Effects of different glycerol feeding strategies on S-adenosyl-l-methionine biosynthesis by PGAP-driven Pichia pastoris overexpressing methionine adenosyltransferase

Hu, Xiaoqing; Chu, Ju; Zhang, Zhuo; Zhang, Siliang; Zhuang, Yingping; Wang, Yonghong; Guo, Meijin; Chen, Huaxin; Yuan, Zhongyi

doi:10.1016/j.jbiotec.2008.04.009

Cited by 25 publications

(9 citation statements)

References 22 publications

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“…The OUR was at its maximum at t = 7–12 h of the bioprocess, having values of OUR = 47.3 mmol/m 3 /s, 10.0 mmol/m 3 /s, 21.6 mmol/m 3 /s and 23.0 mmol/ m 3 /s, respectively, for the M‐0.03, MS‐0.02, MS‐0.03 and MS‐0.04 conditions, and thereafter de‐ creased (Table 1). The obtained OUR values fell within the range of various OUR values reported previously (15–250 mmol/m 3 /s) for different P. pastoris fermentation strategies (Charoenrat et al , 2006; Hu et al , 2008). Remarkably, the presence of sorbitol appeared to lower the OUR , and increasing methanol feed rates caused an increase in the OUR , in relation to the increase in K L a values.…”

Section: Resultssupporting

confidence: 87%

Fed‐batch methanol feeding strategy for recombinant protein production by Pichia pastoris in the presence of co‐substrate sorbitol

Çelik

Çalı́k

Oliver

2009

Yeast

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Batch-wise sorbitol addition as a co-substrate at the induction phase of methanol fed-batch fermentation by Pichia pastoris (Mut + ) was proposed as a beneficial recombinant protein production strategy and the metabolic responses to methanol feeding rate in the presence of sorbitol was systematically investigated. Adding sorbitol batch-wise to the medium provided the following advantages over growth on methanol alone: (a) eliminating the long lag-phase for the cells and reaching 'high cell density production' at t = 24 h of the process (C X = 70 g CDW/l); (b) achieving 1.8-fold higher recombinant human erythropoietin (rHuEPO) (at t = 18 h); (c) reducing specific protease production 1.2-fold; (d) eliminating the lactic acid build-up period; (e) lowering the oxygen uptake rate two-fold; and (f) obtaining 1.4-fold higher overall yield coefficients. The maximum specific alcohol oxidase activity was not affected in the presence of sorbitol, and it was observed that sorbitol and methanol were utilized simultaneously. Thus, in the presence of sorbitol, 130 mg/l rHuEPO was produced at t = 24 h, compared to 80 mg/l rHuEPO (t = 24 h) on methanol alone. This work demonstrates not only the ease and efficiency of incorporating sorbitol to fermentations by Mut + strains of P. pastoris for the production of any bioproduct, but also provides new insights into the metabolism of the methylotrophic yeast P. pastoris.

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Section: Resultssupporting

confidence: 87%

Fed‐batch methanol feeding strategy for recombinant protein production by Pichia pastoris in the presence of co‐substrate sorbitol

Çelik

Çalı́k

Oliver

2009

Yeast

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“…For example, riboflavin producer, Candida famata , exhibits a high riboflavin productivity (2~3 order of magnitude higher than model prediction) [27]. Pichia pastoris , a common species for protein expression, shows high S-adenosyl-L-methionine productivity if a large amount of the intermediate methionine was repeatedly added in the medium [28]. Besides, Pichia stipitis also has high yields of L-lactic acid and ethanol from glucose and xylose [29].…”

Section: Resultsmentioning

confidence: 99%

Statistics-based model for prediction of chemical biosynthesis yield from Saccharomyces cerevisiae

et al. 2011

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BackgroundThe robustness of Saccharomyces cerevisiae in facilitating industrial-scale production of ethanol extends its utilization as a platform to synthesize other metabolites. Metabolic engineering strategies, typically via pathway overexpression and deletion, continue to play a key role for optimizing the conversion efficiency of substrates into the desired products. However, chemical production titer or yield remains difficult to predict based on reaction stoichiometry and mass balance. We sampled a large space of data of chemical production from S. cerevisiae, and developed a statistics-based model to calculate production yield using input variables that represent the number of enzymatic steps in the key biosynthetic pathway of interest, metabolic modifications, cultivation modes, nutrition and oxygen availability.ResultsBased on the production data of about 40 chemicals produced from S. cerevisiae, metabolic engineering methods, nutrient supplementation, and fermentation conditions described therein, we generated mathematical models with numerical and categorical variables to predict production yield. Statistically, the models showed that: 1. Chemical production from central metabolic precursors decreased exponentially with increasing number of enzymatic steps for biosynthesis (>30% loss of yield per enzymatic step, P-value = 0); 2. Categorical variables of gene overexpression and knockout improved product yield by 2~4 folds (P-value < 0.1); 3. Addition of notable amount of intermediate precursors or nutrients improved product yield by over five folds (P-value < 0.05); 4. Performing the cultivation in a well-controlled bioreactor enhanced the yield of product by three folds (P-value < 0.05); 5. Contribution of oxygen to product yield was not statistically significant. Yield calculations for various chemicals using the linear model were in fairly good agreement with the experimental values. The model generally underestimated the ethanol production as compared to other chemicals, which supported the notion that the metabolism of Saccharomyces cerevisiae has historically evolved for robust alcohol fermentation.ConclusionsWe generated simple mathematical models for first-order approximation of chemical production yield from S. cerevisiae. These linear models provide empirical insights to the effects of strain engineering and cultivation conditions toward biosynthetic efficiency. These models may not only provide guidelines for metabolic engineers to synthesize desired products, but also be useful to compare the biosynthesis performance among different research papers.

