Enzymes are biocatalysts with complex structures and specific catalytic mechanisms that determine their distinctive properties, such as high catalytic activity and selectivity of specific substrates. Oxidoreductase (OXR) enzymes are in high demand for biocatalytic applications in the food industry and cosmetics (glucose oxidase (GOx) and cellobiose dehydrogenase (CDH)), bioremediations (horseradish peroxidase (HRP) and laccase (LAC)), and medicine for biosensors and miniature biofuel cells (GOx, CDH, LAC, and HRP). Therefore, scientists are still trying to find optimal fermentation formulas and, most recently, also using protein engineering and directed evolution for an additional increase in the yield of recombinant enzyme production.
Source | Variant of Oxidoreductase Enzyme | Host Strain; Vector | Promoter | Inducer | Signal Sequence | Additional Information | Enzyme Yield | Ref. |
---|---|---|---|---|---|---|---|---|
GOx | ||||||||
A. niger | NR | 2805; YEp352 | GAL1 | 1% galactose | ss of α-factor | NR | 32 a U/mL | [36] |
A. oryzae | NR | 2805 | GAL-10 | NR | α-amylase signal sequence | 30 °C, 150 rpm, feedback-controlled fed-batch | NR | [37] |
A. niger | NR | 2805; Yep352 | Hybrid ADH2-GPD | 2% glucose | ss of α-factor | 1.5% EtOH | 260 a U/mL | [36] |
A. niger | NR | |||||||
Native | ||||||||
Shake flask 0.3 L; 0.5 mM CuSO | ||||||||
4 | ||||||||
; 20 °C; 6 days | ||||||||
45 | ||||||||
e | ||||||||
U/L | ||||||||
[ | 45 | ] | ||||||
L. edodes | Lcc4 | FGY217; pBG13 |
Source | Variant of Oxidoreductase Enzyme | Host Strain; Vector | Promoter | Inducer | Signal Sequence | Additional Information | Enzyme Yield | Ref. | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
GOx | |||||||||||||||||
A. niger | GOx accc30161 | SMD1168;pGAPZαA | GAP | NR | ss of α-factor | 30 °C; pH 6 | 107.18 a U/mL | [59] | |||||||||
A. niger | GOxM | SMD1168; pPIC3.5 | AOX1 | 1% MeOH | 30 °C; 3 days, 220 rpm | 26.93 a U/mL | [60] | ||||||||||
A. niger ATCC 9029 | - | GS115; pPIC9 | AOX1 | 1% MeOH | 28 °C; 225 rpm | NR | [61] | ||||||||||
GRF181 | pSGO2 | ADH2-GPD | 8% sucrose | Native | Shake flask; 28 °C; 200 h | 106 a U/mL | [38] | ||||||||||
A. niger | M12 mutant | KM71H; pPICZαA | AOX | 0.5% MeOH | Proalpha sequence | Nine days of fermentation | 17.5 b U/mL | CDH | |||||||||
[ | 62 | ] | M. thermophilum | Wild type | BJ5465; pJRoC30 | GAL1 | 2% galactose | Native | Deep-well plate (500 µL of medium); 30 °C; 5 days | 50 b U/L | [39] | ||||||
M. thermophilum | Wild type | BJ5465; pJRoC30 | GAL1 | 2% galactose | ss of α factor | Deep-well plate (500 µL of medium); 30 °C; 5 days | 16 b U/L | [39] | |||||||||
P. chrysosporium | U46081.1 | InvSC1; pYES2 | GAL1 | Galactose | Native | Shake flask; 30 °C; 16 h | NR | [40] | |||||||||
