Metabolic flux analysis and pharmaceutical production

Boghigian, Brett A.; Seth, Gargi; Kiss, Róbert; Pfeifer, Blaine A.

doi:10.1016/j.ymben.2009.10.004

Cited by 102 publications

(54 citation statements)

References 194 publications

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“…The importance of assessing the global effect of mutations is becoming increasingly apparent. The complete sequencing of the T. reesei QM6a genome makes the wild-type strain suitable for targeted metabolic engineering strategies for both improved cellulase production (reviewed by Kubicek et al, 2009) and heterologous protein production (reviewed by Boghigian et al, 2010;Matsuoka & Shimizu, 2010;Melzer et al, 2009). Metabolic modelling and flux analysis, in which the flow of carbon and nitrogen can be tracked through a metabolic network, may provide insight into bottlenecks along metabolic pathways that cause reduced yields.…”

Section: Rut-c30 As a Host For Heterologous Protein Productionmentioning

confidence: 99%

Trichoderma reesei RUT-C30 – thirty years of strain improvement

2012

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The hypersecreting mutant Trichoderma reesei RUT-C30 (ATCC 56765) is one of the most widely used strains of filamentous fungi for the production of cellulolytic enzymes and recombinant proteins, and for academic research. The strain was obtained after three rounds of random mutagenesis of the wild-type QM6a in a screening program focused on high cellulase production and catabolite derepression. Whereas RUT-C30 achieves outstanding levels of protein secretion and high cellulolytic activity in comparison to the wild-type QM6a, recombinant protein production has been less successful. Here, we bring together and discuss the results from biochemical-, microscopic-, genomic-, transcriptomic-, glycomic-and proteomic-based research on the RUT-C30 strain published over the last 30 years.

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Section: Rut-c30 As a Host For Heterologous Protein Productionmentioning

confidence: 99%

Trichoderma reesei RUT-C30 – thirty years of strain improvement

2012

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“…However, we also note that the coefficient of KNO C2 is slightly negative (À0.018) which means that removal of competitive pathways (i.e., genetic knockout) may not successfully improve the biosynthesis yield. While this conclusion seems highly uncertain ( P-value ¼ 0.88; standard error ¼ 0.11), it has been shown via in silico analysis that knockout strategies cannot ensure the improvement for product yield even though it is expected to channel more carbon to the final product (Blazeck and Alper, 2010;Boghigian et al, 2010;Feist et al, 2010;Meadows et al, 2010). This inconsistency can be attributed to unfavorable metabolite accumulation and low capacity of biosynthesis pathways for flux amplification after removal of competitive pathways.…”

Section: à0018mentioning

confidence: 99%

“…To this end, systems biology-based models have been developed to provide useful information for rationally engineering microbial hosts with the desired phenotype as well as to design optimal fermentation conditions (Blazeck and Alper, 2010;Boghigian et al, 2010;Feist et al, 2010;Meadows et al, 2010). Cell-wide metabolic analysis via fluxomics and metabolic control theories are often used to estimate metabolic potential, product yield, nutrient limitations, and gene targets for metabolic engineering (Feist et al, 2010;Wildermuth, 2000).…”

Section: Introductionmentioning

confidence: 99%

Evaluating Factors That Influence Microbial Synthesis Yields by Linear Regression with Numerical and Ordinal Variables

Colletti

Goyal

Varman

et al. 2010

Biotech & Bioengineering

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In the production of chemicals via microbial fermentation, achieving a high yield is one of the most important objectives. We developed a statistical model to analyze influential factors that determine product yield by compiling data obtained from engineered Escherichia coli developed within last 10 years. Using both numerical and ordinal variables (e.g., enzymatic steps, cultivation conditions, and genetic modifications) as input parameters, our model revealed that cultivation modes, nutrient supplementation, and oxygen conditions were the three significant factors for improving product yield. Generally, the model showed that product yield decreases as the number of enzymatic steps in the biosynthesis pathway increases (7-9% loss of yield per enzymatic step). Moreover, overexpression of enzymes or removal of competitive pathways (e.g., knockout) does not necessarily result in an amplification of product yield (P-value>0.1), possibly because of limited capacity in the biosynthesis pathway to accommodate an increase in flux. The model not only provides general guidelines for metabolic engineering and fermentation processes, but also allows a priori estimation and comparison of product yields under designed cultivation conditions.

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“…In the past two decades, metabolic flux analysis (MFA) has emerged as a powerful technique for characterizing intracellular metabolic fluxes in living cells [20][21][22][23][24][25]. Determining in vivo fluxes provides quantitative information on the degree of engagement of various metabolic pathways in the overall cellular metabolism [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Towards dynamic metabolic flux analysis in CHO cell cultures

Ahn

Antoniewicz

2011

Biotechnology Journal

111

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Chinese hamster ovary (CHO) cells are the most widely used mammalian cell line for biopharmaceutical production, with a total global market approaching $100 billion per year. In the pharmaceutical industry CHO cells are grown in fed-batch culture, where cellular metabolism is characterized by high glucose and glutamine uptake rates combined with high rates of ammonium and lactate secretion. The metabolism of CHO cells changes dramatically during a fed-batch culture as the cells adapt to a changing environment and transition from exponential growth phase to stationary phase. Thus far, it has been challenging to study metabolic flux dynamics in CHO cell cultures using conventional metabolic flux analysis techniques that were developed for systems at metabolic steady state. In this paper we review progress on flux analysis in CHO cells and techniques for dynamic metabolic flux analysis. Application of these new tools may allow identification of intracellular metabolic bottlenecks at specific stages in CHO cell cultures and eventually lead to novel strategies for improving CHO cell metabolism and optimizing biopharmaceutical process performance.

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Metabolic flux analysis and pharmaceutical production

Cited by 102 publications

References 194 publications

Trichoderma reesei RUT-C30 – thirty years of strain improvement

Trichoderma reesei RUT-C30 – thirty years of strain improvement

Evaluating Factors That Influence Microbial Synthesis Yields by Linear Regression with Numerical and Ordinal Variables

Towards dynamic metabolic flux analysis in CHO cell cultures

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