Abstract
BackgroundThe optimisation and scale-up of process conditions leading to high yields of recombinant proteins is an enduring bottleneck in the post-genomic sciences. Typical experiments rely on varying selected parameters through repeated rounds of trial-and-error optimisation. To rationalise this, several groups have recently adopted the 'design of experiments' (DoE) approach frequently used in industry. Studies have focused on parameters such as medium composition, nutrient feed rates and induction of expression in shake flasks or bioreactors, as well as oxygen transfer rates in micro-well plates. In this study we wanted to generate a predictive model that described small-scale screens and to test its scalability to bioreactors.ResultsHere we demonstrate how the use of a DoE approach in a multi-well mini-bioreactor permitted the rapid establishment of high yielding production phase conditions that could be transferred to a 7 L bioreactor. Using green fluorescent protein secreted from Pichia pastoris, we derived a predictive model of protein yield as a function of the three most commonly-varied process parameters: temperature, pH and the percentage of dissolved oxygen in the culture medium. Importantly, when yield was normalised to culture volume and density, the model was scalable from mL to L working volumes. By increasing pre-induction biomass accumulation, model-predicted yields were further improved. Yield improvement was most significant, however, on varying the fed-batch induction regime to minimise methanol accumulation so that the productivity of the culture increased throughout the whole induction period. These findings suggest the importance of matching the rate of protein production with the host metabolism.ConclusionWe demonstrate how a rational, stepwise approach to recombinant protein production screens can reduce process development time.
Highlights
The optimisation and scale-up of process conditions leading to high yields of recombinant proteins is an enduring bottleneck in the post-genomic sciences
We describe how a DoE-derived model generated using a parallel mini-bioreactor (that can directly control pH, temperature (T) and dissolved oxygen (DO) [13,14]) is predictive of protein yield normalised for both culture volume and density in the same system, but is scalable to P. pastoris cultures grown in a 7 L bioreactor
A predictive model of the induction phase can be generated to describe secreted recombinant GFP yield as a function of pH, T and DO in microwell format We used a classic quadratic Box-Behnken design [16], which allows a reduced number of treatment combinations to be used in building the model and gives no bias towards any potential optimum region of the process
Summary
The optimisation and scale-up of process conditions leading to high yields of recombinant proteins is an enduring bottleneck in the post-genomic sciences. Typical experiments rely on varying selected parameters through repeated rounds of trial-and-error optimisation. Each system has benefits and drawbacks in its use, but yeast with its well-established genetic and molecular biological resources combines the ease and speed-of-use of bacterial systems with its ability as a eukaryote to secrete post-translationally-modified proteins. As a consequence it is an increasingly popular choice in both academic and commercial laboratories [2,3]. The routine achievement of high production yields in yeast or any of the alternative systems mentioned above continues to be a substantial bottleneck to further progress
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