T. clypeatus | GAFV01008428.1 | BY4742; pFL61 | PGK | No | No | NR type of cultivation; Czapek medium, 3 days | 0.039 | Mutant | KM71H; pPICZαAb | AOX1 U/mg | 0.5% MeOH | ss of α-factor[ | Shake flask; 28 °C; 6 days41] | ||||
950 | c | U/L | [ | 64 | ] | HRP | |||||||||||
Horseradish | Wild type | SIP-Ost1 (Δ44–70); modified pESC-URA | TDH3 | pre-Ost1 | Fermenter 5 L (batch fermentation) | 13,506 c U/L | [42] | ||||||||||
Horseradish | |||||||||||||||||
HRP | |||||||||||||||||
Horseradish | wild type | X-33; pPICZαB | AOX1 | 0.5% MeOH | ss of α-factor | 30 °C; BMGY medium supplemented with 1% casamino acids; BMMY medium supplemented with 1.0 mM vitamin B1, 1.0 mM δ-ALA, and trace element mix; the highest yield in 80–90 h post-induction | 377 d U/mg | [43] | HRP 3-17E12 | BJ5465; pYEX-S1 | PGK1 | No | NR | ||||
Horseradish | mutant HRP 2-13A10 | X-33; pPICZαB | AOX1 | 0.5% MeOH | Expression time 25 h | about 250 | c U/L | ss of α-factor[43] | |||||||||
Same as for wild-type | 2053 | d | U/mg | [ | 43 | LAC | |||||||||||
] | M. thermophila | MtL | BJ5465; pJRoC3 | NR | NR | NR | Shake flask 2.8 L; 0.005 mM CuSO4 30 °C; 1 day | 0.6 d U/L | [44] | ||||||||
M. thermophila | T2 mutant | BJ5465; pJRoC3 | NR | NR | NR | Shake flask 2.8 L; 0.005 mM CuSO4; 30 °C; 1 day | 102 d U/L | [44] | |||||||||
T. versicolor | Cvl3 | BY2777; pYES2 | GAL1 | ||||||||||||||
Horseradish | mutant HRP 3-17E12 | X-33; pPICZαB | AOX1 | 0.5% MeOH | ss of α-factor | Same as for wild-type | 1049 d U/mg | [43] | |||||||||
Horseradish | A2A isoenzyme | X-33; pPICZαC | AOX1 | 0.5% MeOH | α-MF-pre-pro signal peptide | BMMY medium supplemented with 1% casamino acids and 1% sorbitol | 25.63 a U/mg | [65] | |||||||||
Horseradish | HRP-SpG | PpFWK3; pPpT4_alpha_S | AOX1 | MeOH | NR | 136 h of methanol induction | 113 d mg/L | [66] | |||||||||
LAC | GAL1 | 4% Galactose | |||||||||||||||
C. cinerea | Native | Lcc9 | X33; pGAPZαA | Fermentor 4 L; 0.5 mM CuSO | GAP | 4; 20 °C; 7 days | 0.5% glucose | ss of α-factor | Shake flasks 0.25 L; 0.3 mM CuSO10 e U/L | [46] | |||||||
4% Galactose | 4 | ; 20 °C; 4 days; 0.8% alanine | 12.8 | f | µkat/L | [ | 67 | ] | A. pediades | ApL | BJ5465; pJRoC30 | GAL1 | 2.2% Galactose | α9H2 signal peptide | Shake flask 0.1 L; 0.4 mM CuSO4; 20 °C; 4 days | 280 e U/L | [47] |
Trametes sp. C30 | Clac1, 2, 3 | W303-1A; YIp351 | PGK1 | No | ss of SUC2 gene product | Fermentor 3 L; 1 mM CuSO4; 28 °C; 3 days | 1200 d U/L | [48] | |||||||||
M. thermophila | T2 mutant | BW31a; pVT-100U | ADH1 | No | Native | Shake flask 0.25 L; 0.6 mM CuSO4; 30 °C; 1 day; 0.8% alanine | 6.52 e U/L | [49] | |||||||||
T. versicolor | Lcc1 | BW31a; pVT-100U | ADH1 | No | Native | Shake flask 0.25 L; 0.6 mM CuSO4; 30 °C; 1 day; 0.8% alanine | 0.45 e U/L | [49] | |||||||||
