Metabolic Engineering · 2019 · 34 citations · 35 references
Life SciencesBiotechnologyGeneticsGenetic EngineeringGenome EditingMetabolismAmino Acid MetabolismCrispr-based EngineeringCrispr-cas SystemsMetabolic Pathway EngineeringMetabolic ReprogrammingProtein SynthesisCrispr-based Genome EngineeringCrispr-cas Genome EditingCrispr-cas9 Genome EditingCrispr-cas9 KnockoutGenome-scale Metabolic Network ReconstructionLactate ProductionMetabolic Network ReconstructionMetabolic Pathway Reconstruction
Chinese hamster ovary (CHO) cells are the preferred host for producing biopharmaceuticals. Amino acids are biologically important precursors for CHO metabolism; they serve as building blocks for proteogenesis, including synthesis of biomass and recombinant proteins, and are utilized for growth and cellular maintenance. In this work, we studied the physiological impact of disrupting a range of amino acid catabolic pathways in CHO cells. We aimed to reduce secretion of growth inhibiting metabolic by-products derived from amino acid catabolism including lactate and ammonium. To achieve this, we engineered nine genes in seven different amino acid catabolic pathways using the CRISPR-Cas9 genome editing system. For identification of target genes, we used a metabolic network reconstruction of amino acid catabolism to follow transcriptional changes in response to antibody production, which revealed candidate genes for disruption. We found that disruption of single amino acid catabolic genes reduced specific lactate and ammonium secretion while specific growth rate and integral of viable cell density were increased in many cases. Of particular interest were Hpd and Gad2 disruptions, which show unchanged AA uptake rates, while having growth rates increased up to 19%, and integral of viable cell density as much as 50% higher, and up to 26% decrease in specific ammonium production and to a lesser extent (up to 22%) decrease in lactate production. This study demonstrates the broad potential of engineering amino acid catabolism in CHO cells to achieve improved phenotypes for bioprocessing.
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