Peroxisome engineering in yeast has emerged as a promising strategy for biomanufacturing, as it enables the compartmentalization of biosynthetic pathways and thus alleviates key bottlenecks in natural product biosynthesis. By sequestering specific metabolic pathways within peroxisomes, this strategy effectively reduces product cytotoxicity, enhances intracellular product storage, and allows precise redirection of metabolic fluxes. Nevertheless, its broader application remains limited by several unresolved challenges, including the insufficient understanding of peroxisomal membrane permeability, inadequate cofactor supply, and glucose-mediated repression of peroxisomal capacity. To overcome these obstacles, a range of conventional and emerging approaches-such as engineering peroxisomal targeting signal type 1 (PTS1), regulation of peroxisome proliferation, development of orthogonal artificial peroxisomal protein transport systems, and applying machine learning to predict gene overexpression for optimizing peroxisomal functional capacity-have expanded the toolkit for peroxisome engineering in yeast. This review summarizes recent advances in peroxisomal surface display engineering, peroxisomal matrix engineering, and multi-organelle spatial combination coordination, highlighting the importance of peroxisome engineering in optimizing yeast-based cell factories for natural product biosynthesis. Moreover, it critically evaluates current limitations, along with a comprehensive discussion of both conventional and emerging approaches aimed at further optimizing peroxisome engineering. In the future, integrating peroxisome engineering with advanced machine learning will be crucial for addressing remaining challenges and fully realizing the potential of sustainable and scalable yeast-based biomanufacturing.

• PMID: 41183603
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Journal Article | Review

Authors

Ye C, Li X, Liu T, Li S, Zhang M, Zhao Y, Cheng J, Yang G, Li P

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