T. trogii | Lcc1 | BW31a; pVT-100U | ADH1 | No | Native | Shake flask 0.25 L; 0.6 mM CuSO4; 20 °C; 14 days; 0.8% alanine | 14.12 e U/L | [49] |
CDH | ||||||||
M. thermophilum | N700S mutant | X33; pPICZαA | AOX1 | 0.5% MeOH | ss of α-factor & propeptide | Fermentor 7 L; 30 °C; 5 days | 1800 c U/L | [39] |
P. cinnabarinus | Wild type | X33; pPICZαA | AOX1 | 3% MeOH | ss of α-factor | Fermentor 1 L; 4 days | 7800 c U/L | [63] |
N. crassa strain FGSC 2489 | NC-cdh1 | X33; pPICZαB | AOX1 | 1% MeOH | ss of α-factor | Shake flask 0.25 L; 30 °C; 1 day | 7451 c U/L | [64] |
P. chrysosporium | ||||||||
P. ostreatus | ||||||||
rPOXA 1B | ||||||||
X33; pGAPZαA | ||||||||
GAP | ||||||||
0.5% glucose | ss of α-factor | Bioreactor 10 L; 1 mM CuSO | 4 | ; 2% peptone; 1.5% yeast extract; 170 h; geometry of flask | 3159.93 | f | U/L | [68] |
T. versicolor | Lcc1 | SMD 1168; pHIL-D2 | AOX1 | 0.5% MeOH | Shake flasks 1 L; 0.1 mM CuSO4; 20 °C; 3 days of induction | 11,500 f U/L | [69] | |
T. versicolor | Lcc1 | SMD 1168; pHIL-D2 | AOX1 | 0.5% MeOH | BioFlo III fermentor; 0.1 mM CuSO4; 20 °C; 8.5 days | 140 f U/L | [69] | |
T. versicolor | Lcc1 | GS115; pPIC3.5 | AOX1 | 1% MeOH | Shake flasks (0.1 L; 0.2 mM CuSO4; 22 °C; initial pH 6; 0.8% alanine: | 23.9 f U/L | [70] | |
T. versicolor | LccA | X33; pPICZαB | AOX | 0.6% MeOH | ss of α-factor | Shake flask (0.05 L of medium); 0.5 mM CuSO4; 28 °C; 16 days; initial pH 7; | 11.972 f U/L | [71] |
T. versicolor | LccA | X33; X33; pPICZαB | AOX | 0.6% MeOH | ss of α-factor | 5 L fermenter; 0.5 mM CuSO4; 28 °C; 4.2 days; initial pH 7; | 18.123 f U/L | [71] |
C. gallica | LcCg | X33; pPICZB | AOX | 1% MeOH | Modified α-factor preproleader | Fernbach flask; 0.5 mM CuSO4; 28 °C; 12 days; initial pH 6; 0.8% alanine | 250 e U/L | [72] |
Trameters sp. 48424 | Lac48424-1 | GS115; pPIC3.5K | AOX | 0.5% MeOH | Native | Shake flasks; 0.3 mM CuSO4; 20 °C; 7 days; initial pH 6; 0.8% alanine | 104.45 f U/L | [73] |
C. cinerea | Lcc9 | GS115; pPIC9K | AOX | 0.5% MeOH | Native | Shake flasks 0.5 L; 0.3 mM CuSO4; 28 °C; 10 days; initial pH 6.5; 0.8% alanine | 3138 ± 62 f U/L | [74] |
C. cinerea | Lcc9 | X33; pPICZαA | AOX | 0.5% MeOH | ss of α-factor | Shake flasks 0.25 L; 0.3 mM CuSO4; 20 °C; 7 days; 0.8% alanine | 9.3 f µkat/L | [67] |
In the articles mentioned above, optimization of fermentation was done by changing fermentation media composition, induction time, temperature optimization, etc. Still, there are also trials of increasing the fermentation yield of these enzymes by using state-of-the-art technologies such as precision fermentation [83], directed evolution [84], protein, and strain engineering [85], high-throughput screening methods based on in vitro compartmentalization [86], flow cytometry, and microfluidics.
