Center for Systems Biology, University of Iceland, Reykjavik, Iceland.
Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.
NPJ Syst Biol Appl. 2021 Sep 17;7(1):36. doi: 10.1038/s41540-021-00195-5.
Epithelial-to-mesenchymal transition (EMT) is fundamental to both normal tissue development and cancer progression. We hypothesized that EMT plasticity defines a range of metabolic phenotypes and that individual breast epithelial metabolic phenotypes are likely to fall within this phenotypic landscape. To determine EMT metabolic phenotypes, the metabolism of EMT was described within genome-scale metabolic models (GSMMs) using either transcriptomic or proteomic data from the breast epithelial EMT cell culture model D492. The ability of the different data types to describe breast epithelial metabolism was assessed using constraint-based modeling which was subsequently verified using C isotope tracer analysis. The application of proteomic data to GSMMs provided relatively higher accuracy in flux predictions compared to the transcriptomic data. Furthermore, the proteomic GSMMs predicted altered cholesterol metabolism and increased dependency on argininosuccinate lyase (ASL) following EMT which were confirmed in vitro using drug assays and siRNA knockdown experiments. The successful verification of the proteomic GSMMs afforded iBreast2886, a breast GSMM that encompasses the metabolic plasticity of EMT as defined by the D492 EMT cell culture model. Analysis of breast tumor proteomic data using iBreast2886 identified vulnerabilities within arginine metabolism that allowed prognostic discrimination of breast cancer patients on a subtype-specific level. Taken together, we demonstrate that the metabolic reconstruction iBreast2886 formalizes the metabolism of breast epithelial cell development and can be utilized as a tool for the functional interpretation of high throughput clinical data.
上皮-间充质转化（EMT）对于正常组织发育和癌症进展都至关重要。我们假设 EMT 可塑性定义了一系列代谢表型，并且个体乳腺上皮代谢表型可能在这个表型景观内。为了确定 EMT 的代谢表型，我们使用来自乳腺上皮 EMT 细胞培养模型 D492 的转录组或蛋白质组数据，在基因组规模代谢模型（GSMM）中描述 EMT 的代谢。使用基于约束的建模评估了不同数据类型描述乳腺上皮代谢的能力，随后使用 C 同位素示踪剂分析进行了验证。与转录组数据相比，蛋白质组数据在通量预测方面提供了相对更高的准确性，应用于 GSMMs。此外，蛋白质组 GSMMs 预测 EMT 后胆固醇代谢改变和精氨酸琥珀酸裂解酶（ASL）依赖性增加，这在体外通过药物测定和 siRNA 敲低实验得到证实。蛋白质组 GSMM 的成功验证提供了 iBreast2886，这是一种乳腺 GSMM，涵盖了 D492 EMT 细胞培养模型定义的 EMT 的代谢可塑性。使用 iBreast2886 分析乳腺肿瘤蛋白质组数据，确定了精氨酸代谢中的脆弱性，从而能够在亚群特异性水平上对乳腺癌患者进行预后区分。总之，我们证明了代谢重建 iBreast2886 形式化了乳腺上皮细胞发育的代谢，可以作为功能解释高通量临床数据的工具。
