Production of recombinant proteins is regarded as an important breakthrough in the field of biomedicine and industrial biotechnology. Due to the complexity of the protein secretory pathway and its tight interaction with cellular metabolism, the application of traditional metabolic engineering tools to improve recombinant protein production faces major challenges. A systematic approach is required to generate novel design principles for superior protein secretion cell factories. Here, we applied a proteome-constrained genome-scale protein secretory model of the yeast Saccharomyces cerevisiae (pcSecYeast) to simulate α-amylase production under limited secretory capacity and predict gene targets for downregulation and upregulation to improve α-amylase production. The predicted targets were evaluated using high-throughput screening of specifically designed CRISPR interference/activation (CRISPRi/a) libraries and droplet microfluidics screening. From each library, 200 and 190 sorted clones, respectively, were manually verified. Out of them, 50% of predicted downregulation targets and 34.6% predicted upregulation targets were confirmed to improve α-amylase production. By simultaneously fine-tuning the expression of three genes in central carbon metabolism, i.e. LPD1, MDH1, and ACS1, we were able to increase the carbon flux in the fermentative pathway and α-amylase production. This study exemplifies how model-based predictions can be rapidly validated via a high-throughput screening approach. Our findings highlight novel engineering targets for cell factories and furthermore shed light on the connectivity between recombinant protein production and central carbon metabolism.

• PMID: 39615667
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Journal Article

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Chen X, Li F, Li X, Otto M, Chen Y, Siewers V

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