Parallel Metabolomic Profiling of Cerebrospinal Fluid, Plasma, and Spinal Cord to Identify Biomarkers for Spinal Cord Injury

Abstract

Loss of physical and emotional health due to spinal cord injury (SCI) has been rapidly increasing worldwide. Effective evaluation of the severity of SCI is crucial to its prognosis. Herein, we constructed rat models of SCI with four different degrees of injury (sham group, light injury group, moderate injury group, and heavy injury group), using the surgical approach. Cerebrospinal fluid (CSF), plasma, and spinal cord were sampled at the sub-acute spinal cord (72 h post-injury) from each rat. The LC–MS-based metabolic profiling of these samples was performed according to a universal metabolome standard (UMS). The results demonstrated that 130, 104, and 128 metabolites were significantly altered within the CSF, plasma, and spinal cord samples, respectively. Among them, there were four differential metabolites, including uric acid, phosphorycholine, pyridoxine, and guanidoacetic acid, which were commonly identified within the CSF, plasma, and spinal cord samples. Further pathway analysis of these differential metabolites demonstrated a disturbance in the metabolism of glyoxylate and dicarboxylate and glycine, serine, and threonine which were associated with pathophysiologic consequence of spinal cord injury. In particular, phosphorycholine, pyridoxine, and guanidoacetic acid demonstrated a relationship with SCI severity. Thus, they could be utilized as potential metabolite biomarkers for SCI severity assessment.

UPLC/Q-TOF MS-Based Urine Metabonomics Study to Identify Diffuse Axonal Injury Biomarkers in Rat

“…Urine could be rapidly and easily obtained by the patients in a noninvasive manner. Moreover, the metabolite components and concentrations in urine are good indicators of metabolic fluctuations [8][9][10][11][12]. Thus, urinary metabolomic markers could collaborate to establish a more efficacious, cheap, and safe screening method for diagnosis of many diseases.…”

“…Thus, urine is an optimal specimen to develop biomarkers to diagnose and monitor DAI survival noninvasively. Metabolomics, which focuses on providing an unbiased view of changes in endogenous metabolites, has been successfully applied for identifying diagnostic biomarkers of many diseases [9][10][11][12]. At present, one of the most powerful analytical technologies for nontargeted metabonomic mapping is ultraperformance liquid chromatography quadrupole-time-of-flight hybrid mass spectrometry (UPLC/Q-TOF MS) technology, which could accurately quantify and discover the remarkably altered metabolites in biofluids or tissues.…”

Integrated transcriptomic and metabolomic profiling reveals dysregulation of purine metabolism during the acute phase of spinal cord injury in rats

“…Normally, creatine can form phosphocreatine under the action of creatine kinase and then participate in the production of adenosine triphosphate (ATP) for energy supply ( Kazak and Cohen, 2020 ). A recent metabolomic study of the injured spinal cord in rats showed increased levels of guanidoacetic acid in the acute phase of SCI ( Yang et al, 2022 ). In the present study, we found that guanidoacetic acid expression was also elevated, and arginine and phosphocreatine expression were decreased in the acute phase of SCI, suggesting that creatine production from guanidoacetic acid was blocked and ATP production was reduced.…”

“…Currently, metabolomics is increasingly used as an analytical tool for early diagnosis, prognostic assessment, and monitoring of treatment response in cancer and metabolic diseases ( Bansal et al, 2022 ; di Meo et al, 2022 ). On the same note, there is a growing interest in the role of metabolomics in SCI, especially in studies related to specific biomarkers for the assessment of SCI severity and prognosis ( Jiang et al, 2010 ; Peng et al, 2014 ; Wu et al, 2016 ; Yang et al, 2022 ). Metabolomics also facilitates insight into the pathophysiological processes of disease and the alteration of metabolic pathways in vivo to study the overall stress of the organism in response to disease or environmental stimuli ( Mathew et al, 2019 ; Gao et al, 2022 ).…”

A swift expanding trend of extracellular vesicles in spinal cord injury research: a bibliometric analysis

“…The main terms comprising cluster 1 are "axon" [52,53], "day" [54,55], "immunoreactivity" [56,57] and "glial cell" [58,59]. Meanwhile, cluster 2 is characterized by the core keywords "vesicle" [60][61][62], "source" [63,64], "miRNAs" [65,66] and "biomarker" [67][68][69]. Lastly, cluster 3 is distinguished by "bone marrow mesenchymal stem cells" [70][71][72], "lipopolysaccharide" [73,74], "angiogenesis" [75][76][77], "apoptosis" [78,79], "polarization" [80,81], "migration" [82,83], "hydrogel" [84,85], "cell communication" [86,87], and "mechanism" [88,89] as its core keywords.…”