Division of Science and Math, New York University Abu Dhabi , Abu Dhabi , UAE ; Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi Institute , Abu Dhabi , UAE.
Division of Science and Math, New York University Abu Dhabi , Abu Dhabi , UAE ; Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi Institute , Abu Dhabi , UAE ; Engineering Division, Biofinery , Manhattan, KS , USA.
Front Bioeng Biotechnol. 2014 Dec 10;2:68. doi: 10.3389/fbioe.2014.00068. eCollection 2014.
Metabolic modeling provides the means to define metabolic processes at a systems level; however, genome-scale metabolic models often remain incomplete in their description of metabolic networks and may include reactions that are experimentally unverified. This shortcoming is exacerbated in reconstructed models of newly isolated algal species, as there may be little to no biochemical evidence available for the metabolism of such isolates. The phenotype microarray (PM) technology (Biolog, Hayward, CA, USA) provides an efficient, high-throughput method to functionally define cellular metabolic activities in response to a large array of entry metabolites. The platform can experimentally verify many of the unverified reactions in a network model as well as identify missing or new reactions in the reconstructed metabolic model. The PM technology has been used for metabolic phenotyping of non-photosynthetic bacteria and fungi, but it has not been reported for the phenotyping of microalgae. Here, we introduce the use of PM assays in a systematic way to the study of microalgae, applying it specifically to the green microalgal model species Chlamydomonas reinhardtii. The results obtained in this study validate a number of existing annotated metabolic reactions and identify a number of novel and unexpected metabolites. The obtained information was used to expand and refine the existing COBRA-based C. reinhardtii metabolic network model iRC1080. Over 254 reactions were added to the network, and the effects of these additions on flux distribution within the network are described. The novel reactions include the support of metabolism by a number of d-amino acids, l-dipeptides, and l-tripeptides as nitrogen sources, as well as support of cellular respiration by cysteamine-S-phosphate as a phosphorus source. The protocol developed here can be used as a foundation to functionally profile other microalgae such as known microalgae mutants and novel isolates.
代谢建模为在系统水平上定义代谢过程提供了手段；然而，基因组规模的代谢模型在其对代谢网络的描述中常常不完整，并且可能包括实验上未经证实的反应。在新分离的藻类物种的重建模型中，这种缺点更为严重，因为对于这些分离物的代谢，可能几乎没有或没有生化证据。表型微阵列 (PM) 技术（Biolog，Hayward，CA，USA）提供了一种高效、高通量的方法，可根据大量进入代谢物功能定义细胞代谢活性。该平台可以实验验证网络模型中许多未经证实的反应，并识别重建代谢模型中缺失或新的反应。PM 技术已被用于非光合细菌和真菌的代谢表型研究，但尚未报道用于微藻的表型研究。在这里，我们以系统的方式将 PM 测定法引入到微藻的研究中，特别是将其应用于绿藻模式物种莱茵衣藻。本研究获得的结果验证了许多现有的注释代谢反应，并确定了一些新的和意外的代谢物。获得的信息用于扩展和细化现有的基于 COBRA 的 C. reinhardtii 代谢网络模型 iRC1080。网络中添加了超过 254 个反应，并描述了这些添加对网络内通量分布的影响。新的反应包括多种 d-氨基酸、l-二肽和 l-三肽作为氮源以及半胱氨酸 S-磷酸作为磷源支持细胞呼吸。这里开发的方案可用于对其他微藻（如已知的微藻突变体和新型分离物）进行功能分析。
