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Matching the Demand of Protein Production By Metabolic Engineering of the Central Metabolism: Lessons Learned from Cofactor and Amino Acid Metabolism in Yeasts

Russmayer, H., University of Natural Resources and Life Sciences
Hohlweg, J., University of Natural Resources and Life Sciences
Gasser, B., University of Natural Resources and Life Sciences

Metabolic engineering has been successfully applied to redirect metabolic fluxes towards desired products of the primary and secondary metabolism. Complex polymeric products like heterologous proteins also demand redistributions of primary metabolic fluxes, their rational design however is far less obvious. Therefore cell engineering for protein overproduction has concentrated mainly on transcription, codon usage, protein folding and secretion.

Amino acid synthesis has been proposed to be a potential bottleneck during the synthesis of an extra load of heterologous protein, especially when the target protein differs largely in its amino acid composition from the host proteome. We have evaluated such potential limitations in the yeast Pichia pastoris by a combined transcriptomics, proteomics and metabolomics approach. By focusing on genes or proteins which were upregulated and metabolites which strongly decreased in their intracellular concentration upon overexpression of several human and fungal proteins, we pinpointed 6 amino acid synthesis pathways. The identified rate-limiting genes in these pathways were overexpressed, leading not only to larger pools of free amino acid but also to a more than two-fold increase in heterologous protein production. This success could, however, not be transferred to traditional fed batch cultures. Reasons for this failure will be presented, illustrating the added complexity of cultivation to such metabolic engineering procedures.

In a complementary approach we integrated the synthesis of heterologous protein to a genome scale metabolic model and predicted metabolic engineering targets for enhanced productivity with FSEOF for gene overexpression and MOMA for gene deletion. Enhanced productivity of the target protein was verified in more than 50 % of these genetic interventions. The most beneficial mutations were related to reduction of the NADP/H pool, leading to four fold increased product formation in the best case.

Taken together, we demonstrate that genome scale metabolic modeling and metabolomics are suitable to design genetic interventions to the primary metabolism to increase recombinant protein production.