Utilizing genome-scale models to optimize nutrient supply for sustained algal growth and lipid productivity
Utilizing genome-scale models to optimize nutrient supply for sustained algal growth and lipid productivity
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DOI：
10.1038/s41540-019-0110-7
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发表时间：
2019-09-24
影响因子：
4
通讯作者：
Betenbaugh, Michael J.
中科院分区：
文献类型：
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作者：
Li, Chien-Ting;Yelsky, Jacob;Betenbaugh, Michael J.
营养物质的可利用性对于藻类和用于产生有价值的生化产品的其他微生物的生长至关重要。确定培养物的营养供应的最佳水平可以消除过量营养的进料，降低生产成本并减少对环境的营养污染。随着组学和生物信息学方法的出现，现在可以构建精确描述微生物代谢的基因组规模模型。在这项研究中，一个基因组规模的绿色小球藻（iCZ 946）的模型被应用到预测多种营养物质，包括硝酸盐和葡萄糖，在自养和异养条件下的喂养。目标函数从优化生长改为最小化硝酸盐和葡萄糖吸收速率，从而能够预测这些营养素的进料速率。代谢模型控制（MMC）算法被验证为自养生长，节省18%的硝酸盐，同时维持藻类生长。此外，我们获得了类似的生长曲线，同时控制葡萄糖和硝酸盐的供应在异养条件下的高和低水平的葡萄糖和硝酸盐。最后，控制硝酸盐的供应，以保持蛋白质和叶绿素的合成，虽然在较低的速率，在氮限制条件下。与氮饥饿相比，这种模型驱动的培养策略使生物质的总体积产量增加了一倍，脂肪酸甲酯（FAME）产量增加了61%，并且叶黄素产量增加了近3倍。本研究介绍了一种控制方法，它集成了组学数据和基因组规模的模型，以优化营养供应的基础上，在不同的营养环境中的藻类细胞的代谢状态。这种方法可以将生物加工控制转化为适合于各种物种的基于系统生物学的范例，以限制营养素投入，降低加工成本，并优化下一代理想生物技术产品的生物制造。
Nutrient availability is critical for growth of algae and other microbes used for generating valuable biochemical products. Determining the optimal levels of nutrient supplies to cultures can eliminate feeding of excess nutrients, lowering production costs and reducing nutrient pollution into the environment. With the advent of omics and bioinformatics methods, it is now possible to construct genome-scale models that accurately describe the metabolism of microorganisms. In this study, a genome-scale model of the green alga Chlorella vulgaris (iCZ946) was applied to predict feeding of multiple nutrients, including nitrate and glucose, under both autotrophic and heterotrophic conditions. The objective function was changed from optimizing growth to instead minimizing nitrate and glucose uptake rates, enabling predictions of feed rates for these nutrients. The metabolic model control (MMC) algorithm was validated for autotrophic growth, saving 18% nitrate while sustaining algal growth. Additionally, we obtained similar growth profiles by simultaneously controlling glucose and nitrate supplies under heterotrophic conditions for both high and low levels of glucose and nitrate. Finally, the nitrate supply was controlled in order to retain protein and chlorophyll synthesis, albeit at a lower rate, under nitrogen-limiting conditions. This model-driven cultivation strategy doubled the total volumetric yield of biomass, increased fatty acid methyl ester (FAME) yield by 61%, and enhanced lutein yield nearly 3 fold compared to nitrogen starvation. This study introduces a control methodology that integrates omics data and genome-scale models in order to optimize nutrient supplies based on the metabolic state of algal cells in different nutrient environments. This approach could transform bioprocessing control into a systems biology-based paradigm suitable for a wide range of species in order to limit nutrient inputs, reduce processing costs, and optimize biomanufacturing for the next generation of desirable biotechnology products.