Glioblastoma (GBM) is one of the most aggressive cancers of the central nervous system. Despite current advances in non-invasive imaging and the advent of novel therapeutic modalities, patient survival remains very low. There is a critical need for the development of effective biomarkers for GBM diagnosis and therapeutic monitoring. Extracellular vesicles (EVs) produced by GBM tumors have been shown to play an important role in cellular communication and modulation of the tumor microenvironment. As GBM-derived EVs contain specific “molecular signatures” of their parental cells and are able to transmigrate across the blood–brain barrier into biofluids such as the blood and cerebrospinal fluid (CSF), they are considered as a valuable source of potential diagnostic biomarkers. Given the relatively harsh extracellular environment of blood and CSF, EVs have to endure and adapt to different conditions. The ability of EVs to adjust and function depends on their lipid bilayer, metabolic content and enzymes and transport proteins. The knowledge of EVs metabolic characteristics and adaptability is essential for their utilization as diagnostic and therapeutic tools. The main aim of this study was to determine the metabolome of small EVs or exosomes derived from different GBM cells and compare to the metabolic profile of their parental cells using NMR spectroscopy. In addition, a possible flux of metabolic processes in GBM-derived EVs was simulated using constraint-based modeling from published proteomics information. Our results showed a clear difference between the metabolic profiles of GBM cells, EVs and media. Machine learning analysis of EV metabolomics, as well as flux simulation, supports the notion of active metabolism within EVs, including enzymatic reactions and the transfer of metabolites through the EV membrane. These results are discussed in the context of novel GBM diagnostics and therapeutic monitoring.

中文翻译：

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胶质母细胞瘤细胞系来源的细胞外囊泡代谢组的分析和模拟

胶质母细胞瘤（GBM）是中枢神经系统最具侵袭性的癌症之一。尽管目前非侵入性成像技术取得了进步，并且出现了新型治疗方式，但患者的生存率仍然很低。迫切需要开发用于 GBM 诊断和治疗监测的有效生物标志物。GBM 肿瘤产生的细胞外囊泡（EV）已被证明在细胞通讯和肿瘤微环境调节中发挥重要作用。由于 GBM 衍生的 EV 含有其亲代细胞的特定“分子特征”，并且能够穿过血脑屏障进入血液和脑脊液 (CSF) 等生物流体中，因此它们被认为是潜在诊断生物标志物的宝贵来源。鉴于血液和脑脊液等相对恶劣的细胞外环境，EV必须忍受并适应不同的条件。EV 的调节和功能能力取决于其脂质双层、代谢含量以及酶和转运蛋白。了解 EV 的代谢特征和适应性对于将其用作诊断和治疗工具至关重要。本研究的主要目的是确定源自不同 GBM 细胞的小 EV 或外泌体的代谢组，并使用 NMR 光谱与其亲代细胞的代谢谱进行比较。此外，使用来自已发表的蛋白质组学信息的基于约束的模型模拟了 GBM 衍生的 EV 中可能的代谢过程通量。我们的结果显示 GBM 细胞、EV 和培养基的代谢特征之间存在明显差异。EV 代谢组学的机器学习分析以及通量模拟支持 EV 内主动代谢的概念，包括酶反应和代谢物通过 EV 膜的转移。这些结果将在新型 GBM 诊断和治疗监测的背景下进行讨论。