3.1. Directed evolution, protein and strain engineering
Strain engineering for protein production can be performed using precision fermentation that combines synthetic biology, genetic engineering, and machine learning approaches. This is based on biofoundries that provide an integrated infrastructure for rapid construction, design, and analyzing genetically modified organisms [86]. The first step usually involves generating large host organism libraries via diverse genetic modifications like protease knock-out, cassette modifications, etc. Afterwards comes the screening process. Computational approaches such as deep learning based on artificial neural networks and analyzing genome sequences to predict gene manipulations to enhance recombinant protein production have recently been used [87][88][89]. This approach can predict the production performance of well-studied organisms like S. cerevisiae [90] and P. pastoris [91] and optimize metabolic flux via altering genes involved in the metabolic network [92]. Therefore, the machine learning approach is proven capable of recommending strain engineering strategies [92].Strain engineering for protein production can be performed using precision fermentation that combines synthetic biology, genetic engineering, and machine learning approaches. This is based on biofoundries that provide an integrated infrastructure for rapid construction, design and analysing genetically modified organisms [87]. The first step usually involves generating large host organism libraries by diverse genetic modifications like protease knock-out, cassette modifications, etc. Afterwards comes the screening process. Computational approaches such as deep learning based on artificial neural networks and analyzing genome sequences for predicting gene manipulations to enhance recombinant protein production have recently been used [88–90]. This approach can predict the production performance of well-studied organisms like S.cerevisiae [91], P. pastoris [92] and optimize metabolic flux via altering genes involved in the metabolic network [93]. Therefore, the machine learning approach is proven capable of recommending strain engineering strategies [93].
Adaptive laboratory evolution is another approach for improving microbial phenotypes in many organisms [93]. In this approach, microbes are cultured in a desired growth environment for an extended period, allowing natural selection to enrich mutant strains. The evolved strains are later characterized and their DNA is sequenced to find adaptive mutations that enable phenotypic improvement.Adaptive laboratory evolution is another approach for improving microbial phenotypes in many organisms [94]. In this approach, microbes are cultured in a desired growth environment for an extended period, allowing natural selection to enrich mutant strains. Evolved strains are later characterized and their DNA sequenced to find adaptive mutations that enable phenotypic improvement.
The expression of recombinant proteins in S. cerevisiae, especially oxidoreductases, can be increased using synthetic biology methods by choosing suitable promotors, selectable markers, and plasmids. Further, an increase in enzyme production can also be enhanced by utilizing various secretion factors. For example, it can be increased dramatically via site-directed mutagenesis or directed evolution of secretion peptide recombinant protein production. For instance, protein engineering approaches were carried out by Aza et al. to facilitate the heterologous production of various laccases by S. cerevisiae that included best-evolved signal peptides, new N-glycosylation sites in the enzyme genes, and consensus enzyme design to enhance protein folding and stability [94]. The introduction of N-glycosylation sites is case specific since it can lead to decreased activity but also can enhance protein folding and, therefore, the enzyme activity. Authors obtained mutated α-factor preproleader αExpression of recombinant proteins in S.cerevisiae, especially oxidoreductases, can be increased using synthetic biology methods by choosing suitable promotors, selectable markers, and plasmids. Further, an increase in enzyme production can also be enhanced by utilizing various secretion factors. For example, it can be increased dramatically by site-directed mutagenesis or directed evolution of secretion peptide recombinant protein production. For instance, protein engineering approaches were carried out by Aza et al. to facilitate the heterologous production of various laccases by S.cerevisiae that included best-evolved signal peptides, new N-glycosylation sites in the enzyme genes, and consensus enzyme design for enhancing protein folding and stability [95]. The introduction of N-glycosylation sites is case specific since it can lead to decreased activity but also can enhance protein folding and, therefore, the enzyme activity. Authors obtained mutated α-factor preproleader α
9H2 that enhanced LAC production in the yeast twofold. Using other above-mentioned protein engineering strategies, they obtained 37 mg/L of ascomycete LAC. The same authors in another publication designed an improved universal signal peptide αthat enhanced LAC production in the yeast twofold. Using other above-mentioned protein engineering strategies, they obtained 37 mg/L of ascomycete LAC. The same authors in another publication designed improved universal signal peptide α
OPTby adding four mutations into the α
9H2 preproleader sequence [52].preproleader sequence [96].
