Nitrogen supplementation ameliorates product quality and quantity during high cell density bioreactor studies of Pichia pastoris: A case study with proteolysis prone streptokinase

Development and Optimization of a Novel Soft Sensor Modeling Method for Fermentation Process of Pichia pastoris

This paper introduces a novel soft sensor modeling method based on BDA-IPSO-LSSVM designed to address the issue of model failure caused by varying fermentation data distributions resulting from different operating conditions during the fermentation of different batches of Pichia pastoris. First, the problem of significant differences in data distribution among different batches of the fermentation process is addressed by adopting the balanced distribution adaptation (BDA) method from transfer learning. This method reduces the data distribution differences among batches of the fermentation process, while the fuzzy set concept is employed to improve the BDA method by transforming the classification problem into a regression prediction problem for the fermentation process. Second, the soft sensor model for the fermentation process is developed using the least squares support vector machine (LSSVM). The model parameters are optimized by an improved particle swarm optimization (IPSO) algorithm based on individual differences. Finally, the data obtained from the Pichia pastoris fermentation experiment are used for simulation, and the developed soft sensor model is applied to predict the cell concentration and product concentration during the fermentation process of Pichia pastoris. Simulation results demonstrate that the IPSO algorithm has good convergence performance and optimization performance compared with other algorithms. The improved BDA algorithm can make the soft sensor model adapt to different operating conditions, and the proposed soft sensor method outperforms existing methods, exhibiting higher prediction accuracy and the ability to accurately predict the fermentation process of Pichia pastoris under different operating conditions.

Evaluation of Methylotrophic Yeast Ogataea thermomethanolica TBRC 656 as a Heterologous Host for Production of an Animal Vaccine Candidate

Multiple yeast strains have been developed into versatile heterologous protein expression platforms. Earlier works showed that Ogataea thermomethanolica TBRC 656 (OT), a thermotolerant methylotrophic yeast, can efficiently produce several industrial enzymes. In this work, we demonstrated the potential of this platform for biopharmaceutical manufacturing. Using a swine vaccine candidate as a model, we showed that OT can be optimized to express and secrete the antigen based on porcine circovirus type 2d capsid protein at a respectable yield. Crucial steps for yield improvement include codon optimization and reduction of OT protease activities. The antigen produced in this system could be purified efficiently and induce robust antibody response in test animals. Improvements in this platform, especially more efficient secretion and reduced extracellular proteases, would extend its potential as a competitive platform for biopharmaceutical industries.

What makes Komagataella phaffii non-conventional?

The important industrial protein production host Komagataella phaffii (syn Pichia pastoris) is classified as a non-conventional yeast. But what exactly makes K. phaffii non-conventional? In this review, we set out to address the main differences to the 'conventional' yeast Saccharomyces cerevisiae, but also pinpoint differences to other non-conventional yeasts used in biotechnology. Apart from its methylotrophic lifestyle, K. phaffii is a Crabtree-negative yeast species. But even within the methylotrophs, K. phaffii possesses distinct regulatory features such as glycerol-repression of the methanol-utilization pathway or the lack of nitrate assimilation. Rewiring of the transcriptional networks regulating carbon (and nitrogen) source utilization clearly contributes to our understanding of genetic events occurring during evolution of yeast species. The mechanisms of mating-type switching and the triggers of morphogenic phenotypes represent further examples for how K. phaffii is distinguished from the model yeast S. cerevisiae. With respect to heterologous protein production, K. phaffii features high secretory capacity but secretes only low amounts of endogenous proteins. Different to S. cerevisiae, the Golgi apparatus of K. phaffii is stacked like in mammals. While it is tempting to speculate that Golgi architecture is correlated to the high secretion levels or the different N-glycan structures observed in K. phaffii, there is recent evidence against this. We conclude that K. phaffii is a yeast with unique features that has a lot of potential to explore both fundamental research questions and industrial applications.

Study on Multi-Model Soft Sensor Modeling Method and Its Model Optimization for the Fermentation Process of Pichia pastoris

The problems that the key biomass variables in Pichia pastoris fermentation process are difficult measure in real time; this paper mainly proposes a multi-model soft sensor modeling method based on the piecewise affine (PWA) modeling method, which is optimized by particle swarm optimization (PSO) with an improved compression factor (ICF). Firstly, the false nearest neighbor method was used to determine the order of the PWA model. Secondly, the ICF-PSO algorithm was proposed to cooperatively optimize the number of PWA models and the parameters of each local model. Finally, a least squares support vector machine was adopted to determine the scope of action of each local model. Simulation results show that the proposed ICF-PSO-PWA multi-model soft sensor modeling method accurately approximated the nonlinear features of Pichia pastoris fermentation, and the model prediction accuracy is improved by 4.4884% compared with the weighted least squares vector regression model optimized by PSO.