Reference : The Mouse Brain Metabolome: Region-Specific Signatures and Response to Excitotoxic Ne...
Scientific journals : Article
Life sciences : Biotechnology
Human health sciences : Neurology
http://hdl.handle.net/10993/20985
The Mouse Brain Metabolome: Region-Specific Signatures and Response to Excitotoxic Neuronal Injury
English
Jäger, Christian* mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Glaab, Enrico* mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Michelucci, Alessandro* mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Binz, Tina []
Köglsberger, Sandra mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Garcia, Pierre mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Trezzi, Jean-Pierre mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Ghelfi, Jenny mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Balling, Rudi mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Buttini, Manuel mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
* These authors have contributed equally to this work.
2015
American Journal of Pathology
American Society for Investigative Pathology
185
6
1699-1712
Yes (verified by ORBilu)
International
0002-9440
1525-2191
Bethesda
MD
[en] mouse ; brain ; metabolomics ; bioinformatics ; biomarker ; neurodegeneration ; statistical ; machine learning ; disease
[en] Neurodegeneration is a multistep process characterized by a multitude of molecular entities and their interactions. Systems' analyses, or omics approaches, have become an important tool in characterizing this process. Although RNA and protein profiling made their entry into this field a couple of decades ago, metabolite profiling is a more recent addition. The metabolome represents a large part or all metabolites in a tissue, and gives a snapshot of its physiology. By using gas chromatography coupled to mass spectrometry, we analyzed the metabolic profile of brain regions of the mouse, and found that each region is characterized by its own metabolic signature. We then analyzed the metabolic profile of the mouse brain after excitotoxic injury, a mechanism of neurodegeneration implicated in numerous neurological diseases. More important, we validated our findings by measuring, histologically and molecularly, actual neurodegeneration and glial response. We found that a specific global metabolic signature, best revealed by machine learning algorithms, rather than individual metabolites, was the most robust correlate of neuronal injury and the accompanying gliosis, and this signature could serve as a global biomarker for neurodegeneration. We also observed that brain lesioning induced several metabolites with neuroprotective properties. Our results deepen the understanding of metabolic changes accompanying neurodegeneration in disease models, and could help rapidly evaluate these changes in preclinical drug studies.
Luxembourg Centre for Systems Biomedicine (LCSB): Experimental Neurobiology (Balling Group) ; Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group) ; Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) ; Luxembourg Centre for Systems Biomedicine (LCSB): Metabolomics (Hiller Group)
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/20985
10.1016/j.ajpath.2015.02.016
http://ajp.amjpathol.org/article/S0002-9440(15)00150-9/abstract
The original publication is available at http://ajp.amjpathol.org/article/S0002-9440(15)00150-9/abstract

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