Enis Cela, David Tweddell, Eric K Patterson, Mark Daley, Gediminas Cepinskas, Douglas D Fraser
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引用次数: 0
Abstract
Introduction: Altered metabolic pathways are critical in the progression of traumatic brain injury (TBI). Identifying differentially abundant metabolites (DAMs) from specific cell types can offer valuable diagnostic and prognostic insights.
Objective: This study aimed to characterize the metabolomic profile of injured human brain microvascular endothelial cells (hBMEC) at 2-, 12-, 24-, and 48 h post-injury.
Methods: Using an in vitro TBI model, we analyzed metabolites in cell culture media through a combination of direct injection mass spectrometry and a custom reverse-phase LC-MS/MS assay. We evaluated 644 metabolites at each time point.
Results: Phosphatidylcholines were significantly upregulated across all time intervals. At 2- and 12 h post-injury, the most significantly upregulated metabolites included sphingomyelin (OH) C22:1, ethylmalonic acid, and methylhistidine, while guanosine and the combination of butyric acid + isobutyric acid were the most downregulated. At 24 and 48 h, deoxyadenosine and inosine, respectively, emerged as the most upregulated metabolites, with butyric acid + isobutyric acid and quinoline-4-carboxylic acid showing the greatest downregulation.
Conclusion: Metabolomic profiling identified various DAMs after traumatic injury that are linked to human endothelial dysfunction. Future experiments should expand the number of metabolites measured to determine the underlying signaling pathways.
期刊介绍:
Metabolomics publishes current research regarding the development of technology platforms for metabolomics. This includes, but is not limited to:
metabolomic applications within man, including pre-clinical and clinical
pharmacometabolomics for precision medicine
metabolic profiling and fingerprinting
metabolite target analysis
metabolomic applications within animals, plants and microbes
transcriptomics and proteomics in systems biology
Metabolomics is an indispensable platform for researchers using new post-genomics approaches, to discover networks and interactions between metabolites, pharmaceuticals, SNPs, proteins and more. Its articles go beyond the genome and metabolome, by including original clinical study material together with big data from new emerging technologies.