Kim Minsuk, Park Beom Gi, Kim Eun-Jung, Kim Joonwon, Kim Byung-Gee
1Institute of Engineering Research, Seoul National University, Seoul, 08826 Republic of Korea.
5Present Address: Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905 USA.
Biotechnol Biofuels. 2019 Jul 24;12:187. doi: 10.1186/s13068-019-1518-4. eCollection 2019.
, an oleaginous yeast, is a promising platform strain for production of biofuels and oleochemicals as it can accumulate a high level of lipids in response to nitrogen limitation. Accordingly, many metabolic engineering efforts have been made to develop engineered strains of with higher lipid yields. Genome-scale model of metabolism (GEM) is a powerful tool for identifying novel genetic designs for metabolic engineering. Several GEMs for have recently been developed; however, not many applications of the GEMs have been reported for actual metabolic engineering of . The major obstacle impeding the application of GEMs is the lack of proper methods for predicting phenotypes of the cells in the nitrogen-limited condition, or more specifically in the stationary phase of a batch culture.
In this study, we showed that environmental version of minimization of metabolic adjustment (eMOMA) can be used for predicting metabolic flux distribution of under the nitrogen-limited condition and identifying metabolic engineering strategies to improve lipid production in . Several well-characterized overexpression targets, such as diglyceride acyltransferase, acetyl-CoA carboxylase, and stearoyl-CoA desaturase, were successfully rediscovered by our eMOMA-based design method, showing the relevance of prediction results. Interestingly, the eMOMA-based design method also suggested non-intuitive knockout targets, and we experimentally validated the prediction with a mutant lacking YALI0F30745g, one of the predicted targets involved in one-carbon/methionine metabolism. The mutant accumulated 45% more lipids compared to the wild-type.
This study demonstrated that eMOMA is a powerful computational method for understanding and engineering the metabolism of and potentially other oleaginous microorganisms.
油脂酵母是一种有前景的平台菌株，可用于生产生物燃料和油脂化学品，因为它能在氮限制条件下积累高水平的脂质。因此，人们进行了许多代谢工程方面的努力来开发脂质产量更高的工程化油脂酵母菌株。基因组规模代谢模型（GEM）是用于识别代谢工程新基因设计的强大工具。最近已开发出几种油脂酵母的GEM；然而，关于GEM在油脂酵母实际代谢工程中的应用报道并不多。阻碍油脂酵母GEM应用的主要障碍是缺乏预测氮限制条件下，更具体地说是分批培养稳定期细胞表型的合适方法。
在本研究中，我们表明环境版最小化代谢调节（eMOMA）可用于预测氮限制条件下油脂酵母的代谢通量分布，并识别提高油脂酵母脂质产量的代谢工程策略。通过我们基于eMOMA的设计方法成功重新发现了几个特征明确的过表达靶点，如二甘油酯酰基转移酶、乙酰辅酶A羧化酶和硬脂酰辅酶A去饱和酶，这表明了预测结果的相关性。有趣的是，基于eMOMA的设计方法还提出了非直观的敲除靶点，并且我们通过缺乏YALI0F30745g（预测参与一碳/甲硫氨酸代谢的靶点之一）的突变体对预测进行了实验验证。该突变体积累的脂质比野生型多45%。
本研究表明eMOMA是一种强大的计算方法，可用于理解和改造油脂酵母以及潜在的其他产油微生物的代谢。
