Chen Xin, Li Feiran, Li Xiaowei, Otto Maximilian, Chen Yu, Siewers Verena
Division of Systems and Synthetic Biology, Department of Life Sciences, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800, Kgs. Lyngby, Denmark.
Division of Systems and Synthetic Biology, Department of Life Sciences, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden; Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, 518055, Shenzhen, China.
Metab Eng. 2025 Mar;88:1-13. doi: 10.1016/j.ymben.2024.11.010. Epub 2024 Nov 29.
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.
重组蛋白的生产被视为生物医学和工业生物技术领域的一项重要突破。由于蛋白质分泌途径的复杂性及其与细胞代谢的紧密相互作用，应用传统代谢工程工具来提高重组蛋白产量面临重大挑战。需要一种系统的方法来生成用于构建高效蛋白质分泌细胞工厂的新设计原则。在此，我们应用了酿酒酵母的蛋白质组约束基因组规模蛋白质分泌模型（pcSecYeast），以模拟在分泌能力有限的情况下α-淀粉酶的生产，并预测用于下调和上调以提高α-淀粉酶产量的基因靶点。使用专门设计的CRISPR干扰/激活（CRISPRi/a）文库的高通量筛选和液滴微流控筛选对预测的靶点进行评估。分别从每个文库中手动验证了200个和190个分选的克隆。其中，50%的预测下调靶点和34.6%的预测上调靶点被证实可提高α-淀粉酶产量。通过同时微调中心碳代谢中三个基因LPD1、MDH1和ACS1的表达，我们能够增加发酵途径中的碳通量并提高α-淀粉酶产量。本研究例证了如何通过高通量筛选方法快速验证基于模型的预测。我们的发现突出了细胞工厂的新工程靶点，并进一步揭示了重组蛋白生产与中心碳代谢之间的联系。