P. pastoris was used by Zhou et al. in 2023 for the expression of GOx; by screening different signal peptides, introducing multiple copies of genes, and engineering vesicle trafficking, the hyperproducing strain G1Ese (co-expressing trafficking components EES and SEC) was obtained that could produce up to 7223 U/mL with 30.7 g/L of GOx—that is 3.3 fold higher than the highest level reported so far [95]. It is also possible to engineer the P. pastoris strain via co-expression of chaperons and protein disulfide isomerase in these yeast cells [96]. To increase the secretory expression of heterologous proteins in P. pastoris, Duan et al. screened endogenous signal peptides and protein folding factors. Their effects on the expression of three reporter proteins were tested and they were able to identify the Msb2 signal peptide and Dan4 signal peptide, both of which increase recombinant protein secretion 8 and 172 fold, respectively, compared to the alpha-mating preproleader sequence in P. pastoris [97].Ito et al. l., in their recent work, created a terminator catalog by testing 72 sequences of terminators from S. cerevisiae and P. pastoris and found that terminator RNA sequences from S. cerevisiae maintain function when transferred to P. pastoris [100]. They managed to fine-tune protein expression levels in metabolic engineering and synthetic biology in P. pastoris and enhance them 17-fold. In a similar work on RNAi expression tuning, Wang et all. found genes with functions in cellular metabolism, protein modification and degradation, and cell cycle that can significantly influence the expression level of proteins in S. cerevisiae [101].
One of the problems with expressing recombinant proteins in Pichia can be glycosylation, although it is a much bigger problem for S. cerevisiae and usually glycosylation is necessary for eukaryotic proteins to be correctly folded and expressed. To solve this problem, strain glyco-engineering trials of Pichia were performed to prevent hyperglycosylation and enable a higher fermentation yield of recombinant peroxidases [100]. Different glycoengineered P. pastoris strains were developed, and the physiology and growth behaviors of Man5GlcNAc2 glycosylating P. pastoris strain in the controlled environment of a bioreactor were characterized using flow cytometry during the expression of the HRP C1A isoform of the enzyme. The HRP C1A isoform expressed in the novel glycoengineered Pichia strain had similar kinetic characteristics to the one expressed in the wild-type Pichia strain. Still, the thermal stability of the recombinant HRP was decreased due to the reduced glycosylation. Furthermore, the recombinant enzyme formation rate in the novel strain increased from 0.77 U/gh to 1.05 U/gh during fermentation.One of the problems with expressing recombinant proteins in Pichia can be glycosylation, although its is a much bigger problem for S. cerevisiae and usually, glycosylation is necessary for eukaryotic proteins to be correctly folded and expressed. To solve this problem, strain glyco-engineering trials of Pichia were done to prevent hyperglycosylation and enable higher fermentation yield of recombinant peroxidases [102]. Different glycoengineered P. pastoris strains were developed, and the physiology and growth behaviors of Man5GlcNAc2 glycosylating P. pastoris strain in the controlled environment of a bioreactor was characterized using flow cytometry during expression of HRP C1A isoform of the enzyme. The HRP C1A isoform expressed in the novel glycoengineered Pichia strain had similar kinetic characteristics as the one expressed in the wild-type Pichia strain. Still, the thermal stability of the recombinant HRP was decreased due to the reduced glycosylation. Still, the recombinant enzyme formation rate in the novel strain increased from 0.77 U/gh to 1.05 U/gh during fermentation.
