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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.
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