Application of medium optimization tools for improving recombinant human interferon gamma production from Kluyveromyces lactis

A comprehensive review on the application of neural network model in microbial fermentation

The development of high-performance strains and the continuous breakthrough of strain screening technology also pose challenges to downstream fermentation optimization and scale-up. Therefore, neural network models are utilized to optimize the fermentation process to meet the goals of boosting yield or lowering cost, with the use of artificial intelligence technology in conjunction with the peculiarities of the fermentation process. High-performance strains' yield rise and fermentation process amplification will be sped up with the aid of neural network models. This paper offers a helpful review for anyone interested in state-of-the-art microbial fermentation processes, as it thoroughly reviews the application of neural network models in predicting fermentation yield, optimizing the fermentation process, and monitoring the fermentation process.

Modeling and optimization of culture media for recombinant Helicobacter pylori vaccine antigen HpaA

Introduction

H. pylori (Helicobacter pylori) infection represents a significant global health concern, exacerbated by the emergence of drug-resistant strains resulting from conventional antibiotic treatments. Consequently, the development of vaccines with both preventive and therapeutic properties has become crucial in addressing H. pylori infections. The H. pylori adhesin protein HpaA has demonstrated strong immunogenicity across various adjuvants and dosage forms, positioning it as a key candidate antigen for recombinant subunit vaccines against H. pylori. Optimizing fermentation culture conditions is an effective strategy to enhance product yield and lower production costs. However, to date, there has been no systematic investigation into methods for improving the fermentation yield of HpaA. Enhancing the fermentation medium to increase HpaA yield holds significant potential for application and economic benefits in the prevention and detection of H. pylori infection.

Methods

To achieve a stable and high-yielding H. pylori vaccine antigen HpaA, this study constructed recombinant Escherichia coli expressing HpaA. The impact of fermentation medium components on the rHpaA yield was assessed using a one-factor-at-a-time approach alongside Plackett-Burman factorial experiments. Optimal conditions were effectively identified through response surface methodology (RSM) and artificial neural network (ANN) statistical computational models. The antigenicity and immunogenicity of the purified rHpaA were validated through immunization of mice, followed by Western Blot analysis and serum IgG ELISA quantification.

Results

Glucose, yeast extract, yeast peptone, NH4Cl and CaCl2 all contributed to the production of rHpaA, with glucose, yeast extract, and NH4Cl demonstrating particularly significant effects. The artificial neural network linked genetic algorithm (ANN-GA) model exhibited superior predictive accuracy, achieving a rHpaA yield of 0.61 g/L, which represents a 93.2% increase compared to the initial medium. Animal immunization experiments confirmed that rHpaA possesses good antigenicity and immunogenicity.

Discussion

This study pioneers the statistical optimization of culture media to enhance rHpaA production, thereby supporting its large-scale application in H. pylori vaccines. Additionally, it highlights the advantages of the ANN-GA approach in bioprocess optimization.

Enhanced Biomass Production of Recombinant Pfu DNA Polymerase Producer Escherichia coli BL21(DE3) by Optimization of Induction Variables Using Response Surface Methodology

Pfu DNA polymerase is one of the most preferred molecular enzymes that is isolated from the hyperthermophilic Pyrococcus furiosus and used for high-throughput DNA synthesis by the polymerase chain reaction. Therefore, an efficient Pfu DNA polymerase production method is necessary for molecular techniques. In the present study, Pfu DNA polymerase was expressed in recombinant Escherichia coli BL21(DE3) and significant parameters for the biomass production were optimized using the central composite design which is the most popular method of response surface methodology. Induction conditions including cell density prior induction (OD_600nm), post-induction temperature, IPTG concentration, and post-induction time and their interactions on biomass production were investigated. The maximum biomass production (14.1 g/L) in shake flasks was achieved using the following predicted optimal conditions: OD_600nm before induction of 0.4 and the induction at 32 °C for 7.7 h, with 0.6 mM IPTG. Optimized culture conditions were implemented to scale up experiments. 22% and 70% increase in biomass production was achieved in 3 L and 10 L bioreactors, respectively as compared to initial biomass production observed in unoptimized conditions. Similary, a 30% increase of Pfu DNA polymerase production was obtained after the optimization. The polymerase activity of the purifed Pfu DNA polymerase was assessed by PCR amplification and determined as 2.9 U/μl by comparison with commercial Pfu DNA polymerase. The findings of this study indicated that the proposed fermentation conditions will contribute to further scale‑up studies to enhance the biomass for the production of other recombinant proteins.

