Aug 2026· International Journal of Biological Macromolecules· pp.
153992
· 0 citations· 109 references
Medicine
TL;DR
This review provides the current state of PHAs production from wild yeast strains and the various approaches that have been used to improve yield, and discusses the performance, challenges, and limitations of various synthetic biology and metabolic engineering strategies in yeast strains for PHAs production.
Abstract
Growing environmental concerns about conventional plastics have created a demand for alternative resources that are both truly biodegradable and cost-effective. Microbial Polyhydroxyalkanoates (PHAs) are drawing great attention due to their degradability and lower environmental impact on ecosystems. However, higher production cost of bacteria-derived PHAs has become a bottleneck for commercial applications, prompting the search for alternative microbial hosts. Among various microbes, yeasts have become an alternative host for PHAs production due to their stress tolerance, generally regarded as safe (GRAS) status, versatility in substrate utilization, resistance to phage infection, lack of effective biopolymer depolymerizing enzymes and the unique physicochemical environment offered by subcellular compartments for PHAs production. In this perspective, this review provides the current state of PHAs production from wild yeast strains and the various approaches that have been used to improve yield. Furthermore, we discuss the performance, challenges, and limitations of various synthetic biology and metabolic engineering strategies in yeast strains for PHAs production, including overexpressing PHAs biosynthetic genes, knocking out competing pathways, metabolic engineering for precursor supply, and improving renewable feedstock utilization. This review highlights the knowledge gaps in yeast based PHAs production using native and engineered strains, and explains its limitations compared to other microbial sources.
The response surface methodology functions as a robust approach to investigate and explore the independent and interactive effects of variables on PHA production, and is a highly effective and dependable statistical tool for evaluating the factors influencing PHA production.
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