Untargeted metabonomic analysis of a cerebral stroke model in rats: a study based on UPLC–MS/MS - Huang, Dunbing; Yang, Yihan; Song, Wei; Jiang, Cai; Zhang, Yuhao; Zhang, Anren; Lin, Zhonghua; Ke, Xiaohua (Frontiers in Neuroscience, 2023)
Brain tissue damage caused by ischemic stroke can trigger changes in the body's metabolic response, and understanding the changes in the metabolic response of the gut after stroke can contribute to research on poststroke brain function recovery. Despite the increase in international research on poststroke metabolic mechanisms and availability of powerful research tools in recent years, there is still an urgent need for poststroke metabolic studies. Metabolomic examination of feces from a cerebral ischemia-reperfusion rat model can provide new insights into poststroke metabolism and identify key metabolic pathways, which will help reveal diagnostic and therapeutic targets as well as inspire pathophysiological studies after stroke. We randomly divided 16 healthy adult pathogen-free male Sprague-Dawley (SD) rats into the normal and the study group after middle cerebral artery occlusion/reperfusion(MCAO/R). After 1 and 14 days of modelling, compared to the study group, rats in the study group showed significant neurological deficits (P<0.001) and significant increased infarct volume (day 1: P<0.001; day 14: P=0.001). Ultra-performance liquid chromatography-tandem mass spectrometry (UPLCMS/MS) was used to determine the identities and concentrations of metabolites across all groups, and filtered highquality data were analyzed for overall analysis, differential screening and differential metabolite functional analysis. Mass spectra identified 1044 and 635 differential metabolites in rat feces in positive and negative ion modes, respectively, which differed significantly between the normal and study groups. The upregulated metabolites identified in the study group involved tryptophan metabolism (P=0.036678, P<0.05), arachidonic acid metabolism (P=0.15695), cysteine and methionine metabolism (P=0.24705), and pyrimidine metabolism (P=0.3413), whereas the downregulated metabolites involved arginine and proline metabolism (P=0.15695) and starch and sucrose metabolism (P=0.52256). Finally, we determined that UPLC-MS/MS could be employed for nontargeted metabolomics research. Moreover, tryptophan metabolic pathways may be disordered in the study group. Alterations in the tryptophan metabolome may provide additional theoretical and data support for elucidating stroke pathogenesis and selecting intervention pathways.