Xu Shumin, Zhang Linpei, Zhou Shenghu, Deng Yu
National Engineering Laboratory for Cereal Fermentation Technology (NELCF), Jiangnan University, Wuxi, Jiangsu, China.
Jiangsu Provincial Research Center for Bioactive Product Processing Technology, Jiangnan University, Wuxi, Jiangsu, China.
Appl Environ Microbiol. 2021 May 26;87(12):e0011321. doi: 10.1128/AEM.00113-21.
Glycolate is widely used in industry, especially in the fields of chemical cleaning, cosmetics, and medical materials, and has broad market prospects for the future. Recent advances in metabolic engineering and synthetic biology have significantly improved the titer and yield of glycolate. However, an expensive inducer was used in previous studies, which is not feasible for use in large-scale industrial fermentations. To constitutively biosynthesize glycolate, the expression level of each gene of the glycolate synthetic pathway needs to be systemically optimized. The main challenge of multigene pathway optimization is being able to select or screen the optimum strain from the randomly assembled library by an efficient high-throughput method within a short time. To overcome these challenges, we firstly established a glycolate-responsive biosensor and developed agar plate- and 48-well deep-well plate-scale high-throughput screening methods for the rapid screening of superior glycolate producers from a large library. A total of 22 gradient-strength promoter-5'-untranslated region (UTR) complexes were randomly cloned upstream of the genes of the glycolate synthetic pathway, generating a large random assembled library. After rounds of screening, the optimum strain was obtained from 6 × 10 transformants in a week, and it achieved a titer of 40.9 ± 3.7 g/liter glycolate in a 5-liter bioreactor. Furthermore, high expression levels of the enzymes YcdW and GltA were found to promote glycolate production, whereas AceA has no obvious impact on glycolate production. Overall, the glycolate biosensor-based pathway optimization strategy presented in this work provides a paradigm for other multigene pathway optimizations. The use of strong promoters, such as pTrc and T7, to control gene expression not only needs the addition of expensive inducers but also results in excessive protein expression that may result in unbalanced metabolic flux and the waste of cellular building blocks and energy. To balance the metabolic flux of glycolate biosynthesis, the expression level of each gene needs to be systemically optimized in a constitutive manner. However, the lack of high-throughput screening methods restricted glycolate synthetic pathway optimization. Our work firstly established a glycolate-response biosensor, and agar plate- and 48-well plate-scale high-throughput screening methods were then developed for the rapid screening of optimum pathways from a large library. Finally, we obtained a glycolate-producing strain with good biosynthetic performance, and the use of the expensive inducer isopropyl-β-d-thiogalactopyranoside (IPTG) was avoided, which broadens our understanding of the mechanism of glycolate synthesis.
乙醇酸在工业中广泛应用，尤其在化学清洗、化妆品和医用材料领域，且未来具有广阔的市场前景。代谢工程和合成生物学的最新进展显著提高了乙醇酸的产量和产率。然而，先前的研究中使用了昂贵的诱导剂，这在大规模工业发酵中不可行。为了组成型生物合成乙醇酸，需要系统地优化乙醇酸合成途径中每个基因的表达水平。多基因途径优化的主要挑战在于能够在短时间内通过高效的高通量方法从随机组装的文库中筛选出最优菌株。为了克服这些挑战，我们首先建立了一种乙醇酸响应生物传感器，并开发了琼脂平板和48孔深孔板规模的高通量筛选方法，用于从大型文库中快速筛选出优良的乙醇酸生产者。总共22种梯度强度的启动子-5'-非翻译区（UTR）复合物被随机克隆到乙醇酸合成途径基因的上游，构建了一个大型随机组装文库。经过多轮筛选，在一周内从6×10个转化子中获得了最优菌株，该菌株在5升生物反应器中乙醇酸的产量达到40.9±3.7克/升。此外，发现YcdW和GltA酶的高表达水平促进了乙醇酸的产生，而AceA对乙醇酸的产生没有明显影响。总体而言，本研究中提出的基于乙醇酸生物传感器的途径优化策略为其他多基因途径优化提供了范例。使用强启动子（如pTrc和T7）来控制基因表达不仅需要添加昂贵的诱导剂，还会导致蛋白质过度表达，这可能导致代谢通量失衡以及细胞组成成分和能量的浪费。为了平衡乙醇酸生物合成的代谢通量，需要以组成型方式系统地优化每个基因的表达水平。然而，缺乏高通量筛选方法限制了乙醇酸合成途径的优化。我们的工作首先建立了乙醇酸响应生物传感器，随后开发了琼脂平板和48孔板规模的高通量筛选方法，用于从大型文库中快速筛选最优途径。最后，我们获得了具有良好生物合成性能的乙醇酸生产菌株，并且避免了使用昂贵的诱导剂异丙基-β-D-硫代半乳糖苷（IPTG），这拓宽了我们对乙醇酸合成机制的理解。