A review of algorithmic approaches for cell culture media optimization

Cell culture media composition and culture conditions play a crucial role in product yield, quality and cost of production. Culture media optimization is the technique of improving media composition and culture conditions to achieve desired product outcomes. To achieve this, there have been many algorithmic methods proposed and used for culture media optimization in the literature. To help readers evaluate and decide on a method that best suits their specific application, we carried out a systematic review of the different methods from an algorithmic perspective that classifies, explains and compares the available methods. We also examine the trends and new developments in the area. This review provides recommendations to researchers regarding the suitable media optimization algorithm for their applications and we hope to also promote the development of new cell culture media optimization methods that are better suited to existing and upcoming challenges in this biotechnology field, which will be essential for more efficient production of various cell culture products.

A review of yeast: High cell-density culture, molecular mechanisms of stress response and tolerance during fermentation

Yeast is widely used in the fermentation industry, and the major challenges in fermentation production system are high capital cost and low reaction rate. High cell-density culture is an effective method to increase the volumetric productivity of the fermentation process, thus making the fermentation process faster and more robust. During fermentation, yeast is subjected to various environmental stresses, including osmotic, ethanol, oxidation, and heat stress. To cope with these stresses, yeast cells need appropriate adaptive responses to acquire stress tolerances to prevent stress-induced cell damage. Since a single stressor can trigger multiple effects, both specific and nonspecific effects, general and specific stress responses are required to achieve comprehensive protection of cells. Since all these stresses disrupt protein structure, the upregulation of heat shock proteins and trehalose genes is induced when yeast cells are exposed to stress. A better understanding of the research status of yeast HCDC and its underlying response mechanism to various stresses during fermentation is essential for designing effective culture control strategies and improving the fermentation efficiency and stress resistance of yeast.

Recent advances in systems and synthetic biology approaches for developing novel cell-factories in non-conventional yeasts

Microbial bioproduction of chemicals, proteins, and primary metabolites from cheap carbon sources is currently an advancing area in industrial research. The model yeast, Saccharomyces cerevisiae, is a well-established biorefinery host that has been used extensively for commercial manufacturing of bioethanol from myriad carbon sources. However, its Crabtree-positive nature often limits the use of this organism for the biosynthesis of commercial molecules that do not belong in the fermentative pathway. To avoid extensive strain engineering of S. cerevisiae for the production of metabolites other than ethanol, non-conventional yeasts can be selected as hosts based on their natural capacity to produce desired commodity chemicals. Non-conventional yeasts like Kluyveromyces marxianus, K. lactis, Yarrowia lipolytica, Pichia pastoris, Scheffersomyces stipitis, Hansenula polymorpha, and Rhodotorula toruloides have been considered as potential industrial eukaryotic hosts owing to their desirable phenotypes such as thermotolerance, assimilation of a wide range of carbon sources, as well as ability to secrete high titers of protein and lipid. However, the advanced metabolic engineering efforts in these organisms are still lacking due to the limited availability of systems and synthetic biology methods like in silico models, well-characterised genetic parts, and optimized genome engineering tools. This review provides an insight into the recent advances and challenges of systems and synthetic biology as well as metabolic engineering endeavours towards the commercial usage of non-conventional yeasts. Particularly, the approaches in emerging non-conventional yeasts for the production of enzymes, therapeutic proteins, lipids, and metabolites for commercial applications are extensively discussed here. Various attempts to address current limitations in designing novel cell factories have been highlighted that include the advances in the fields of genome-scale metabolic model reconstruction, flux balance analysis, 'omics'-data integration into models, genome-editing toolkit development, and rewiring of cellular metabolisms for desired chemical production. Additionally, the understanding of metabolic networks using ¹³C-labelling experiments as well as the utilization of metabolomics in deciphering intracellular fluxes and reactions have also been discussed here. Application of cutting-edge nuclease-based genome editing platforms like CRISPR/Cas9, and its optimization towards efficient strain engineering in non-conventional yeasts have also been described. Additionally, the impact of the advances in promising non-conventional yeasts for efficient commercial molecule synthesis has been meticulously reviewed. In the future, a cohesive approach involving systems and synthetic biology will help in widening the horizon of the use of unexplored non-conventional yeast species towards industrial biotechnology.