Santos-Merino María, Gargantilla-Becerra Álvaro, de la Cruz Fernando, Nogales Juan
Instituto de Biomedicina y Biotecnología de Cantabria, Universidad de Cantabria-CSIC, Santander, Cantabria, Spain.
Department of Systems Biology, Centro Nacional de Biotecnología (CSIC), Madrid, Spain.
Front Microbiol. 2023 Mar 14;14:1126030. doi: 10.3389/fmicb.2023.1126030. eCollection 2023.
Cyanobacteria are prokaryotic organisms that capture energy from sunlight using oxygenic photosynthesis and transform CO into products of interest such as fatty acids. PCC 7942 is a model cyanobacterium efficiently engineered to accumulate high levels of omega-3 fatty acids. However, its exploitation as a microbial cell factory requires a better knowledge of its metabolism, which can be approached by using systems biology tools. To fulfill this objective, we worked out an updated, more comprehensive, and functional genome-scale model of this freshwater cyanobacterium, which was termed MS837. The model includes 837 genes, 887 reactions, and 801 metabolites. When compared with previous models of PCC 7942, MS837 is more complete in key physiological and biotechnologically relevant metabolic hubs, such as fatty acid biosynthesis, oxidative phosphorylation, photosynthesis, and transport, among others. MS837 shows high accuracy when predicting growth performance and gene essentiality. The validated model was further used as a test-bed for the assessment of suitable metabolic engineering strategies, yielding superior production of non-native omega-3 fatty acids such as α-linolenic acid (ALA). As previously reported, the computational analysis demonstrated that overexpression is a feasible metabolic target to increase ALA production, whereas deletion and overexpression of cannot be used for this purpose. Flux scanning based on enforced objective flux, a strain-design algorithm, allowed us to identify not only previously known gene overexpression targets that improve fatty acid synthesis, such as Acetyl-CoA carboxylase and β-ketoacyl-ACP synthase I, but also novel potential targets that might lead to higher ALA yields. Systematic sampling of the metabolic space contained in MS837 identified a set of ten additional knockout metabolic targets that resulted in higher ALA productions. simulations under photomixotrophic conditions with acetate or glucose as a carbon source boosted ALA production levels, indicating that photomixotrophic nutritional regimens could be potentially exploited to improve fatty acid production in cyanobacteria. Overall, we show that MS837 is a powerful computational platform that proposes new metabolic engineering strategies to produce biotechnologically relevant compounds, using PCC 7942 as non-conventional microbial cell factory.
蓝细菌是原核生物，通过产氧光合作用从阳光中捕获能量，并将二氧化碳转化为脂肪酸等目标产物。集胞藻PCC 7942是一种经过有效改造的模式蓝细菌，能够积累高水平的ω-3脂肪酸。然而，将其用作微生物细胞工厂需要对其代谢有更深入的了解，这可以通过使用系统生物学工具来实现。为了实现这一目标，我们构建了一个更新的、更全面且具有功能的该淡水蓝细菌的基因组规模模型，命名为MS837。该模型包含837个基因、887个反应和801个代谢物。与之前的集胞藻PCC 7942模型相比，MS837在关键生理和生物技术相关的代谢枢纽方面更完整，如脂肪酸生物合成、氧化磷酸化、光合作用和转运等。MS837在预测生长性能和基因必需性方面具有很高的准确性。经过验证的模型进一步用作评估合适代谢工程策略的试验台，实现了非天然ω-3脂肪酸如α-亚麻酸（ALA）的更高产量。如先前报道，计算分析表明过表达是增加ALA产量的可行代谢靶点，而删除和过表达不能用于此目的。基于强制目标通量的通量扫描（一种菌株设计算法）使我们不仅能够识别先前已知的改善脂肪酸合成的基因过表达靶点，如乙酰辅酶A羧化酶和β-酮脂酰-ACP合酶I，还能识别可能导致更高ALA产量的新潜在靶点。对MS837中包含的代谢空间进行系统采样，确定了另外一组十个敲除代谢靶点，这些靶点导致了更高的ALA产量。在以乙酸盐或葡萄糖作为碳源的光混合营养条件下的模拟提高了ALA的产量水平，表明光混合营养培养方案可能被用于提高蓝细菌中脂肪酸的产量。总体而言，我们表明MS837是一个强大的计算平台，它提出了新的代谢工程策略，以利用集胞藻PCC 7942作为非常规微生物细胞工厂来生产生物技术相关化合物。