3.2. High-throughput screening methods
3.2.1. Flow cytometry
To follow the influence of various factors on protein production in P. pastoris, it is essential to be able to follow the physiological state of recombinant yeast cells. Hyka et al. quantified factors affecting the physiological state of recombinant P. pastoris Mut+ (methanol utilization-positive) by using a combination of staining with different fluorescent dyes and analysis via flow cytometry [101]. The authors found that cell vitalities could range from 5% to 95% in high-cell-density cultures with strain-producing HRP, depending on the influence of various stresses such as recombinant protein expression, high cell density, and pH. This quantitative assessment of the individual cells’ physiology using flow cytometry enables the implementation of innovative concepts in bioprocess development. This is especially important because the paradigm assumes a uniform cell population and does not differentiate between individual cells whose state can only be followed by single-cell analysis. The conclusion was that only part of the cell population contributes to the recombinant protein production, and the objective should be to maintain productive cells over a long period for as long as possible.To follow the influence of various factors on protein production in P. pastoris, it is essential to be able to follow the physiological state of recombinant yeast cells. Hyka et al. quantified factors affecting the physiological state of recombinant P. pastoris Mut+ (methanol utilization-positive) by using a combination of staining with different fluorescent dyes and analyzing by flow cytometry [103]. The authors found that cell vitalities could range from 5% to 95% in high-cell-density cultures with strain-producing HRP, depending on the influence of various stresses such as recombinant protein expression, high cell density, and pH. This quantitative assessment of the individual cells' physiology using flow cytometry enables the implementation of innovative concepts in bioprocess development. This is especially important because the paradigm assumes a uniform cell population and does not differentiate between individual cells whose state can only be followed by single-cell analysis. The conclusion was that only part of the cell population contributes to the recombinant protein production, and the objective should be to maintain productive cells over a long period as long as possible.
Flow cytometry can also be used for the following expression and correct folding of active proteins like cytochrome c peroxidase when the recombinant protein is fused with a green fluorescent protein (GFP) [102].Flow cytometry can also be used for following expression and correctly folding active proteins like cytochrome c peroxidase when recombinant protein is fused with a green fluorescent protein (GFP) [104].
3.2.2. Microfluidics
Since the screening phase and early process development based on microtiter plates and flasks still represents a bottleneck due to the high cost and time-consuming procedures, Totaro et al. developed a screening protocol for P. pastoris clone selection based on the multiplexed microfluidic device using 15 µL cultivation chambers that were able to operate in perfusion mode and monitor dissolved oxygen content in the culture in a non-invasive way [103]. Using a microfluidic platform, the authors identified the best producer clone after 12 h from inoculation and confirmed the results via lab-scale fermentation.Since the screening phase and early process development based on microtiter plates and flasks still represent a bottleneck due to the high cost and time-consuming procedures, Totaro et al. developed a screening protocol for P. pastoris clone selection based on the multiplexed microfluidic device using 15 µL cultivation chambers that were able to operate in perfusion mode and monitor dissolved oxygen content in the culture in a non-invasive way [105]. Using a microfluidic platform, the authors identified the best producer clone after 12 h from inoculation and confirmed the results by lab-scale fermentation.
Microfluidics combined with flow cytometry were also used for high-throughput droplet screening and genome sequencing analysis to improve the amylase-producing A. oryzae strain [104]. In this work, 450,000 droplets were screened within two weeks, and a high-producing strain with 6.6-fold increased production was found.Microfluidics combined with flow cytometry was also used for high.-throughput droplet screening and genome sequencing analysis for improved amylase-producing A. oryzae strain [106]. This work, 450000 droplets were screened within two weeks, and a high-producing strain with 6.6 folds increased production was found.
3.2.3. In vitro compartmentalization
In vitro, compartmentalization is often used in protein engineering and can be made in a polydisperse format for single-cell experiments, or it can be made in a monodisperse format via microfluidics [105]. Microspheres made of soft materials are also used in protein engineering as an alternative to liquid compartments [106]. Both of these compartmentalization methods can be used to not only improve enzyme activity and stability but also production yield during fermentation; usually, the best way to do so is to perform directed evolution experiments using strains for production.In vitro, compartmentalization is often used in protein engineering and can be made in polydisperse format for single-cell experiments, or it can be made in monodisperse format by microfluidics [107]. Microspheres made of soft materials are also used in protein engineering as an alternative to liquid compartments [108]. Both of these compartmentalization methods can be used to improve not only enzyme activity and stability but also production yield during fermentation, and usually, the best way to do so is to perform directed evolution experiments using strains for production.