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“…This result implies that MAT activity is not the rate-limiting factor for SAM synthesis. To enhance SAM production further, other limiting steps, such as elevating ATP level through pulsed feeding of glycerol (19), optimizing the L-Met feeding strategy (18), or blocking the transsulfuration pathway through cystathionine by disrupting the cystathionine synthase (16), should be targeted. In addition, a negative influence on the cell growth was observed when the specific SAM production exceeded 73 mg/g (dry cell weight [DCW]) (Fig.…”

Section: Vol 77 2011mentioning

confidence: 99%

GAP Promoter Library for Fine-Tuning of Gene Expression in Pichia pastoris

Qin

Qian

Yao

et al. 2011

Appl Environ Microbiol

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124

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A library of engineered promoters of various strengths is a useful genetic tool that enables the fine-tuning and precise control of gene expression across a continuum of broad expression levels. The methylotrophic yeast Pichia pastoris is a well-established expression host with a large academic and industrial user base. To facilitate manipulation of gene expression spanning a wide dynamic range in P. pastoris, we created a functional promoter library through mutagenesis of the constitutive GAP promoter. Using yeast-enhanced green fluorescent protein (yEGFP) as the reporter, 33 mutants were chosen to form the functional promoter library. The 33 mutants spanned an activity range between ϳ0.6% and 19.6-fold of the wild-type promoter activity with an almost linear fluorescence intensity distribution. After an extensive characterization of the library, the broader applicability of the results obtained with the yEGFP reporter was confirmed using two additional reporters (␤-galactosidase and methionine adenosyltransferase [MAT]) at the transcription and enzyme activity levels. Furthermore, the utility of the promoter library was tested by investigating the influence of heterologous MAT gene expression levels on cell growth and S-adenosylmethionine (SAM) production. The extensive characterization of the promoter strength enabled identification of the optimal MAT activity (around 1.05 U/mg of protein) to obtain maximal volumetric SAM production. The promoter library permits precise control of gene expression and quantitative assessment that correlates gene expression level with physiologic parameters. Thus, it is a useful toolbox for both basic and applied research in P. pastoris.The creation of a library of engineered promoters of various strengths has enabled the precise control of gene expression in a broad range of activities required for detailed genotypephenotype characterization and the identification of an optimal gene expression level for the desired product yield (1,4,12,20,22,29,35). For example, a library of promoters has been used to assess the influence of phosphoenolpyruvate carboxylase levels on growth yield and deoxyxylulose-P synthase levels on lycopene production and to demonstrate that the optimal expression levels of deoxyxylulose-P synthase are different in a strain preengineered to produce lycopene and in the parental strain (1). In another example, random mutagenesis of the Pm promoter has been applied to improve production of the medically important granulocyte-macrophage colony-stimulating factor in Escherichia coli at an industrial level (4). Furthermore, promoter libraries coupled with in silico modeling have been used to guide predictable gene network construction in Saccharomyces cerevisiae (13). Therefore, the promoter library constitutes a valuable addition to the genetic toolbox for the study of metabolic engineering, functional genomics, and synthetic biology, as well as for various biotechnology applications. However, this powerful tool is implemented mainly in bacteria (2,4,12,20,35,36...

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Effects of different glycerol feeding strategies on S-adenosyl-l-methionine biosynthesis by PGAP-driven Pichia pastoris overexpressing methionine adenosyltransferase

Cited by 25 publications

References 22 publications

Fed‐batch methanol feeding strategy for recombinant protein production by Pichia pastoris in the presence of co‐substrate sorbitol

Fed‐batch methanol feeding strategy for recombinant protein production by Pichia pastoris in the presence of co‐substrate sorbitol

Statistics-based model for prediction of chemical biosynthesis yield from Saccharomyces cerevisiae

GAP Promoter Library for Fine-Tuning of Gene Expression in Pichia pastoris

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