• Yiman Wu, Femke Streijger, Yining Wang, Guohui Lin, Sean Christie, Jean-Marc Mac-Thiong, Stefan Parent, Christopher S Bailey, Scott Paquette, Michael C Boyd, Tamir Ailon, John Street, Charles G Fisher, Marcel F Dvorak, Brian K Kwon, and Liang Li.
• Department of Chemistry, University of Alberta, Edmonton, AB, T6G2G2, Canada.
• Sci Rep. 2016 Dec 14; 6: 38718.

AbstractSuffering an acute spinal cord injury (SCI) can result in catastrophic physical and emotional loss. Efforts to translate novel therapies in acute clinical trials are impeded by the SCI community's singular dependence upon functional outcome measures. Therefore, a compelling rationale exists to establish neurochemical biomarkers for the objective classification of injury severity. In this study, CSF and serum samples were obtained at 3 time points (~24, 48, and 72 hours post-injury) from 30 acute SCI patients (10 AIS A, 12 AIS B, and 8 AIS C). A differential chemical isotope labeling liquid chromatography mass spectrometry (CIL LC-MS) with a universal metabolome standard (UMS) was applied to the metabolomic profiling of these samples. This method provided enhanced detection of the amine- and phenol-containing submetabolome. Metabolic pathway analysis revealed dysregulations in arginine-proline metabolism following SCI. Six CSF metabolites were identified as potential biomarkers of baseline injury severity, and good classification performance (AUC > 0.869) was achieved by using combinations of these metabolites in pair-wise comparisons of AIS A, B and C patients. Using the UMS strategy, the current data set can be expanded to a larger cohort for biomarker validation, as well as discovering biomarkers for predicting neurologic outcome.

11 keywords...

hide…