatorial libraries with systematically varied expression levels of pathway genes. Screening identified optimal combinations of gene expression levels, revealing how specific genes and their expression balance contribute to enhanced melatonin production. Notably, strains with approximately two-fold higher production than the baseline were obtained, demonstrating the effectiveness of this approach for guiding rational strain design. Integration of droplet microfluidics enabled single-cell analysis at a throughput of up to 10^7 samples per minute, while machine learning-assisted image analysis allowed robust identification of high-producing variants.
[Consideration]
These results demonstrate that GPCR-based metabolite sensing enables efficient evaluation of production phenotypes and extraction of design-relevant factors from strain libraries. The platform establishes a direct link between intracellular metabolite levels and phenotypic screening, supporting knowledge-driven optimization. Integration with droplet microfluidics further provides a promising route for scaling screening throughput, particularly for single-cell analysis of large libraries.
[Conclusion]
This study establishes a biosensor-based framework for high-throughput evaluation of microbial production that addresses the bottleneck in the DBTL “Test” phase. By enabling rapid selection of high-producing strains and identification of optimal gene expression patterns, this approach advances rational microbial cell factory engineering. The developed droplet-based system provides a foundation for future large-scale screening applications, including enzyme engineering and diverse mutant libraries, enabling systematic optimization of gene combinations and expression levels.
In synthetic biology, microbial cell factory engineering is advanced through iterative Design–Build–Test–Learn (DBTL) cycles. However, the “Test” phase remains a major bottleneck due to the lack of methods capable of rapidly evaluating large-scale strain libraries. To address this challenge, we developed a biosensor-based high-throughput screening platform. Yeast GPCR-based biosensors were engineered to selectively monitor target metabolites, allowing rapid evaluation of production performance and efficient extraction of production-relevant factors from large strain libraries.
[Method]
A yeast-based GPCR biosensor platform was constructed by integrating human GPCRs into Saccharomyces cerevisiae, enabling quantitative fluorescence-based detection of aromatic metabolites. For high-throughput evaluation, the system was integrated with a droplet-based screening platform. Yeast cells were encapsulated at the single-cell level, allowing massively parallel analysis. An autocrine sensing configuration linked metabolite secretion with GPCR-mediated signal detection. A machine learning-based workflow was used to analyze droplet images and identify high-performing variants.
[Results]
The GPCR-based biosensor enabled rapid and quantitative evaluation of intracellular metabolite production across combinatorial libraries with systematically varied expression levels of pathway genes. Screening identified optimal combinations of gene expression levels, revealing how specific genes and their expression balance contribute to enhanced melatonin production. Notably, strains with approximately two-fold higher production than the baseline were obtained, demonstrating the effectiveness of this approach for guiding rational strain design. Integration of droplet microfluidics enabled single-cell analysis at a throughput of up to 10^7 samples per minute, while machine learning-assisted image analysis allowed robust identification of high-producing variants.
[Consideration]
These results demonstrate that GPCR-based metabolite sensing enables efficient evaluation of production phenotypes and extraction of design-relevant factors from strain libraries. The platform establishes a direct link between intracellular metabolite levels and phenotypic screening, supporting knowledge-driven optimization. Integration with droplet microfluidics further provides a promising route for scaling screening throughput, particularly for single-cell analysis of large libraries.
[Conclusion]
This study establishes a biosensor-based framework for high-throughput evaluation of microbial production that addresses the bottleneck in the DBTL “Test” phase. By enabling rapid selection of high-producing strains and identification of optimal gene expression patterns, this approach advances rational microbial cell factory engineering. The developed droplet-based system provides a foundation for future large-scale screening applications, including enzyme engineering and diverse mutant libraries, enabling systematic optimization of gene combinations and expression levels.
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