To optimize recombinant protein production in yeasts (P. pastoris), droplet microfluidics can be used to encapsulate (compartmentalize) large genetic libraries of strains within biocompatible gel beads that are engineered to selectively retain any recombinant proteins of interest by binding it via His tag usually used for labeling and purification; afterward, staining of secreted protein using fluorescent dyes occurs [107]. This platform can be used broadly for various proteins, including oxidoreductases. As proof of principle, authors found a P. pastoris strain that 5.7-fold increased recombinant cutinase production after screening more than 10To optimize recombinant protein production in yeasts (P. pastoris), droplet microfluidics can be used to encapsulate (compartmentalize) large genetic libraries of strains within biocompatible gel beads that are engineered to selectively retain any recombinant protein of interest by binding it via His tag usually used for labeling and purification, and afterward staining of secreted protein with fluorescent dyes [109]. This platform can be used broadly for various proteins, including oxidoreductases. As proof of principle, authors found P. pastoris strain that 5.7-fold increased recombinant cutinase production after screening more than 10
6genotypes.
Compartmentalization within double emulsion can also be used to optimize recombinant protein production instead of beads. In vitro, compartmentalization within a double emulsion of water in oil was performed using microfluidics and fluorinated oil. The fluorescent immunosensor quench-body detected the secreted recombinant protein (fibroblast growth factor 9), and clones with high protein secretion were detected via fluorimetry [108]. This method also shortens the development period of industrial strains for recombinant protein production.Compartmentalization within double emulsion can also be used to optimize recombinant protein production instead of beads. In vitro, compartmentalization within a double emulsion of water-in-oil-in-water was done using microfluidics and fluorinated oil. The fluorescent immunosensor quench-body detected the secreted recombinant protein (fibroblast growth factor 9), and clones with high protein secretion were detected by fluorimetry [110]. This method also shortens the development period of industrial strains for recombinant protein production.
Optimization of culture conditions represents one of the most used techniques to overcome the problem of low yield since the composition of medium plays a significant role in the production of recombinant protein. Establishing optimal reaction conditions such as pH and temperature is one of the critical steps for a higher yield of recombinant expression.
It can be concluded that, despite the problems with the expression of OXR in yeasts like S. cerevisiae and P. pastoris due to the necessity of adding transition metals (copper and iron) and metabolic precursors of FAD and heme during fermentation, there are various approaches to increase the expression yield of these enzymes. Some of them are optimizing fermentation conditions, the codon usage, using strong promoters and terminators, and multi-copy expression vectors. P. pastoris usually gives higher expression yields of proteins with lesser glycosylation levels compared to S. cerevisiae, which usually gives smaller expression yields of recombinant proteins, very high glycosylation levels, and microheterogeneity of expressed proteins. Still, there are always exceptions depending on the specific recombinant protein and used yeast strain.It can be concluded that despite the problems with the expression of OXR in yeasts like S. cerevisiae and P. pastoris due to the necessity of adding transition metals (copper, iron) and metabolic precursors of FAD and heme during fermentation, there are various approaches to increase expression yield of these enzymes. Some of them are optimizing fermentation conditions, the codon usage, using strong promoters and terminators, and multi-copy expression vectors. P. pastoris usually gives higher expression yields of proteins with lesser glycosylation levels compared to S. cerevisiae, which usually gives smaller expression yields of recombinant proteins, very high glycosylation levels, and microheterogeneity of expressed proteins. Still, there are always exceptions depending on the specific recombinant protein and used yeast strain.
As we could see from the literature recently, there are also new possibilities to further increase in fermentation yield that explore cutting-edge technologies such as directed evolution, protein and strain engineering, high-throughput screening methods based on in vitro compartmentalization, flow cytometry, and microfluidics.As we could see from the literature recently, there are also new possibilities for further increase in fermentation yield that explore cutting-edge technologies such as directed evolution, protein and strain engineering, high-throughput screening methods based on in vitro compartmentalization, flow cytometry, and microfluidics.