Leveraging yeast as a platform for enhanced protein production
Project duration: November 2022 – October 2025
Dr Juan Martinez
Dr Carol Hartley, Dr Colin Scott, Dr Robert Speight, Dr Thomas Vanhercke
With an expected two billion extra people on the planet to feed by 2050, coupled with changing tastes and dietary preferences, there is a growing demand around the world for more protein, produced more sustainably and from a wider variety of sources.
Precision fermentation offers alternatives to complement traditional agricultural production through new products produced with fewer resources.
Pichia pastoris is a type of yeast that is particularly well-suited as a platform for enhanced protein production during precision fermentation. This organism can produce large quantities of protein and can be easily engineered to produce specific proteins of interest. Pichia pastoris has a high tolerance for stress and can grow in various conditions, making it ideal for large-scale protein production.
However, several bottlenecks can limit its productivity. One major bottleneck is the “jackpot clone” factor, which refers to the fact that only a small proportion of the transformed cells will express the desired protein at high levels. Additionally, the endoplasmic reticulum (ER) stress response can occur when the yeast cells produce large amounts of protein, accumulating unfolded or misfolded proteins, which can inhibit protein production and reduce cell growth. Other bottlenecks include low secretion efficiency and recombinant protein yield. These bottlenecks can limit the ability of Pichia pastoris to produce large quantities of high-quality protein, making it essential to find ways to overcome these limitations to improve productivity.
To address the challenge of the “jackpot clone” factor, our team is developing and implementing advanced fluorescent-based biosensor technologies and high-throughput experimental techniques to conduct rapid screening. This approach enables us to gather crucial insights into the factors responsible for high protein production and will allow the identification of optimal clones for large-scale protein production.
The ER is an organelle in eukaryotic cells responsible for folding, modifying, and transporting proteins. When the ER is overwhelmed by the accumulation of unfolded or misfolded proteins, it initiates a cellular response known as ER stress, which can negatively affect protein production. This project will implement biosensors as high-throughput tools to detect and quantify the level of ER stress in yeast strains, allowing us to predict the protein production capacity of the strains, identify the specific protein properties that may be hampering secretion, and guide rational engineering strategies to optimise heterologous protein production.
High protein production in Pichia pastoris depends on multiple factors, including the complexity of specific proteins, their folding and secretion, and the associated stress in the molecular machinery. However, there is still a gap in our knowledge about how these factors interact to impact protein production. Biosensors can help to close this gap by providing real-time data on the growth, health, and productivity of Pichia pastoris cultures. This technology can improve the efficiency of protein production and potentially enable the conversion of waste streams into valuable protein products at a larger scale.
Using design-build-test-learn cycles coupled with biosensor technologies can significantly improve the capacity of Pichia pastoris strains to produce proteins of interest. This information can then be used to improve the efficiency of protein production by guiding rational engineering strategies that target specific genes or proteins. The increased protein titre can potentially be translated to other proteins of interest in other business areas of CSIRO.
This includes research underway to target proteins in meat and dairy products to help meet the growing demand for sustainable protein sources. With the integration of machine learning and artificial intelligence techniques, the capacity of Pichia pastoris to produce more proteins of interest can be further enhanced. Using data generated from the biosensors, machine learning algorithms can be trained to predict the protein production capacity of strains, identify the specific protein properties that may be hampering secretion, and guide rational engineering strategies to optimise heterologous protein production.