Analysis and Simulation of Glioblastoma Cell Lines-Derived Extracellular Vesicles Metabolome
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 dia...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Metabolites |
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| Online Access: | https://www.mdpi.com/2218-1989/10/3/88 |
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| author | Miroslava Čuperlović-Culf Nam H. Khieu Anuradha Surendra Melissa Hewitt Claudie Charlebois Jagdeep K. Sandhu |
| author_facet | Miroslava Čuperlović-Culf Nam H. Khieu Anuradha Surendra Melissa Hewitt Claudie Charlebois Jagdeep K. Sandhu |
| author_sort | Miroslava Čuperlović-Culf |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T17:00:42Z |
| format | Article |
| id | doaj.art-8c57e8fbf70f4cf390876cb35c0cba45 |
| institution | Directory Open Access Journal |
| issn | 2218-1989 |
| language | English |
| last_indexed | 2024-12-19T17:00:42Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metabolites |
| spelling | doaj.art-8c57e8fbf70f4cf390876cb35c0cba452022-12-21T20:13:18ZengMDPI AGMetabolites2218-19892020-03-011038810.3390/metabo10030088metabo10030088Analysis and Simulation of Glioblastoma Cell Lines-Derived Extracellular Vesicles MetabolomeMiroslava Čuperlović-Culf0Nam H. Khieu1Anuradha Surendra2Melissa Hewitt3Claudie Charlebois4Jagdeep K. Sandhu5Digital Technologies Research Centre, Bldg-M50, 1200 Montreal Road, National Research Council Canada, Ottawa, ON K1A0R6, CanadaHuman Health Therapeutics Research Centre, Bldg-M54, 1200 Montreal Road, National Research Council Canada, Ottawa, ON K1A0R6, CanadaDigital Technologies Research Centre, Bldg-M50, 1200 Montreal Road, National Research Council Canada, Ottawa, ON K1A0R6, CanadaHuman Health Therapeutics Research Centre, Bldg-M54, 1200 Montreal Road, National Research Council Canada, Ottawa, ON K1A0R6, CanadaHuman Health Therapeutics Research Centre, Bldg-M54, 1200 Montreal Road, National Research Council Canada, Ottawa, ON K1A0R6, CanadaHuman Health Therapeutics Research Centre, Bldg-M54, 1200 Montreal Road, National Research Council Canada, Ottawa, ON K1A0R6, CanadaGlioblastoma (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.https://www.mdpi.com/2218-1989/10/3/88extracellular vesiclesmetabolomicsmetabolism modelingmachine learningglioblastoma |
| spellingShingle | Miroslava Čuperlović-Culf Nam H. Khieu Anuradha Surendra Melissa Hewitt Claudie Charlebois Jagdeep K. Sandhu Analysis and Simulation of Glioblastoma Cell Lines-Derived Extracellular Vesicles Metabolome Metabolites extracellular vesicles metabolomics metabolism modeling machine learning glioblastoma |
| title | Analysis and Simulation of Glioblastoma Cell Lines-Derived Extracellular Vesicles Metabolome |
| title_full | Analysis and Simulation of Glioblastoma Cell Lines-Derived Extracellular Vesicles Metabolome |
| title_fullStr | Analysis and Simulation of Glioblastoma Cell Lines-Derived Extracellular Vesicles Metabolome |
| title_full_unstemmed | Analysis and Simulation of Glioblastoma Cell Lines-Derived Extracellular Vesicles Metabolome |
| title_short | Analysis and Simulation of Glioblastoma Cell Lines-Derived Extracellular Vesicles Metabolome |
| title_sort | analysis and simulation of glioblastoma cell lines derived extracellular vesicles metabolome |
| topic | extracellular vesicles metabolomics metabolism modeling machine learning glioblastoma |
| url | https://www.mdpi.com/2218-1989/10/3/88 |
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